Cardiac CT, Coronary CT Angiography, Calcium Scoring and CT Fractional Flow Reserve

Number: 0228

Table Of Contents

Policy
Applicable CPT / HCPCS / ICD-10 Codes
Background
References

Policy

Scope of Policy

This Clinical Policy Bulletin addresses cardiac CT, coronary CT angiography, calcium scoring and CT fractional flow reserve.

  1. Medical Necessity

    1. Cardiac Computed Tomography (CT) Angiography of the Coronary Arteries

      1. Aetna considers cardiac computed tomography (CT) angiography of the coronary arteries using 64-slice or greater medically necessary for the following indications:

        1. Rule out obstructive coronary stenosis in symptomatic persons with an low or intermediate pre-test probability of coronary artery disease or atherosclerotic cardiovascular disease by Framingham risk scoring, Pooled Cohort Equations, or by American College of Cardiology (ACC) criteria (see Appendix);
        2. Rule out obstructive coronary stenosis in persons with a low or intermediate pre-test probability of coronary artery disease or atherosclerotic cardiovascular disease by Framingham risk scoring, Pooled Cohort Equations, or by American College of Cardiology (ACC) criteria (see Appendix) with a positive (i.e., greater than or equal to 1 mm ST segment depression) stress test;
        3. Evaluation of asymptomatic persons at an intermediate pre-test probability of coronary heart disease or atherosclerotic cardiovascular disease by Framingham risk scoring or Pooled Cohort Equations (see Appendix) who have an equivocal or uninterpretable exercise or pharmacological stress test or have resting electrocardiogram (ECG) changes (such as left bundle branch block (LBBB), pathologic q-waves, or right bundle branch block (RBBB) with left anterior fascicular block (LAFB) in which coronary artery disease (CAD) is a possible etiology (Note: Current guidelines from the American Heart Association recommend against routine stress testing for screening asymptomatic adults);
        4. Pre-operative assessment of persons scheduled to undergo 'high-risk" non-cardiac surgery, where an imaging stress test or invasive coronary angiography is being deferred unless absolutely necessary; the ACC defines high-risk surgery as emergent operations, especially in the elderly, aortic and other major vascular surgeries, peripheral vascular surgeries, and anticipated prolonged surgical procedures with large fluid shifts and/or blood loss involving the abdomen and thorax;
        5. Pre-operative assessment for planned non-coronary cardiac surgeries including valvular heart disease, congenital heart disease, and pericardial disease, in lieu of cardiac catheterization as the initial imaging study, in persons with low or intermediate pretest risk of obstructive CAD;
        6. Detection and delineation of suspected coronary anomalies in young persons (less than 40 years of age) with suggestive symptoms (e.g., angina, syncope, arrhythmia, and exertional dyspnea without other known etiology of these symptoms in children and adults; dyspnea, tachypnea, wheezing, periods of pallor, irritability (episodic crying), diaphoresis, poor feeding and failure to thrive in infants);
        7. Evaluation of in-stent stenosis;
        8. Calculation of fractional flow reserve (HeartFlow FFRCT) for persons who have a coronary CTA that has shown coronary artery disease of uncertain functional significance, or is non-diagnostic;
        9. Evaluation of coronary ectasia.

      2. Aetna considers CT angiography of cardiac morphology for pulmonary vein mapping medically necessary for the following indications:

        1. Evaluation of persons needing biventricular pacemakers to accurately identify the coronary veins for lead placement;
        2. Evaluation of the pulmonary veins in persons undergoing pulmonary vein isolation procedures for atrial fibrillation (pre- and post-ablation procedure).
      3. Aetna considers CT angiography medically necessary for preoperative assessment of the aortic valve annulus prior to anticipated transcatheter aortic valve replacement (TAVR).
      4. Aetna considers cardiac computed tomography (CT) angiography medically necessary for evaluation of aortic erosion in symptomatic members (e.g., chest pain) who have been treated for atrial septal defect with an occlusive device.
      5. Aetna considers cardiac CT for evaluating cardiac structure and morphology medically necessary for the following indications:

        1. Anomalous pulmonary venous drainage;
        2. Evaluation of other complex congenital heart diseases;
        3. Evaluation of sinus venosum atrial-septal defect;
        4. Evaluation of suspected native or prosthetic cardiac valve dysfunction when echocardiographic imaging is inconclusive or there is suspicion for paravalvular abscess formation;
        5. Kawasaki's disease;
        6. Person scheduled or being evaluated for surgical repair of tetralogy of Fallot or other congenital heart diseases;
        7. Pulmonary outflow tract obstruction;
        8. Suspected or known Marfan's syndrome;
        9. Symptomatic post heart transplant recipients.

    2. Calcium Scoring

      1. Aetna considers a single calcium scoring by means of low-dose multi-slice CT angiography, ultrafast [electron-beam] CT, or spiral [helical] CT medically necessary for screening the following:

        1. Asymptomatic persons age 40 years and older with diabetes; or
        2. Asymptomatic persons with an intermediate (10 % to 20 %) 10-year risk of cardiac events based on Framingham Risk Scoring or Pooled Cohort Equations (see Appendix).

      2. Repeat calcium scoring is considered medically necessary only if the following criteria are met:

        1. Member’s most recent coronary artery calcium (CAC) scan result was zero;
        2. Member's most recent CAC scan was at least 5 years ago; and
        3. Discovery of coronary calcium would change management.
      3. Aetna considers calcium scoring by means of low-dose CT angiography medically necessary for persons who meet criteria for diagnostic cardiac CT angiography to assess whether an adequate image of the coronary arteries can be obtained.
      4. Aetna considers calcium scoring of the aortic valve medically necessary in the setting of persons with suspected paradoxical low-flow, low-gradient symptomatic severe aortic stenosis when transthoracic echocardiography is inconclusive.
      5. Aetna considers radiological computer-assisted prioritization / artificial intelligence (AI) software (e.g., Nanox.AI's HealthCCSng) not medically necessary to aid in the identification of cardiovascular disease during computed tomography (CT) scanning of the chest, as the software does not provide diagnostic information beyond triage and prioritization of radiological medical images, and should not be used in place of full member evaluation, or relied upon to make or confirm diagnosis.

  2. Experimental, Investigational, or Unproven

    Aetna considers the following experimental, investigational, or unproven because the effectiveness for indications other than the ones listed above has not been established:

    1. Cardiac CT angiography for persons with any of the following contraindications to the procedure:

      1. Body mass index (BMI) greater than 40 (except when 3rd generation Dual-Source CT (DSCT) 120-kv tube voltage is utilized);
      2. Inability to image at desired heart rate (under 80 beats/min), despite beta blocker administration;
      3. Persons in atrial fibrillation (except when rate-controlled and 3rd generation Dual-Source CT (DSCT) 120-kv tube voltage is utilized).or with other significant arrhythmia;
      4. Persons with extensive coronary calcification by plain film or with prior Agatston score greater than 1000;
    2. Cardiac CT angiography using less than 64-slice scanners;
    3. Cardiac CT angiography for prediction of adverse events in individuals with coronary artery bypass graft (CABG);
    4. Coronary CT angiography for screening of asymptomatic persons (including routine testing of asymptomatic heart transplant recipients), evaluation of atherosclerotic burden, evaluation of persons at high pre-test probability of coronary artery disease, identification of vulnerable plaques, monitoring of atheroma burden, and for all other indications (e.g., atrial angiosarcoma); Note: The selection of CT angiography should be made within the context of other testing modalities such as stress myocardial perfusion images or cardiac ultrasound results so that the resulting information facilitates the management decision and does not merely add a new layer of testing;
    5. Serial or repeat calcium scoring;
    6. Coronary CT angiography for assessment of coronary atherosclerosis in asymptomatic diabetics who do not otherwise meet the above criteria for CT coronary angiography because of insufficient evidence;
    7. Calcium scoring (e.g., with ultrafast [electron-beam] CT, spiral [helical] CT, and multi-slice CT) for all other indications because of insufficient evidence in the peer-reviewed published medical literature;
    8. The Cleerly Coronary Report (Cleerly Labs) for the evaluation of coronary artery disease (CAD) because of a lack of evidence that the use of the Cleerly Coronary Report would improve health outcomes of persons with CAD.
Table:

CPT Codes / HCPCS Codes / ICD-10 Codes
Code Code Description

Cardiac computed tomography (CT) angiography:

CPT codes covered if selection criteria are met:
75571 Computed tomography, heart, without contrast material, with quantitative evaluation of coronary calcium [not covered for serial or repeat calcium scoring]
75572 Computed tomography, heart, with contrast material, for evaluation of cardiac structure and morphology (including 3D image postprocessing, assessment of cardiac function, and evaluation of venous structure, if performed
75573 Computed tomography, heart, with contrast material, for evaluation of cardiac structure and morphology in the setting of congenital heart disease (including 3D image postprocessing, assessment of LV cardiac function, RV structure and function and evaluation of venous structures, if performed
75574 Computed tomographic angiography, heart, coronary arteries and bypass grafts (when present), with contrast material, including 3D image postprocessing (including evaluation of cardiac structure and morphology, assessment of cardiac function, and evaluation of venous structures, if performed
75580 Noninvasive estimate of coronary fractional flow reserve (FFR) derived from augmentative software analysis of the data set from a coronary computed tomography angiography, with interpretation and report by a physician or other qualified health care professional

Other CPT codes related to the CPB:
33250 - 33266 Cardiac tissue ablation procedures
33361 - 33369 Transcatheter aortic valve replacement with prosthetic valve (TAVR/TAVI)
93015 - 93024 Cardiovascular stress testing and ergonovine provocation test
93650 - 93657 Intracardiac catheter ablation procedures

ICD-10 codes covered if selection criteria is met (not all-inclusive):
E08.59 Diabetes mellitus due to underlying condition with other circulatory complications [coronary atherosclerosis in symptomatic diabetics]
E09.59 Drug or chemical induced diabetes mellitus with other circulatory complications [coronary atherosclerosis in symptomatic diabetics]
E10.59 Type 1 diabetes mellitus with other circulatory complications [coronary atherosclerosis in symptomatic diabetics]
E11.59 Type 2 diabetes mellitus with other circulatory complications [coronary atherosclerosis in symptomatic diabetics]
E13.59 Other specified diabetes mellitus with other circulatory complications [coronary atherosclerosis in symptomatic diabetics]
I06.0. I06.2 Rheumatic aortic stenosis and rheumatic aortic stenosis with insufficiency [in the setting of persons with suspected paradoxical low-flow, low-gradient symptomatic severe aortic stenosis when transthoracic echocardiography is inconclusive]
I08.0, I08.2, I08.3 Rheumatic disorders of both mitral and aortic valves, rheumatic disorders of both aortic and tricuspid valves, & combined rheumatic disorders of mitral, aortic and tricuspid valves [in the setting of persons with suspected paradoxical low-flow, low-gradient symptomatic severe aortic stenosis when transthoracic echocardiography is inconclusive]
I25.10 - I25.119 Atherosclerotic heart disease of native coronary artery
I25.41 Coronary artery aneurysm [prediction of adverse events in individuals with coronary artery bypass graft (CABG)]
I25.700 - I25.84 Atherosclerosis of coronary bypass graft(s) and coronary artery of transplanted heart, atherosclerosis of nonautologous biological coronary bypass graft(s), chronic total occlusion of coronary artery, coronary atherosclerosis due to lipid rich plaque, and coronary atherosclerosis due to calcified coronary lesions
I35.0, I35.2 Nonrheumatic aortic (valve) stenosis and nonrheumatic aortic (valve) stenosis with insufficiency [in the setting of persons with suspected paradoxical low-flow, low-gradient symptomatic severe aortic stenosis when transthoracic echocardiography is inconclusive]
I37.0 - I37.9 Nonrheumatic pulmonary valve disorders [pulmonary outflow obstruction]
I44.4 Left anterior fascicular block
I44.7 Left bundle-branch block, unspecified
I45.10 - I45.19 Other and unspecified right bundle-branch block
I48.0, I48.1, I48.2, I48.91 Paroxysmal atrial fibrillation, persistent atrial fibrillation, chronic atrial fibrillation, and unspecified atrial fibrillation info [when rate-controlled and 3rd generation Dual-Source CT (DSCT) 120-kv tube voltage is utilized]
M30.3 Mucocutaneous lymph node syndrome [Kawasaki disease]
Q21.1 Atria septal defect [aortic erosion in symptomatic members (e.g., chest pain) who have been treated for atrial septal defect with an occlusive device]
Q21.3 Tetrology of Fallot
Q23.0 Congenital stenosis of aortic valve [in the setting of persons with suspected paradoxical low-flow, low-gradient symptomatic severe aortic stenosis when transthoracic echocardiography is inconclusive]
Q26.0 - Q26.9 Congenital malformations of great veins
Q87.40 - Q87.43 Marfan syndrome
R07.1 - R07.9 Chest pain
R94.39 Abnormal result of other cardiovascular function study [covered for evaluation of asymptomatic persons at an intermediate pre-test probability of coronary heart disease by Framingham risk scoring (see Appendix) who have an equivocal or uninterpretable exercise or pharmacological stress test]
T82.01A - T82.9xxS Complications of cardiac and vascular prosthetic devices, implants and grafts [when echocardiographic imaging is inconclusive or there is suspicion for paravalvular abscess formation]
T82.855A - T82.855S Stenosis of coronary artery stent
T86.20 - T86.23 Complications of heart transplant
Z01.810 Encounter for preprocedural cardiovascular examination [pre-operative assessment for planned non- coronary cardiac surgeries]
Z68.41 - Z68.45 Body mass index (BMI) 40.0 or greater [when 3rd generation DSCT 120-kv tube voltage is utilized]

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):
C38.0 Malignant neoplasm of heart [atrial angiosarcoma]
Z48.21 Encounter for aftercare following heart transplant [routine screening]
Z94.1 Heart transplant status [routine screening]
Z95.1 Presence of aortocoronary bypass graft [with prediction of]

ICD-10 codes contraindicated for this CPB (not all-inclusive):
I46.2 - I46.9 Cardiac arrest
I47.0 - I47.9
I49.2 - I49.3		 Paroxysmal supraventricular tachycardia, paroxysmal ventricular tachycardia, paroxysmal tachycardia, unspecified
I48.1, I48.3 - I48.4, I48.92 Atrial flutter
Z68.41 - Z68.45 Body mass index 40 and over, adult
Z91.041 Radiographic dye allergy status [iodinated contrast material]

Calcium Scoring:

CPT codes not covered for indications listed in the CPB:
0721T Quantitative computed tomography (CT) tissue characterization, including interpretation and report, obtained without concurrent CT examination of any structure contained in previously acquired diagnostic imaging
0722T Quantitative computed tomography (CT) tissue characterization, including interpretation and report, obtained with concurrent CT examination of any structure contained in the concurrently acquired diagnostic imaging dataset (List separately in addition to code for primary procedure)

HCPCS codes covered for indications listed in the CPB:
S8092 Electron beam computed tomography (also known as ultrafast CT, cine CT)

ICD-10 codes covered if selection criteria is met (not all-inclusive):
E08.00 - E09.9 Diabetes mellitus due to underlying condition [asymptomatic persons age 40 years and older]
E10.10 - E13.9 Diabetes mellitus [asymptomatic persons age 40 years and older]
Z13.6 Encounter for screening for cardiovascular disorders

Cleerly Coronary Report :

CPT codes not covered for indications listed in the CPB:
0623T Automated quantification and characterization of coronary atherosclerotic plaque to assess severity of coronary disease, using data from coronary computed tomographic angiography; data preparation and transmission, computerized analysis of data, with review of computerized analysis output to reconcile discordant data, interpretation and report [Cleerly Coronary Report]
0624T Automated quantification and characterization of coronary atherosclerotic plaque to assess severity of coronary disease, using data from coronary computed tomographic angiography; data preparation and transmission [Cleerly Coronary Report]
0625T Automated quantification and characterization of coronary atherosclerotic plaque to assess severity of coronary disease, using data from coronary computed tomographic angiography; review of computerized analysis output to reconcile discordant data, interpretation and report [Cleerly Coronary Report]
0626T Intraprocedural coronary fractional flow reserve (FFR) with 3D functional mapping of color-coded FFR values for the coronary tree, derived from coronary angiogram data, for real-time review and interpretation of possible atherosclerotic stenosis(es) intervention (List separately in addition to code for primary procedure) [Cleerly Coronary Report]
0710T Noninvasive arterial plaque analysis using software processing of data from non-coronary computerized tomography angiography; including data preparation and transmission, quantification of the structure and composition of the vessel wall and assessment for lipid-rich necrotic core plaque to assess atherosclerotic plaque stability, data review, interpretation and report
0711T      data preparation and transmission
0712T      quantification of the structure and composition of the vessel wall and assessment for lipid-rich necrotic core plaque to assess atherosclerotic plaque stability
0713T      data review, interpretation and report

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):
I25.10 - I25.119 Atherosclerotic heart disease of native coronary artery
I25.700 – I25.709 Atherosclerotic of coronary artery bypass graft(s) and coronary artery of transplanted heart with angina pectoris

Background

Cardiac CT Angiography

Coronary computed tomography angiography (CCTA) is a noninvasive imaging modality designed to be an alternative to invasive cardiac angiography (cardiac catheterization) for diagnosing CAD by visualizing the blood flow in arterial and venous vessels. The gold standard for diagnosing coronary artery stenosis is cardiac catheterization.

Contrast-enhanced cardiac CT angiography (CTA)  involves the use of multi-slice CT and intravenously administered contrast material to obtain detailed images of the blood vessels of the heart. Beta-blockers and sublingual nitrates may be administered prior to the scan in order to lower the heart rate, avoid arrhythmia and dilate the coronary arteries. In order to allow for an improved image quality and contrast media dose reduction, the CCTA is usually ECG-triggered to adapt the scan sequence to the person's heartbeat (Bell et al, 2018).

In addition to being a non-invasive alternative to conventional invasive coronary angiography for evaluating coronary artery disease, CCTA has emerged as the gold-standard for the detection of coronary artery anomalies Ramjattan and Makaryus, 2018).

The performance of cardiac CTA has been improved by increasing the number of slices that can be acquired simultaneously by increasing the number of detector rows (AHTA, 2006).  As the number of slices that can be acquired simultaneously increases, the scan time is shortened, spatial resolution is increased, and reconstruction artifacts are significantly reduced.  Initial cardiac CT imaging was conducted with 4-slice detector CT.  Scanning times were reduced from 40 seconds down to 20 seconds with 16-slice detector CT. With the advent of 64-slice detector CT, scanning times were reduced to a 10 second breath-hold. Current generation scanners can perform full volumetric acquisition requiring only 1 cardiac cycle (1 R-R interval) and/or can be performed without breath holding (Abbara et al, 2016).

Cardiac CTA using 64-slices has been shown in studies to have a high negative predictive value (93 to 100 %), using conventional coronary angiography as the reference standard.  Given its high negative predictive value, cardiac CTA has been shown to be most useful for evaluating persons at low to intermediate risk of coronary artery disease.  This would include evaluation of asymptomatic low- to intermediate-risk persons with an equivocal exercise or pharmacologic stress test, and evaluation of low- to intermediate-risk persons with chest pain. Cardiac CTA is also a useful alternative to invasive coronary angiography for pre-operative evaluation of persons undergoing non-coronary cardiac surgery or high-risk non-cardiac surgery, where invasive coronary angiography would otherwise be indicated.

Einstein and colleagues (2007) ascertained the lifetime attributable risk (LAR) of cancer incidence associated with radiation exposure from a 64-slice computed tomography coronary angiography (CTCA) study and evaluated the influence of age, sex, and scan protocol on cancer risk.  Organ doses from 64-slice CTCA to standardized phantom (computational model) male and female patients were estimated using Monte Carlo simulation methods, using standard spiral CT protocols.  Age- and sex-specific LARs of individual cancers were estimated using the approach of BEIR VII and summed to obtain whole-body LARs.  Main outcome measures were whole-body and organ LARs of cancer incidence.  Organ doses ranged from 42 to 91 mSv for the lungs and 50 to 80 mSv for the female breast.  Lifetime cancer risk estimates for standard cardiac scans varied from 1 in 143 for a 20-year old woman to 1 in 3,261 for an 80-year old man.  Use of simulated electrocardiographically controlled tube current modulation (ECTCM) decreased these risk estimates to 1 in 219 and 1 in 5,017, respectively.  Estimated cancer risks using ECTCM for a 60-year old woman and a 60-year old man were 1 in 715 and 1 in 1911, respectively.  A combined scan of the heart and aorta had higher LARs, up to 1 in 114 for a 20-year old woman.  The highest organ LARs were for lung cancer and, in younger women, breast cancer.  The authors concluded that these estimates derived from simulation models suggested that use of 64-slice CTCA is associated with a non-negligible LAR of cancer.  This risk varies markedly and is considerably greater for women, younger patients, and for combined cardiac and aortic scans.

Arbab-Zadeh et al (2012) evaluated the impact of patient population characteristics on accuracy by CTA to detect obstructive CAD.  For the CORE-64 (Coronary Artery Evaluation Using 64-Row Multidetector Computed Tomography Angiography) study, a total of 371 patients underwent CTA and cardiac catheterization for the detection of obstructive CAD, defined as greater than or equal to 50 % luminal stenosis by quantitative coronary angiography (QCA).  This analysis includes 80 initially excluded patients with a calcium score greater than or equal to 600.  Area under the receiver-operating characteristic curve (AUC) was used to evaluate CTA diagnostic accuracy compared to QCA in patients according to calcium score and pre-test probability of CAD.  Analysis of patient-based quantitative CTA accuracy revealed an AUC of 0.93 (95 % CI: 0.90 to 0.95).  The AUC remained 0.93 (95 % CI: 0.90 to 0.96) after excluding patients with known CAD but decreased to 0.81 (95 % CI: 0.71 to 0.89) in patients with calcium score greater than or equal to 600 (p = 0.077).  While AUCs were similar (0.93, 0.92, and 0.93, respectively) for patients with intermediate, high pre-test probability for CAD, and known CAD, negative predictive values were different: 0.90, 0.83, and 0.50, respectively.  Negative predictive values decreased from 0.93 to 0.75 for patients with calcium score less than 100 or greater than or equal to 100, respectively (p = 0.053).  The authors concluded that both pre-test probability for CAD and coronary calcium scoring should be considered before using CTA for excluding obstructive CAD.  For that purpose, CTA is less effective in patients with calcium score greater than or equal to 600 and in patients with a high pre-test probability for obstructive CAD.  (CTA is most useful as a rule-out test in patients with low-intermediate pre-test probability of disease and mild coronary calcification or those with a calcium score of zero". 

The use of 64-slice CCTA scanners was associated with a non-negligible effective radiation dose and thus, may increase the lifetime attributable risk of cancer. However, second-generation CCTA scanners may be used which can decrease the amount of radiation exposure. A study by Chen and colleagues (2013) reported on 107 participants who received CCTA with a second-generation 320-detector row machine and compared the radiation exposure to 100 participants who had previous imaging with a first-generation scanner. For the second-generation scanner the median radiation dose was 0.93 mSv and 2.76 mSv with the first-generation scanner. This radiation dose places CT scans at an intermediate (1–10 mSv) level of risk under international guidelines, a risk level for which the corresponding benefit should be "moderate" to "substantial." Einstein and colleagues (2007) reported that the use of a 64-slice CCTA is associated with a non-negligible LAR (lifetime attributable risk) of cancer and that the risk is "Considerably greater for women, younger patients and for combined cardiac and aortic scans." CCTA requires the use of intravenous iodinated contrast and, in most cases, beta-blocker or calcium channel blocker medications to slow the heart rate prior to image acquisition. In patients with a GFR > 60, the risks for nephrotoxicity are very low (<1%). Beta-blocker and calcium channel blocker administration, particularly given the short duration of use, are associated with a very low risk (<1%) for adverse reactions. Additionally, CCTA may offer an option in obese patients as data suggests no significant reduction in sensitivity and specificity when compared to non-obese patients. Particularly on newer CT scanner platforms, diagnostic quality images are expected even in patients with modest HR control prior to acquisition, though more thorough pre-scan HR control does allow for better radiation dose reduction. Limitations to utilization of CCTA include patients with irregular heart rhythms, known high levels of coronary calcification (CAC scores > 400), borderline tachycardia (HR>80 despite pre-treatment), baseline renal impairment, and known IV contrast allergy.

A number of controlled clinical trials and registry evidence have addressed the diagnostic accuracy and clinical effectiveness of CCTA in the evaluation of symptomatic patients. Registry data can be broadly subdivided into those that address the use of CCTA to evaluate individuals with symptoms suggestive of CAD, to risk stratify individuals at risk for coronary artery disease, and those that use CCTA after equivocal results of other cardiac imaging procedures, such as myocardial perfusion imaging (MPI) or echocardiography. In part these proposed uses result from the observation that a negative CCTA has high negative predictive value for the presence of CAD (Bluemke, 2008).

There is a large body of evidence evaluating the diagnostic characteristics of CCTA for identifying coronary lesions. The best estimate of the diagnostic characteristics of CCTA can be obtained from recent meta-analyses and systematic reviews. Sensitivities for functional stress testing tended to range between 70% and 90%, depending on the test and study, and specificities ranged between 70% and 90%.  For CCTA, estimates of sensitivity from various systematic reviews are considerably higher. The guideline statement from Fihn cited studies reporting sensitivities between 93% and 97%. A meta-analysis by Ollendorf et al of 42 studies showed a summary sensitivity estimate of 98% and a specificity of 85%. A meta-analysis of 8 studies conducted by the Ontario Health Ministry showed a summary sensitivity estimate of 97.7% and a specificity of 79%.  In the meta-analysis by Nielsen et al, sensitivity of CCTA varied between 98% and 99% (depending on the analysis group). The biggest criticism of historical trials investigating the diagnostic characteristics of any non-invasive testing modality is referral bias: only patients with abnormal tests were referred for invasive coronary angiography (ICA). The recently published PICTURE trial is a prospective, multicenter investigation enrolling 230 patients with chest pain referred for MPI who were subsequently randomized to CCTA or MPI. All patients were then referred for ICA regardless of noninvasive test findings. In this trial, the sensitivity of CCTA to predict a stenosis >50% on ICA was far superior to MPI utilizing both a CCTA stenosis ≥50% (92.0% vs 54.5%, p<0.001) or ≥70% (92.6% vs 59.3%, p<0.001). The odds ratio for CAD on ICA was 12.73 (95%CI 2.43-66.55, p<0.001) for a summed stress score by MPI ≥5% (utilizing a 17-segment model). In contrast, the odds ratio for CAD on ICA was 51.75 (95% CI 8.50-314.94, p<0.001) for CCTA utilizing a stenosis ≥50% (Ollendorf et al, 2011).

The extent and severity of CAD by CCTA has significant prognostic implications. Long term follow-up data from the CONFIRM registry observed that the absence of CAD on CCTA is associated with very favorable prognosis with major adverse cardiac event rates (MACE) of < 1% out to 7 years.  This “warranty period” affords the ability to avoid future unnecessary ischemic testing and provide reassurance to patients. Lin et al found 2.09% mortality rate at 3 years of follow-up in over 2,500 symptomatic patients with nonobstructive CAD (HR 1.98 (1.06-3.69), p=0.03). Up to 25% of nonobstructive CAD (<50%) patients and 50% of obstructive CAD (≥50%) patients will not have detectable perfusion defects by single-photon emission computed tomography (SPECT), thus a significant cohort of these patients at significant risk for mortality and cardiovascular events would be underdiagnosed and incorrectly risk stratified.  CCTA provided incremental prognostic information after adjusting for traditional risk factors with hazard ratios of 2.20 and 2.91 in the 2-vessel and 3-vessel groups, respectively (p=0.013 and 0.001) (Lin et al, 2011).

In addition to very robust diagnostic and prognostic performance when compared to invasive coronary angiography, there is now considerable prospective randomized data demonstrating that CCTA meaningfully guides provider decision making, resulting in improved patient outcomes. SCOT-HEART is a randomized, prospective trial of more than 4,000 patients being evaluated for stable chest pain. Following initial clinical evaluation and, in 85% of patients, an exercise stress electrocardiogram, patients were assigned to undergo CCTA or continue with their previously determined plan of care.  A diagnosis of coronary heart disease was made in 47% of participants and 36% of patients were labeled as having angina due to coronary heart disease following initial clinical evaluation. At 6 weeks, CCTA reclassified 558 (27%) patients to a diagnosis of CHD and 481 (23%) patients to a diagnosis of angina due to CHD (standard care 22 [1%] and 23 [1%]; p<0·0001). CCTA increased the provider diagnostic certainty, as well as the frequency of the diagnosis of CHD (RR 2.56, 95% CI 2·33–2·79; p<0·0001 and RR 1·09, 95% CI 1·02–1·17; p=0·0172, respectively). Furthermore, CCTA also increased provider certainty in the diagnosis of angina due to CHD (RR 1·79, 95% CI 1·62–1·96; p<0·0001). This reclassification and increase in diagnostic certainty resulted in an increased rate of change in planned investigation (15% vs 1%; p<0·0001) and in medical treatments (23% vs 5%; p<0·0001) following CCTA. While there was no significant difference in outcomes reported at 1.7 years of follow-up in the initial publication, 3 year follow-up data showed a significant reduction in both fatal and non-fatal myocardial infarction.  A similar reduction in non-fatal MI was observed in the prospective, multicenter PROMISE trial, which randomized over 10,000 intermediate pre-test risk patients with stable chest pain symptoms to a strategy of functional testing or CCTA as the initial diagnostic evaluation. While PROMISE was a neutral trial with no difference in the primary endpoint between the CCTA arm (3.3%) and the functional-testing arm (3.0%, adjusted HR 1.04; 95% CI 0.83-1.29, p=0.75), death and non-fatal MI was less frequent in the CCTA arm at 12 months of follow-up (HR 0.66, p=0.049).  Additionally, a recently published investigation of the PROMISE data demonstrated superior prognostic and discriminatory ability with CCTA compared with functional testing, in addition to an improvement in appropriate initiation of primary prevention medications, such as aspirin (11.8% vs 7.8%), statins (12.7% vs 6.2%), and beta blockers (8.1% vs 5.3%, p<0.0001 for all) in patients in the CCTA arm when compared to functional testing. The prevalence of healthy eating (p=0.002) and lower rates of obesity (p=0.040) were also observed following CCTA when compared to functional testing (SCOT-HEART investigators, 2015).

A recent meta-analysis combining data from PROMISE and SCOT-HEART, in addition to a third prospective randomized stable chest pain trial (CAPP), concluded that CCTA was associated with a 31% reduction in non-fatal myocardial infarction (HR 0.69, 95% CI 0.49-0.98, p=0.038). While not included in this meta-analysis, recently published data from the nationwide Danish registry demonstrated that evaluation of stable chest pain with CCTA was associated with greater use of statins and aspirin, likely explaining the observed reduction in non-fatal MI in this cohort. CCTA was, however, associated with higher rates of ICA and functional testing costs (Williams et al, 2017).

This observed improvement in hard cardiovascular outcomes following CCTA is likely explained by the unique ability of CCTA to not only detect significant epicardial coronary vessel stenosis, but also to diagnose non-obstructive coronary atherosclerosis. This early detection of CAD allows for early, aggressive implementation of primary prevention medications and positively impacts patient adoption and adherence to lifestyle modifications regarding diet, exercise, smoking cessation, and weight loss. Data in 2,800 consecutive symptomatic patients undergoing CCTA at tertiary hospital centers suggested that CAD burden, even in the absence of a severe stenosis by CCTA, resulted in intensification of primary prevention medical therapy by providers. Additionally, in patients with nonobstructive CAD, those treated with statin therapy had a mortality reduction compared to those without atherosclerotic plaque on CCTA. CCTA also identified a high risk cohort of patients with extensive nonobstructive CAD in whom statin therapy was associated with a significant reduction in cardiovascular death and non-fatal MI (HR=0.18, p=0.011) (Hulten et al, 2014).

Data from the National Cardiovascular Data Registry’s (NCDR) CathPCI Registry demonstrated that, despite a multitude of noninvasive testing modalities available to providers nationwide, 58.4% of patients were found to have no or nonobstructive CAD at the time of elective ICA. In contrast, only 30% of patients referred for ICA after CCTA were found to have nonobstructive CAD.  Revascularization based on findings of high-risk CAD on CCTA was associated with a significant reduction in all-cause mortality with revascularization when compared to medical therapy alone (2.3% vs 5.3%, p=0.008) in the CONFIRM registry. Additionally, the opposite effect was observed in patients without high-risk CAD referred for revascularization compared with medical therapy (2.3% vs 1.0%, p=0.0138). The CCTA allows for more precise risk stratification beyond simple epicardial stenosis for appropriately selecting patients who benefit from revascularization. In prospective trials, CCTA was associated with increased rates of revascularization. A meta-analysis by Hulten et al looking at CCTA in the ED for acute chest pain patients demonstrated a cost savings in 3 of the 4 large randomized control trials (RCTs) and shorter hospital lengths of stay in all 4 studies. An increased referral rate for ICA (OR 1.36, 95% CI 1.03-1.80, p=0.030) and subsequent revascularization (OR 1.81, 95% CI 1.20-2.72, p=0.004) was also observed with a number needed to scan to increase ICA and revascularization over usual care by 1 of 48 and 50, respectively.  The strategy of CCTA as a “gatekeeper” to the catheterization lab was recently presented in soon to be published data from the CONSERVE trial. This multicenter, prospective trial enrolled stable chest pain patients without known CAD who were referred for ICA. Patients were randomized to undergo CCTA followed by selective catheterization based on CCTA results (and at the discretion of the provider) or direct catheterization in patients with elective indications for diagnostic coronary angiography. Pre-test risk, rates of abnormal non-invasive stress testing, and symptoms were similar between the groups. CCTA followed by selective catheterization was associated with a 78% reduction (p<0.001) in per-patient testing, which included the index evaluation plus downstream costs, when compared with direct catheterization. Revascularization rates were 41% lower (p<0.001) in the selective catheterization arm, as well. This resulted in a 50% cardiovascular cost savings ($3,338 vs $6,740, p<0.001) utilizing CCTA as a gatekeeper. Importantly, MACE outcomes were the same between the two strategies over study follow-up. In summary, CCTA can appropriately identify patients who would most benefit from referral for ICA and revascularization and result in lower rates of normal or minimally abnormal findings on ICA making CCTA an effective gatekeeper to the catheterization laboratory. The use of CCTA does seem to increase the rates of revascularization when compared to functional testing, both in the stable chest pain and ED population (Hulten, 2017). 

The addition of CCTA early in the evaluation of patients presenting acutely to the emergency department with chest pain has been extensively studied in prospective, multicenter trials. A 2012 randomized trial by Hoffman and colleagues (ROMICAT II) compared the effectiveness of CCTA with that of standard evaluation in individuals suggestive of acute coronary syndrome in the emergency room. A total of 501 individuals had CCTA, 499 individuals had a standard evaluation in the emergency room.  Individuals were excluded if they had known CAD. The primary endpoint of length of hospital stay was significantly reduced in the CCTA cohort. Additionally, the a priori secondary effectiveness endpoint of time to diagnosis was also decreased with CCTA. Of note, there was more downstream testing and radiation exposure was higher in the CCTA cohort. In another randomized trial, Litt et al (2012) compared individuals at low-to-intermediate risk with possible acute coronary syndromes who presented to the emergency room. Individuals were randomly assigned in a 2:1 ratio to undergo CCTA or receive traditional care. The primary outcome was safety (measured by the rate of cardiac events within 30 days). None of the participants with a negative CCTA had myocardial infarction or died within 30 days. There were no cardiac deaths in the traditional group. And while the CCTA group had a higher rate of discharge from the emergency room and decreased overall length of stay, there were no differences between the groups in the use of invasive angiography or rate of revascularization. The CT-STAT trial (Goldstein et al) compared CCTA with MPI in the early evaluation of nearly 700 patients with acute chest pain and found a 54% reduction in time to diagnosis (p<0.0001), a 38% reduction in cost of care (p<0.0001), and no difference in MACE rates. A subsequent meta-analysis by Hulten et al concluded that ED CCTA was associated with decreased cost and reduced length of stay, but increased ICA and revascularization rates. In summary, the high negative predictive value (NPV) of CCTA in patients presenting to the ED with chest pain permits ruling out coronary disease with high accuracy. The efficiency of the workup is improved, because patients are safely and quickly discharged from the ED with no adverse outcomes among patients with negative CCTA examinations. Finally, CCTA was associated with improved clinical outcomes when instituted in the immediate post-discharge evaluation of patients with acute chest pain discharged from the ED as reported in the CATCH trial.  CCTA demonstrated lower rates of a composite of cardiac death, MI, unstable angina, late symptom-driven revascularization, and chest pain readmission when compared to standard care utilizing bicycle exercise ECG or MPI (11% vs 16%, p=0.04; HR 0.62, 95% CI 0.40-0.98). Additionally, when looking only at major adverse cardiovascular events (MACE), a CCTA-guided strategy was also superior (2% vs 5%, p=0.04), predominantly driven by higher rates of myocardial infarction in the standard care cohort.  CCTA was also compared to high-sensitivity troponin assays (hs-troponins) in the evaluation and disposition of acute chest pain patients presenting to the ED. In a prospective, multicenter trial of 500 patients randomized to hs-troponin based evaluation and disposition to CCTA, there was no difference in the primary endpoint of patients identified with significant CAD requiring revascularization. Additionally, ED discharge rates, ED length of stay, and incidence of undetected ACS were similar. CCTA lowered direct medical costs by 34% (p<0.01) when compared to hs-troponins and there was less downstream testing following the index ED visit (4% vs 10%, p<0.01).

An assessment by the Blue Cross Blue Shield Association Technology Evaluation Center's Evidence Street (revised June 2017) concluded that CCTA in individuals with stable chest pain and intermediate risk for CAD, the evidence is sufficient to determine that the technology results in a meaningful improvement in the net health outcome for patients. Additionally, prior assessment found that CCTA was equally powerful in patients with acute chest pain presenting to the emergency room with no known history of coronary artery disease, and found not to have evidence of acute coronary syndromes. The TEC assessment stated that evidence obtained in the emergency setting, similar to more extensive results among ambulatory patients, indicates a normal CCTA appears to provide a prognosis as good as other noninvasive tests (BCBSA, 2011).

Cardiac CT angiography often produces non-cardiac incidental findings.  To evaluate the incidence, clinical importance, and costs of these incidental findings, MacHaalany, et al (2009) studied 966 consecutive patients who underwent CTA. Incidental findings were noted in 401 patients (41.5 %); of these, 12 were deemed to be clinically significant (e.g., 5 thrombi, 1 aortic dissection that was not clinically suspected, 1 ruptured breast implant), and 68 were deemed to be indeterminate (e.g., 34 non-calcified pulmonary nodules less than 1 cm, 11 larger lung nodules, 9 liver nodules/cysts).  After a mean 18-month follow-up, no indeterminate finding became clinically significant, although 3 malignancies were diagnosed after subsequent diagnostic tests.  Non-cardiac and cancer death rates were not significantly different between patients with and without incidental findings.  In all, 164 additional diagnostic tests and procedures were performed in the 80 patients with indeterminate or clinically significant incidental findings, including 1 patient who suffered empyema and abdominal abscesses as a complication of transthoracic biopsy.

In an observational study, Kim and colleagues (2013) evaluated the prevalence and characteristics of coronary atherosclerosis in asymptomatic subjects classified as low-risk by National Cholesterol Education Program (NCEP) guideline using CCTA.  A total of 2,133 (49.2 %) subjects, who were classified as low-risk by the NCEP guideline, of 4,339 consecutive middle-aged asymptomatic subjects who underwent CCTA with 64-slice scanners as part of a general health evaluation were included in this study.  Main outcome measures were the incidence of atherosclerosis plaques and significant stenosis.  In the subjects at low-risk, 11.4 % (243 of 2,133) of subjects had atherosclerosis plaques, 1.3 % (28 of 2,133) of subjects had significant stenosis, and 0.8 % (18 of 2,133) of subjects had significant stenosis caused by non-calcified plaque (NCP).  Especially, 75.0 % (21 of 28) of subjects with significant stenosis and 94.4 % (17 of 18) of subjects with significant stenosis caused by NCP were young adults.  Mid-term follow-up (29.3 ± 14.9 months) revealed 4 subjects with cardiac events: 3 subjects with unstable angina requiring hospital stay and 1 subject with percutaneous coronary intervention.  The authors concluded that although an asymptomatic population classified as low-risk by the NCEP guideline has been regarded as a minimal risk group, the prevalence of atherosclerosis plaques and significant stenosis were not negligible.  However, considering very low event rate for those patients, CCTA should not be performed in low-risk asymptomatic subjects, although CCTA might have the potential for identification of high-risk groups in the selected subjects regarded as a minimal-risk group by NCEP guideline.

Dorr and associates (2013) stated that clinical studies have consistently shown that there is only a very weak correlation between the angiographically determined severity of CAD and disturbance of regional coronary perfusion.  On the other hand, the results of randomized trials with a fractional flow reserve (FFR)-guided coronary intervention (DEFER, FAME I, FAME II) showed that it is not the angiographically determined morphological severity of CAD but the functional severity determined by FFR that is critical for prognosis and the indications for re-vascularization.  A non-invasive method combining the morphological image of the coronary anatomy with functional imaging of myocardial ischemia is therefore particularly desirable.  An obvious solution is the combination of CCTA with a functional procedure, such as perfusion positron emission tomography (PET), perfusion single photon emission computed tomography (SPECT) or perfusion magnetic resonance imaging (MRI).  This can be performed with fusion imaging or with hybrid imaging using PET-CT or SPECT-CT.  First trial results with PET-CCTA and SPECT-CCTA carried out as cardiac hybrid imaging on a 64-slice CT showed a major effect to be a decrease in the number of false-positive results, significantly increasing the specificity of CCTA and SPECT.  The authors concluded that although the results are promising, due to the previously high costs, low availability and the additional radiation exposure, current data are not yet sufficient to give clear recommendations for the use of hybrid imaging in patients with a low-to-intermediate risk of CAD.  Moreover, they stated that ongoing prospective studies such as the SPARC or EVINCI trials will bring further clarification.

In a retrospective study, Kang et al (2014) evaluated coronary arterial lesions and assessed their correlation with clinical findings in patients with Takayasu arteritis (TA) by using coronary CT angiography.  A total of 111 consecutive patients with TA (97 females, 14 males; mean age of 44 years ± 13.8 [standard deviation]; age range of 14 to 74 years) underwent CT angiography of the coronary arteries and aorta with 128-section dual-source CT.  Computed tomography angiographic, clinical, and laboratory findings of each patient were retrospectively reviewed.  Statistical differences between coronary CT angiographic findings and clinical parameters were examined with uni-variate analysis.  Of 111 patients, 32 (28.8 %) had cardiac symptoms and the remaining 79 (71.2 %) had no cardiac symptoms; 59 patients (53.2 %) had coronary arterial lesions at coronary CT angiography.  Three main radiologic features were detected:
  1. coronary ostial stenosis (n = 31, 28.0 %),
  2. non-ostial coronary arterial stenosis (n = 41, 36.9 %), and
  3. coronary aneurysm (n = 9, 8.1 %). 

Coronary artery ostial or luminal stenosis of 50 % or more or coronary aneurysms were observed in 26 (23.4 %) patients with TA.  Patients with coronary arterial abnormalities at coronary CT angiography had higher incidences of hypertension (p = 0.02), were older at the time of CT (p = 0.01), and had longer duration of TA (p = 0.02) than those without coronary artery abnormalities.  The presence of cardiac symptoms, disease activity, and other co-morbidities was not associated with differences in coronary artery involvement.  The authors concluded that in patients with TA, there is a high prevalence of coronary arterial abnormalities at coronary CT angiography, regardless of disease activity or symptoms.  Thus, these researchers noted that coronary CT angiography may add information on coronary artery lesions in patients with TA.

Marwick et al (2015) discussed the potential of CCTA to serve as an effective gatekeeper to invasive coronary angiography. The authors note that functional testing prior to ICA is not widespread. Possibly as a consequence, 40% of angiograms in the National Cardiovascular Database Registry detect normal coronary arteries. The authors reviewed the PROMISE trial outcomes and noted that although the findings are insufficient to conclude the possibility of either harm or benefit from the use of CCTA, a particularly salient feature was that although catheterization was performed in more CCTA patients in the 90 days following noninvasive testing, the likelihood of nonsignificant CAD was significantly lower in the CCTA group (3.4% vs. 4.3%; p = 0.02). The authors state that CCTA is a promising noninvasive method for identification and exclusion of CAD, which may provide a diagnostic paradigm to curb unnecessary invasive testing. CCTA has the potential to serve as an effective gatekeeper to curb unnecessary ICA. However, there is no definitive evidence to favor either a CCTA-guided or a stress testing–guided approach for evaluation of acute CP. The authors believe the PROMISE trial results are equivocal and concluded that results from future prospective multicenter studies will be needed to justify CCTA’s contribution to patients with suspected CAD for ICA.

Williams et al (2016) conducted a prospective, randomized, controlled, multicenter trial to evaluate the consequences of CCTA-assisted diagnosis on invasive coronary angiography (ICA), preventive treatments, and clinical outcomes. A little over 4,000 patients were randomized to receive standard care or standard care plus coronary computed tomography angiography (CCTA). The investigators found that despite similar overall rates (409 vs. 401; p = 0.451), ICA was less likely to demonstrate normal coronary arteries (p < 0.001) but more likely to show obstructive CAD (p = 0.005) in those allocated to CCTA. More preventive therapies (p < 0.001) were initiated after CCTA, with each drug commencing at a median of 48 to 52 days after clinic attendance. From the median time for preventive therapy initiation (50 days), fatal and nonfatal myocardial infarction was halved in patients allocated to CCTA compared with those assigned to standard care (p = 0.020). Cumulative 6-month costs were slightly higher with CCTA: difference $462 (95% CI: $303 to $621). The investigators concluded that their findings show that CCTA allows more appropriate and effective selection of ICA related to CAD.

CCTA is generally contraindicated for decompensated heart failure; however, may be considered on a case-by-case basis (Abbara et al, 2016).

Jorgensen et al (2017) conducted an observational, non-randomized study to compare functional testing to CCTA in patients with stable coronary artery disease. The investigators studied patients enrolled in a Danish registry who underwent initial noninvasive cardiac testing with either a CCTA or functional testing (exercise electrocardiography or nuclear stress testing) from 2009 to 2015. They further evaluated the use of noninvasive testing, invasive procedures, medications, and medical costs within 120 days. Out of 86,705 patients, 53,744 underwent functional testing and 32,961 underwent CCTA. Compared with functional testing, there was significantly higher use of statins (15.9% vs. 9.1%), aspirin (12.7% vs. 8.5%), invasive coronary angiography (14.7% vs. 10.1%), and percutaneous coronary intervention (3.8% vs. 2.1%); all p < 0.001 after CCTA. The mean costs of subsequent testing, invasive procedures, and medications were higher after CCTA (p < 0.001). Unadjusted rates of mortality (2.1% vs. 4.0%) and MI hospitalization (0.8% vs. 1.5%) were lower after CCTA than functional testing (both p < 0.001). After adjustment, CCTA was associated with a comparable all-cause mortality (HR: 0.96; 95% CI: 0.88 to 1.05), and a lower risk of MI (HR: 0.71; 95% CI: 0.61 to 0.82). The investigators concluded that CCTA was associated with greater use of statins, aspirin, invasive procedures, and higher costs than functional testing in stable patients who were evaluated for suspected CAD. The investigators also concluded that although CCTA was associated with a lower risk of MI, it had a similar risk of all-cause mortality.

The Prospective Multicenter Imaging Study for Evaluation of chest pain (PROMISE) trial was a pragmatic trial that recruited a large cohort from USA and Canadian centres to determine whether an initial assessment of suspected stable CAD using CTCA reduces major adverse cardiovascular events (Douglas, et al, 2015). There was no improvement in death, myocardial infarction or major procedural complication after a median of 2-years of follow-up when compared with a functional-guided strategy.

Hoffman et al (2017) discussed insights from the prospective, randomized, multicenter PROMISE trial which evaluated the prognostic value of noninvasive cardiovascular testing in patients with stable chest pain. The authors note that there are limited data from randomized trials comparing anatomic with functional testing for determining optimal management of patients with stable chest pain. In the PROMISE trial, patients with stable chest pain and intermediate pretest probability for obstructive CAD were randomly assigned to functional testing (exercise electrocardiography, nuclear stress, or stress echocardiography) or CCTA. The primary end point was death, myocardial infarction, or unstable angina hospitalizations over a median follow-up of 26.1 months. Both the prevalence of normal test results and incidence rate of events in these patients were significantly lower among 4500 patients randomly assigned to CTA in comparison with 4602 patients randomly assigned to functional testing (both P<0.001). In CTA, 54.0% of events (n=74/137) occurred in patients with non-obstructive CAD (1%-69% stenosis). Prevalence of obstructive CAD and myocardial ischemia was low (11.9% versus 12.7%, respectively), with both findings having similar prognostic value (95% CI, 2.60-5.39; and 3.47; 95% CI, 2.42-4.99). When test findings were stratified as mildly, moderately, or severely abnormal, hazard ratios for events in comparison with normal tests increased proportionally for CTA (2.94, 7.67, 10.13; all p<0.001) but not for corresponding functional testing categories (0.94 [p=0.87], 2.65 [p=0.001], 3.88 [p<0.001]). They found that anatomic assessment with CCTA provided significantly better prognostic information compared to function testing (p=0.04). They noted that adding the Framingham Risk Score to functional test results significantly improved the prognostic value of functional testing. If 2714 patients with at least an intermediate Framingham Risk Score (>10%) who had a normal functional test were reclassified as being mildly abnormal, the discriminatory capacity improved to 0.69 (95% CI, 0.64-0.74). The authors stated that contemporary stable chest pain populations present with a low prevalence of myocardial ischemia and obstructive CAD, and that in that particular population, CCTA provides better prognostic information than functional testing. The authors concluded that in this population, the detection of non-obstructive CAD identifies additional at-risk patients while consideration of the Framingham Risk Score is important for proper risk stratification of patients with normal stress testing. These results may contribute to a better understanding of how to use this information to guide management of these patients.

Newer generation CT scanners have emerged which allows for faster, higher-quality images. Dual-Source CT (DSCT) scanners allow for a "gapless acquisition with a pitch of up to 3.4 which cannot be achieved with conventional single-source CT scanners. A high-pitch spiral acquisition can be performed in less than one second and thus information from a single heartbeat can be generated. In combination with iterative reconstruction techniques, high-pitch spiral acquisition allows for cardiac CT with sub-milliSievert doses". Contraindications include acute MI, screening asymptomatic patients with low-to-intermediate risk of CAD, evaluation of coronary artery stents less than 3 mm, and evaluation of asymptomatic patients post CABG less than 5 years old and post sent placement less than 2 years old (Bell et al, 2018).

CCTA with slower temporal resolution scanners, such as the 64-slice single-source CT scanner, is not recommended in persons with significant arrhythmia or atrial fibrillation (AF). Arrhythmias have presented a challenge due to motion artifact resulting from irregular rhythm; however, studies are now showing that newer generation CT scanners are capable of providing quality images for patients with AF. CCTA with dual-source CT scanner technology and algorithms have been developed to perform CCTA in persons with atrial fibrillation who cannot be effectively imaged with single-source CT. These newer generation CT scanners allow faster temporal resolution and are capable of producing motion-free images (Soman et al, 2017).

Yang et al (2015) evaluated 85 patients with persistent atrial fibrillation (AF) who underwent prospective ECG-triggered sequential second-generation dual-source CCTA. Their aim was to evaluate the effects of mean heart rate (HR) and heart rate variation (HRV) on image quality and analyze the diagnostic accuracy. Tube current and voltage were adjusted according to BMI (range 17.3-36.3 kg/m2) and iterative reconstruction was used. Image quality of coronary segments (four-point scale) and presence of significant stenosis (>50%) were evaluated. Diagnostic accuracy was analyzed in 30 of the 85 patients who underwent additional invasive coronary angiography (ICA). All subjects had AF longer than 1 year. The results showed that 8 of 1102 (0.7%) segments demonstrated poor image quality. No significant impact on image quality was found for mean HR (p=0.663) or HRV (p=0.895). On per-segment analysis, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 89.7% (26/29), 99.4% (355/357), 92.9% (26/28), and 99.2% (355/358), respectively, with excellent correlation (kappa=0.91) with ICA. Mean effective dose was 3.3±1.0 mSv. The authors concluded that "prospectively ECG-triggered sequential dual-source CCTA provides diagnostic image quality and good diagnostic accuracy for detection of coronary artery stenosis in AF patients without significant influence by HR or HRV."

The Society of Cardiovascular Computed Tomography (SCCT) guidelines committee produced an update in 2016 which states that "the development of dual-source CT and wide-detector scanners may allow imaging of selected patients with higher and irregular heart rates such as atrial fibrillation with diagnostic imaging quality. It should be acknowledged, however that coronary CTA in high or irregular heart rates typically is associated with a higher radiation dose. Moreover, in the event of irregular heart rates or atrial fibrillation it is essential that other determinants of image quality such as coronary calcification, body weight and patient cooperation are taken into consideration before  deciding whether to proceed with the scan. The presence of frequent premature complexes prior to scanning therefore should trigger consideration of aborting the examination." (Abbara et al, 2016).

Prazeres et al (2018) compared image quality and radiation dose of coronary computed tomography (CT) angiography performed with dual-source CT scanner using 2 different protocols in patients with atrial fibrillation (AF). The study included 732 subjects with AF who underwent 2 different acquisition protocols: double high-pitch (DHP) spiral acquisition and retrospective spiral acquisition. The image quality was ranked according to a qualitative score by 2 experts: 1, no evident motion; 2, minimal motion not influencing coronary artery luminal evaluation; and 3, motion with impaired luminal evaluation. A third expert was included to resolve any disagreement. The results reflected that the DHP group (24 patients, 374 segments) showed more segments classified as score 1 than the retrospective spiral acquisition group (71.3% vs 37.4%). Image quality evaluation agreement was high between observers (κ = 0.8). There was significantly lower radiation exposure for the DHP group (3.65 [1.29] vs 23.57 [10.32] mSv). The authors concluded that their comparison showed that a double high-pitch spiral protocol for CCTA acquisition resulted in lower radiation exposure and superior image quality in patients with AF compared with conventional spiral retrospective acquisition.

With the advent of the third generation dual-source CT, persons with BMI greater than or equal to 40 may now be able to undergo a CCTA. Mangold et al (2016) conducted a retrospective study to evaluate the quality of third generation dual-source CT (CCTA) in obese patients. The study included 102 obese patients who had undergone CCTA performed with (third) generation dual-source CT, prospectively ECG-triggered acquisition at 120 kV, and automated tube current modulation. Patients were divided into three BMI groups:
  1. 25-29.9 kg/m(2);
  2. 30-39.9 kg/m 2); and
  3. ≥ 40 kg/m(2).

Vascular attenuation in the coronary arteries was measured. Contrast-to-noise ratio (CNR) was calculated. Image quality was subjectively evaluated using five-point scales. Image quality was considered diagnostic in 97.6 % of examinations. CNR was consistently adequate in all groups but decreased for groups 2 and 3 in comparison to group 1 as well as for group 3 compared to group 2 (p = 0.001, respectively). Subjective image quality was significantly higher in group 1 compared to group 3 (p < 0.001). The mean effective dose was 9.5 ± 3.9 mSv for group 1, 11.4 ± 4.7 mSv for group 2 and 14.0 ± 6.4 mSv for group 3. The authors concluded that diagnostic CCTA, with 3(rd) generation DSCT at 120 kV, can routinely be performed in persons with BMI greater than 40.

Chinnaiyan et al (2009) investigated the dual-source computed tomography (DSCT), which was novel at that time, in morbidly obese patients. The authors state that persons with BMI greater than or equal to 40 have an increased risk of cardiovascular morbidity and mortality but have not been able to obtain a CCTA due to reduced accuracy. The authors conducted an observational study of 50 patients with mean BMI 44.8. Each patient served as their own control. After a single DSCT acquisition, standard quarter-scan image reconstructions at a temporal resolution of 83 milliseconds were compared with temporal resolution reconstructions at 105, 125, and 165 milliseconds. Images were evaluated for diagnostic adequacy score and for image noise, signal-to-noise ratio, and contrast-to-noise ratio. In each patient, the image reconstruction with the best visual diagnostic score was compared with the control image for quantitative measures. The authors found that scans were of diagnostic quality in 47 (94%) patients using the "best reconstruction" compared with 38 (76%) patients using quarter-scan reconstruction. Significant improvements were observed in noise (p < 0.0001), contrast-to-noise ratio (p = 0.0038), and signal-to-noise ratio (p = 0.030). The authors concluded that "CCTA with DSCT using a modified scan protocol and adjustable temporal reconstructions provides diagnostic image quality in >90% of morbidly obese patients."

A 2016 guideline update produced by the Society of Cardiovascular Computed Tomography (SCCT) discussed weight considerations for CCTA. The guidelines states that "scan settings should be adjusted to the patient's body weight. Both tube voltage and tube current should be optimized to deliver the least necessary radiation for adequate image quality. In obese patients, higher tube current and tube voltage are required in order to preserve contrast to noise ratio. More importantly, tube current should be adjusted to the total volume of soft tissues within the scanned region. The specific adjustments are dependent on the scanner specifications (Abbara et al, 2016).

Noninvasive Fractional Flow Reserve (HeartFlow FFRCT)

HeartFlow FFRCT (HeartFlow, Inc, Redwood City, CA) is a coronary physiologic simulation software used for the clinical qualitative and quantitative analysis of previously acquired computerized tomography Digital Imaging and Communications in Medicine (DICOM) data. The software provides a non-invasive method of estimating fractional flow reserve using standard coronary CT angiography (CCTA) image data (NICE, 2017).

FFR is the ratio between the maximum blood flow in a narrowed artery and the maximum blood flow in a normal artery. FFR is currently measured invasively using a pressure wire placed across a narrowed artery. An assessment by the BlueCross BlueShield Association Technology Evaluation Center (BCBSA, 2011) concluded that invasive fractional flow reserve guided percutaneous coronary intervention (PCI) results in better outcomes than an angiography alone guided strategy for persons who are undergoing revascularization. The assessment concluded that "The evidence is consistent with prior physiologic data and long-held beliefs that identifying stenoses is insufficient to determine when revascularization is likely to have benefit. If revascularization is anticipated in patients with angina, evidence supports a conclusion that FFR-guided PCI results in better outcomes than an angiography alone-guided strategy." 

A medical consultation technology document from the National Institute for Health and Care Excellence (NICE, 2016) found that "[t]he case for adopting HeartFlow FFRCT for estimating fractional flow reserve from coronary CT (CCT) angiography is supported by the evidence. The technology is non-invasive and safe, and has a high level of diagnostic accuracy." The consultation stated that HeartFlow FFRCT should be considered as an option for patients with stable, recent onset chest pain of suspected cardiac origin and a clinically determined intermediate (10% to 90%) risk of coronary artery disease. The consultation technology document found that, using HeartFlow FFRCT may avoid the need for invasive coronary angiography and revascularisation. For correct use, HeartFlow FFRCT requires access to 64-slice (or above) coronary CT angiography facilities.

NICE guidance (2017) states that "[t]he case for adopting HeartFlow FFRCT for estimating fractional flow reserve from coronary CT angiography (CCTA) is supported by the evidence. . . .  HeartFlow FFRCT should be considered as an option for patients with stable, recent onset chest pain who are offered CCTA as part of the NICE pathway on chest pain. Using HeartFlow FFRCT may avoid the need for invasive coronary angiography and revascularisation." The guidance notes that, for correct use, HeartFlow FFRCT requires access to 64‑slice (or above) CCTA facilities. Because the safety and effectiveness of FFRCT analysis has not been evaluated in other patient subgroups, HeartFlow FFRCT is not recommended in patients who have an acute coronary syndrome or have had a coronary stent, coronary bypass surgery or myocardial infarction in the past month.

The American College of Cardiology CathPCI Registry (Messenger et al, 2017) has announced that they will allow FFRCT as an acceptable noninvasive method of documenting ischemia around the time of revascularization. Documentation of ischemia around the time of revascularization is important to the appropriate use criteria (AUC) for percutaneous coronary interventions (PCI). 

Calcium Scoring

Coronary artery calcium (CAC) scoring is a noninvasive test that has been reported to detect the presence of subclinical coronary artery disease (CAD) by measuring the location and extent of calcium in the coronary arteries. Purportedly, the presence of (CAC) has been shown to be strongly correlated with the extent of atherosclerotic plaque as well as the severity of CAD. Tests to determine CAC scoring include multi-slice computed tomography, and electron beam computed tomography (EBCT), also known as ultrafast computed tomography (UFCT).

Ultrafast computed tomography (also known as electron-beam computed tomography [EBCT]) has been shown to be able to quantify the amount of calcium in the coronary arteries, and thus has been primarily investigated as a tool to predict risk of CAD.  In ultrafast CT, an electron-beam is magnetically steered along stationary tungsten rings to produce a rotating X-ray beam.

Research has indicated that EBCT is highly sensitive in detecting coronary artery calcification in comparison to other types of CT.  Moreover, various studies have shown a strong correlation between EBCT calcium scores and quantities of atherosclerotic plaque.  However, there is skepticism about the relationship between EBCT calcium scores and the likelihood of coronary events because of the following factors:

  • Calcium does not collect exclusively at sites with severe stenosis; 
  • EBCT calcium scores do not identify the location of specific vulnerable lesions;
  • Substantial non-calcified plaque is frequently present in the absence of coronary artery calcification;
  • There are no proven relationships between coronary artery calcification and the probability of plaque rupture.

Some advocates have argued that EBCT scores could be an effective substitute for standard risk factors in predicting the risk of coronary artery disease.  However, citing evidence that shows that only a small proportion of asymptomatic individuals with calcified coronary arteries ultimately develop symptomatic coronary artery disease, a 1996 American Heart Association (AHA) scientific statement on coronary artery calcification concludes that the presence of coronary artery calcium is a poor predictor of coronary artery disease risk, and that there is no role for ultrafast CT as a general screening tool to detect atherosclerosis in people who have no symptoms of the disease and no risk factors.  More importantly, although a negative scan may mean a low probability of significant artery blockage in asymptomatic people with or without a previous cardiac event (e.g., myocardial infarction, bypass surgery, angioplasty, etc.), an unstable or vulnerable plaque may go undetected by ultrafast CT, and may rupture and cause thrombosis and obstruction of the coronary artery.  Detrano (1999) demonstrated that the addition of EBCT data provided no added value to the risk of coronary artery disease risk determined by the Framingham and National Cholesterol Education Program risk models.

Several investigators have examined the potential role of ultrafast CT measurements of coronary artery calcium in ruling out coronary artery disease in patients with atypical anginal symptoms.  The AHA report estimates that the negative predictive value of an ultrafast CT scan in these patients ranges from 90 % to 95 %, and suggests that a negative study may be useful in determining the need for further work-up with exercise stress testing and/or angiography.  It must be realized, however, that ultrafast CT provides only anatomic and not physiologic information.  Although ultrafast CT can be used to determine whether calcium is present in the coronary arteries, it can not replace stress testing and angiography in determining whether lesions result in significant coronary artery obstruction and ischemia.  Ultrafast CT is being investigated for this proposed use.

The AHA does not recommend ultrafast CT as a replacement for stress testing and/or angiography in patients with conventional risk factors and in patients with typical anginal chest pain.  The increased predictive value of ultrafast CT of the coronary arteries relative to traditional risk factor assessment is not yet defined.  Although a greater amount of calcium may indicate a greater likelihood of obstructive disease, studies have shown that site-specificity and exact 1:1 correlations are not well predicted, that is, ultrafast CT can not define the location or amount of obstruction with sufficient accuracy to be of use in predicting risk of coronary artery disease, in diagnosing coronary artery disease, or in planning surgical treatment.

Several studies have shown a variability in repeated measures of coronary calcium by ultrafast CT; therefore, use of serial ultrafast CT scans in individual patients to track the progression or regression of calcium is problematic.  Although there is emerging evidence that ultrafast CT may help in identifying the presence of early coronary artery disease in people with known heart disease risk factors, there is no definitive evidence that ultrafast CT can substitute for coronary angiography because the absence of calcific deposits on an ultrafast CT scan does not imply the absence of atherosclerosis.  Conversely, the presence of calcium does not secure a diagnosis of significant angiographic narrowing.  There is still a need for further clarification regarding the relationship between calcification, atherosclerosis, and risk of plaque rupture.

The critical issue that defines the utility (or lack thereof) of ultrafast CT is its prognostic value.  The evidence in the peer-reviewed medical literature linking detectable coronary calcium to event outcomes such as future coronary bypass surgery, angioplasty, myocardial infarction, and coronary death is limited.  Large-scale prospective studies are still needed to define a role for ultrafast CT.

In a review on coronary artery calcium scoring by means of EBCT, Thomson and Hachamovitch (2002) stated that studies have indicated that the very early detection of a coronary artery burden is possible with EBCT.  However, both the Prevention Conference V and the ACC/AHA Expert Consensus Document on EBCT have recommended against the routine use of EBCT for screening for CAD in asymptomatic individuals.  Moreover, there is no evidence so far to support using the results of EBCT in an asymptomatic patient to select a therapy or to guide referral to invasive investigations.  The clinical role of EBCT is yet to be established in terms of screening for disease or risk assessment.  Electron beam computed tomography is highly sensitive, but its specificity is low.  In fact, when referral to angiography is based on the results of EBCT, referrals will be made for very few patients with normal results while many referrals will be made for those with abnormal results.  The outcome will be that, in clinical practice, the observed sensitivity of EBCT will be increased, and the observed specificity will be reduced.  To date, there are no well-conducted studies that clearly demonstrate the incremental value of calcium scoring over traditional assessments of risk factors, and the clinical role of EBCT is yet to be established in terms of screening for disease or risk assessment.  The authors’ view is shared by Redberg and Shaw (2002) who stated that widespread use of EBCT is not recommended.  More research is needed to establish the effectiveness of EBCT in the role of risk factor reduction and prevention of cardiovascular disease.  Furthermore, Greenland (2003) stated that "To date, most research on EBT [electron-beam computed tomography] has been observational in nature, based entirely on self-referred patients" and that the "role of EBT remains uncertain" and that "additional randomized trials to define specific roles for EBT in risk prediction" are needed.

These conclusions are consistent with those of the U.S. Preventive Services Task Force (2004), which stated that there is "insufficient evidence to recommend for or against routine screening with ... EBCT [electron beam CT] scanning for coronary calcium for either the presence of severe [coronary artery stenosis] or the prediction of [coronary heart disease] events in adults at increased risk for coronary heart disease.”  The USPSTF reaffirmed their position in 2009, stating that the evidence is insufficient to assess the balance of benefits and harms of using coronary artery calcification (CAC) score on electron-beam computed tomography (EBCT) to screen asymptomatic men and women with no history of CHD to prevent CHD events.

Guidelines from the American College of Cardiology and the American Heart Association on assessment of cardiovascular risk (Goff et al, 2014) concluded that CAC score may be considered to inform decision making if, after quantitative risk assessment, a risk-based treatment decision is uncertain. This was a grade E recommendation (expert opinion), meaning that “[t]here is insufficient evidence or evidence is unclear or conflicting, but this is what the Work Group recommends.” The guidelines state that, on the basis of current evidence, it is the Work Group's opinion that assessments of CAC “show some promise for clinical utility among the novel risk markers, based on limited data.” The Work Group noted that a review by Peters et al (2012) provides evidence to support the contention that assessing CAC is likely to be the most useful of the current approaches to improving risk assessment among individuals found to be at intermediate risk after formal risk assessment. Further research is recommended in this area.

American College of Cardiology/American Heart Association guidelines (Greenland et al, 2010) have two Class IIa recommendations for screening with calcium scoring, where Class IIa recommendations are defined as those for which “[t]he weight of evidence or opinion is in favor of the procedure or treatment.” Class IIa recommendations for calcium scoring are for asymptomatic patients with an intermediate (10% to 20%) 10-year risk of cardiac events based on the Framingham risk score (FRS) or other global risk algorithm, and for asymptomatic patients 40 years and older with diabetes mellitus. The guidelines state that there are no data demonstrating that serial CAC testing leads to improved outcomes or changes in therapeutic decision making.

Multi-slice (or multi-row detector) CT and spiral (or helical) CT has also been used to quantify calcium in the coronary arteries.  Spiral or helical CT differs from conventional CT in that the patient is continuously rotated as he is moved.  Multi-slice CT is a technical advance over spiral CT, and uses multiple rows of detector arrays to rapidly obtain multiple slices with one pass.  Multi-slice CT differs from ultrafast CT in that the latter has no moving parts, and ultrafast CT scans are faster than with multi-slice CT.  One study examined the accuracy of spiral CT in evaluating coronary calcification, using ultrafast CT as the gold standard for comparison, in 33 asymptomatic individuals who were referred for calcium scans.  Spiral CT was reported to have a sensitivity of 74 % and a specificity of 70 % compared to ultrafast CT.  An assessment of spiral CT and multi-slice CT in screening persons with coronary artery disease by the Canadian Coordinating Office for Health Technology Assessment (2003) found no adequate long-term studies on clinical outcomes of people screened with multi-slice CT or spiral CT.  In addition, the assessment failed to identify studies that compared spiral CT and multi-slice CT with established screening modalities like risk factor algorithms.  The authors noted that the low specificity of spiral CT and multi-slice CT gives rise to concern over false-positive results, and that false-positives may cause harm and expense due to inappropriate and invasive follow-up.  The assessment concluded that “[t]here is insufficient evidence at this time to suggest that asymptomatic people derive clinical benefit from undergoing coronary calcification screening using MSCT [multislice CT] or spiral CT scanning." 

In an editorial accompanying a meta-analysis of electron-beam CT for CAD by Pletcher et al (2004), Ewy (2004) explained that "the clinical utility of fast computed tomography (CT) scanners (i.e., the electron beam [EB] and double helical CT scanner) is still limited.  Electron beam CT is not ready for prime time."

An assessment of the literature on calcium scoring by the German Agency for Health Technology Assessment (DAHTA, 2006) concluded that measuring coronary calcium is a "promising" tool for risk stratification, but that many questions remain unanswered about the targeted use in medical practice, including which patient groups should be screened,  which calcium score threshold should be applied, and which scoring method should be used. 

An assessment prepared for the National Coordinating Centre for Health Technology Assessment (Waugh et al, 2006) found: "CT examination of the coronary arteries can detect calcification indicative of arterial disease in asymptomatic people, many of whom would be at low risk when assessed by traditional risk factors.  The higher the CAC score, the higher the risk.  Treatment with statins can reduce that risk.  However, CT screening would miss many of the most dangerous patches of arterial disease, because they are not yet calcified, and so there would be false-negative results: normal CT followed by a heart attack.  There would also be false-positive results in that many calcified arteries will have normal blood flow and will not be affected by clinically apparent thrombosis: abnormal CT not followed by a heart attack."  The NCCHTA assessment concluded: "For CT screening to be cost-effective, it has to add value over risk factor scoring, by producing sufficient extra information to change treatment and hence cardiac outcomes, at an affordable cost per quality-adjusted life-year.  There was insufficient evidence to support this. Most of the NSC [National Screening Committee] criteria were either not met or only partially met."

An assessment by the Institute for Clinical Effectiveness and Health Policy (Bardach, 2005) concluded: "Most consensus consider EBCT, SCT and MSCT still at their investigational stage for the following:
  1. Detection of coronary artery calcifications as a screening method for asymptomatic subjects with coronary disease;
  2. Detection of coronary artery calcifications in symptomatic patients; and
  3. Assessment of coronary graft viability. 

No study reported that calcification measuring (plaque characterization) reduces the incidence of coronary events or death."

Detrano and associates (2008) noted that in white populations, computed tomographic measurements of coronary artery calcium (CAC) predict coronary heart disease (CHD) independently of traditional coronary risk factors.  However, it is unclear if CAC predicts coronary heart disease in other racial or ethnic groups.  These researchers collected data on risk factors and performed scanning for CAC in a population-based sample of 6,722 men and women, of whom 38.6 % were white, 27.6 % were black, 21.9 % were Hispanic, and 11.9 % were Chinese.  The study subjects had no clinical cardiovascular disease at entry and were followed for a median of 3.8 years.  There were 162 coronary events, of which 89 were major events (myocardial infarction or death from coronary heart disease).  In comparison with participants with no CAC, the adjusted risk of a coronary event was increased by a factor of 7.73 among participants with coronary calcium scores between 101 and 300 and by a factor of 9.67 among participants with scores above 300 (p < 0.001 for both comparisons).  Among the 4 racial and ethnic groups, a doubling of the calcium score increased the risk of a major coronary event by 15 to 35 % and the risk of any coronary event by 18 to 39 %.  The AUCs for the prediction of both major coronary events and any coronary event were higher when the calcium score was added to the standard risk factors.  The authors concluded that the coronary calcium score is a strong predictor of incident coronary heart disease and provides predictive information beyond that provided by standard risk factors in 4 major racial and ethnic groups in the United States.  No major differences among racial and ethnic groups in the predictive value of calcium scores were detected.  While there were some interesting differences in the prevalence of CAC among the 4 racial and ethnic groups, what remains unclear is how this test should best be employed, or if it should be used at all, to attain better health outcomes for patients.

Calcium scoring may be useful when performed with an otherwise indicated multi-slice cardiac CTA to assess the calcium burden of the coronary arteries to determine whether an adequate scan can be obtained.  The calcium score may be estimated with a scout scan, and the injection of contrast withheld if it appears that the patient has a prohibitively high calcium score.  This allows one to avoid exposing the patient to unnecessary radiation from contrast if it is clear that the patient's calcium score is so high that an adequate image of the coronary vessels can not be obtained.  In such cases, the patient may need invasive angiography to adequately assess the coronary vessels.

Baig and colleagues (2009) stated that CAD is present in 38 % to 40 % of patients starting dialysis.  Both traditional and chronic kidney disease-related cardiovascular risk factors contribute to this high prevalence rate.  In patients with end-stage renal disease, CAD, especially acute myocardial infarction, is under-diagnosed.  Dobutamine stress echocardiography and, to a lesser extent, stress myocardial perfusion imaging have proved useful in screening for CAD in such patients.  Coronary artery calcium scoring is less useful.  Acute myocardial infarction is associated with high short- and long-term mortality in dialysis patients.  Cardiac troponin I appears to be more specific than cardiac troponin T or creatine kinase MB subunits in the diagnosis of acute myocardial infarction.

Ma and colleagues (2010) examined the relationship between coronary calcium score (CCS) and angiographic stenosis on a patient-based or vessel-based analysis.  A total of 91 consecutive patients underwent both low-dose 64-slice CT calcium scoring scan as well as conventional angiography of the heart.  The total CCS of abnormal coronary angiogram (n = 45) was 297.38 +/- 416.93, whereas that of normal coronary angiogram (n = 46) was 5.37 +/- 9.35 (p < 0.001).  The CCS and degree of stenosis were moderately correlated on patient-based or vessel-based analysis (r = 0.517, 0.521, respectively; both p < 0.001).  The authors concluded that CCS could reflect the degree of vessel stenosis to some extent, but CCS of zero could not rule out CAD.

Cademartiri et al (2010) compared the coronary artery calcium score (CACS) and CTCA for the assessment of non-obstructive/obstructive CAD in high-risk asymptomatic subjects.  A total of 213 consecutive asymptomatic subjects (113 males; mean age of 53.6 +/- 12.4 years) with more than 1 risk factor and an inconclusive or unfeasible non-invasive stress test result underwent CACS and CTCA in an out-patient setting.  All patients underwent conventional coronary angiography (CAG).  Data from CACS (threshold for positive image: Agatston score 1/100/1,000) and CTCA were compared with CAG regarding the degree of CAD (non-obstructive/obstructive; less than/greater than or = 50 % lumen reduction).  The mean calcium score was 151 +/- 403 and the prevalence of obstructive CAD was 17 % (8 % 1-vessel and 10 % 2-vessel disease).  Per-patient sensitivity, specificity, positive and negative predictive values of CACS were: 97 %, 75 %, 45 %, and 100 %, respectively (Agatston greater than or equal to 1); 73 %, 90 %, 60 %, and 94 %, respectively (Agatston greater than or equal to 100); 30 %, 98 %, 79 %, and 87 %, respectively (Agatston greater than or equal to 1,000).  Per-patient values for CTCA were 100 %, 98 %, 97 %, and 100 %, respectively (p < 0.05).  Computed tomography coronary angiography detected 65 % prevalence of all CAD (48 % non-obstructive), while CACS detected 37 % prevalence of all CAD (21 % non-obstructive) (p < 0.05).  The authors concluded that CACS proved inadequate for the detection of obstructive and non-obstructive CAD compared with CTCA.  Computed tomography coronary angiography has a high diagnostic accuracy for the detection of non-obstructive and obstructive CAD in high-risk asymptomatic patients with inconclusive or unfeasible stress test results.

Hadamitzky et al (2011) compared CCTA with calcium scoring and clinical risk scores for the ability to predict cardiac events.  Patients (n = 2,223) with suspected CAD undergoing CCTA were followed-up for a median of 28 months.  The end point was the occurrence of cardiac events (cardiac death, nonfatal myocardial infarction, unstable angina requiring hospitalization, and coronary re-vascularization later than 90 days after CCTA).  Patients with obstructive CAD had a significantly higher event rate (2.9 % per year; 95 % CI: 2.1 to 4.0) than those without obstructive CAD, having an event rate 0.3 % per year (95 % CI: 0.1 to 0.5; hazard ratio, 13.5; 95 % CI: 6.7 to 27.2; p < 0.001).  Coronary computed tomography angiography had significant incremental predictive value when compared with calcium scoring, both with scores assessing the degree of stenosis (p < 0.001) and with scores assessing the number of diseased coronary segments (p = 0.027).  The authors concluded that in patients with suspected CAD, CCTA not only detects coronary stenosis but also improves prediction of cardiac events over and above conventional risk scores and calcium scoring.

In a prospective population-based study, Kavousi et al (2012) evaluated if newer risk markers for CHD risk prediction and stratification improve Framingham risk score (FRS) predictions.  A total of 5,933 asymptomatic, community-dwelling participants (mean age of 69.1 years [SD, 8.5]) were included in this analysis.  Traditional CHD risk factors used in the FRS (age, sex, systolic blood pressure, treatment of hypertension, total and high-density lipoprotein cholesterol levels, smoking, and diabetes) and newer CHD risk factors (N-terminal fragment of prohormone B-type natriuretic peptide levels, von Willebrand factor antigen levels, fibrinogen levels, chronic kidney disease, leukocyte count, C-reactive protein levels, homocysteine levels, uric acid levels, CACS, carotid intima-media thickness, peripheral arterial disease, and pulse wave velocity).  Adding CACS to the FRS improved the accuracy of risk predictions (c-statistic increase, 0.05 [95 % CI: 0.02 to 0.06]; net re-classification index, 19.3 % overall [39.3 % in those at intermediate-risk, by FRS]).  Levels of N-terminal fragment of prohormone B-type natriuretic peptide also improved risk predictions but to a lesser extent (c-statistic increase, 0.02 [CI: 0.01 to 0.04]; net re-classification index, 7.6 % overall [33.0 % in those at intermediate-risk, by FRS]).  Improvements in predictions with other newer markers were marginal.  The authors concluded that among 12 CHD risk markers, improvements in FRS predictions were most statistically and clinically significant with the addition of CACS.  Moreover, they stated that further investigation is needed to assess whether risk refinements using CACS lead to a meaningful change in clinical outcome.

Cho and colleagues (2012) stated that the predictive value of CCTA in subjects without chest pain syndrome (CPS) has not been established.  These researchers investigated the prognostic value of CAD detection by CCTA and determined the incremental risk stratification benefit of CCTA findings compared with clinical risk factor scoring and CACS for individuals without CPS.  An open-label, 12-center, 6-country observational registry of 27,125 consecutive patients undergoing CCTA and CACS was queried, and 7,590 individuals without CPS or history of CAD met the inclusion criteria.  All-cause mortality and the composite of all-cause mortality and non-fatal myocardial infarction were measured.  During a median follow-up of 24 months (interquartile range, 18 to 35 months), all-cause mortality occurred in 136 individuals.  After risk adjustment, compared with individuals without evidence of CAD by CCTA, individuals with obstructive 2- and 3-vessel disease or left main coronary artery disease experienced higher rates of death and composite outcome (p < 0.05 for both).  Both CACS and CCTA significantly improved the performance of standard risk factor prediction models for all-cause mortality and the composite outcome (likelihood ratio p < 0.05 for all), but the incremental discriminatory value associated with their inclusion was more pronounced for the composite outcome and for CACS (C statistic for model with risk factors only was 0.71; for risk factors plus CACS, 0.75; for risk factors plus CACS plus CCTA, 0.77).  The net re-classification improvement resulting from the addition of CCTA to a model based on standard risk factors and CACS was negligible.  The authors concluded that although the prognosis for individuals without CPS is stratified by CCTA, the additional risk-predictive advantage by CCTA is not clinically meaningful compared with a risk model based on CACS.  Therefore, at present, the application of CCTA for risk assessment of individuals without CPS should not be justified.

The American College of Radiology Expert Panel on Cardiac Imaging’s clinical guideline on “Chronic chest pain - low to intermediate probability of coronary artery disease” (Woodard et al, 2012) rendered a “3” rating for CT coronary calcium (a “3” rating denotes the procedure is usually not appropriate).

Dedic et al (2016) noted that it is uncertain whether a diagnostic strategy supplemented by early CCTA is superior to contemporary standard optimal care (SOC) encompassing high-sensitivity troponin assays (hs-troponins) for patients suspected of acute coronary syndrome (ACS) in the emergency department (ED).  In a prospective, open-label, multi-center, randomized trial, these researchers examined if a diagnostic strategy supplemented by early CCTA improves clinical effectiveness compared with contemporary SOC.  They enrolled patients presenting with symptoms suggestive of an ACS at the ED of 5 community and 2 university hospitals in the Netherlands.  Exclusion criteria included the need for urgent cardiac catheterization and history of ACS or coronary re-vascularization.  The primary end-point was the number of patients identified with significant CAD requiring re-vascularization within 30 days.  The study population consisted of 500 patients, of whom 236 (47 %) were women (mean age of 54 ± 10 years).  There was no difference in the primary end-point (22 [9 %] patients underwent coronary re-vascularization within 30 days in the CCTA group and 17 [7 %] in the SOC group [p = 0.40]).  Discharge from the ED was not more frequent after CCTA (65 % versus 59 %, p = 0.16), and length of stay was similar (6.3 hours in both groups; p = 0.80).  The CCTA group had lower direct medical costs (€337 versus €511, p < 0.01) and less outpatient testing after the index ED visit (10 [4 %] versus 26 [10 %], p < 0.01).  There was no difference in incidence of undetected ACS.  The authors concluded that CCTA, applied early in the work-up of suspected ACS, is safe and associated with less out-patient testing and lower costs.  However, they stated that in the era of hs-troponins, CCTA did not identify more patients with significant CAD requiring coronary re-vascularization, shorten hospital stay, or allow for more direct discharge from the ED.

Calcium scores greater than 1000 have been taken as a relative contraindication for CCTA (Maurya et al, 2016).

A calcium score of 1000 is often used as the cutoff value above which a CCTA will not be diagnostic (Lin, 2017).

Coronary CT Angiography for Assessment of Coronary Atherosclerosis in Asymptomatic Diabetics

Muhlestein and Moreno (2016) noted that it is well-known that there is a very high risk of cardiovascular complications among diabetic patients.  In spite of all efforts at aggressive control of diabetes and its complications, the incidence of cardiovascular morbidity and mortality remains high, including in patients with no prior symptoms, underscoring a possible advantage for appropriate screening of asymptomatic patients for the presence of obstructive CAD.  These investigators reviewed the results of studies designed to evaluate a possible role of CCTA in the screening of asymptomatic diabetic patients for possible obstructive CAD.  The review of current literature indicated that there is still no method of CAD screening identified that has been shown to reduce the cardiovascular risk of asymptomatic diabetic patients.  Thus, the use and value of screening for CAD in asymptomatic diabetic patients remains controversial.  CCTA screening has shown promise and has been demonstrated to predict future risk, but as yet has not demonstrated improvement in the outcomes of these high-risk patients.  At the present state of knowledge, aggressive risk factor reduction appeared to be the most important primary prevention strategy for all asymptomatic high-risk diabetic patients.  However, there remains a great need for better and more sensitive and specific screening methods, as well as more effective treatments that may allow clinicians to more accurately target diabetic patients who really are at high risk.  The authors concluded that further large randomized and well-controlled clinical trials are needed to examine if screening for CAD could reduce cardiovascular event rates in patients with diabetes.

Guaricci and colleagues (2018) stated that the prognostic impact of diabetes mellitus (DM) on cardiovascular outcomes is well known.  As a consequence of previous studies showing the high incidence of CAD in diabetic patients and the relatively poor outcome compared to non-diabetic populations, DM is considered as CAD equivalent, which means that diabetic patients are labeled as asymptomatic individuals at high cardiovascular risk.  Lessons learned from the analysis of prognostic studies over the past decade have challenged this dogma and now support the idea that diabetic population is not uniformly distributed in the highest risk box.  Detecting CAD in asymptomatic high risk individuals is controversial and, what is more, in patients with diabetes is challenging, and that is why the reliability of traditional cardiac stress tests for detecting myocardial ischemia is limited.  The authors stated that CCTA represents an emerging non-invasive technique able to explore the atherosclerotic involvement of the coronary arteries and, thus, to distinguish different risk categories tailoring this evaluation on each patient.

Lee and associates (2018) noted that it is well-known that diabetic patients have a high risk of cardiovascular events, and although there has been a tremendous effort to reduce these cardiovascular risks, the incidence of cardiovascular morbidity and mortality in diabetic patients remains high.  Thus, the early detection of CAD is necessary in those diabetic patients who are at risk of cardiovascular events.  Significant medical and radiological advancements, including CCTA, mean that it is now possible to examine the characteristics of plaques, instead of solely evaluating the calcium level of the coronary artery.  Recently, several studies reported that the prevalence of subclinical coronary atherosclerosis (SCA) is higher than expected, and this could impact on CAD progression in asymptomatic diabetic patients.  In addition, several reports suggested the potential benefit of using CCTA for screening for SCA in asymptomatic diabetic patients, which might dramatically decrease the incidence of cardiovascular events.  For these reasons, the medical interest in SCA in diabetic patients is increasing.  The authors concluded that the prevalence of SCA in diabetic patients is high, and the progression of coronary atherosclerosis leads to the onset of future CV events and is associated with a poor prognosis.  Moreover, they stated that although CCTA screening has not yet been demonstrated as improving the outcomes of asymptomatic diabetic patients, it has been shown to be beneficial in predicting future risk, and is promising for screening with an additional technique.

Furthermore, an UpToDate review on “Screening for coronary heart disease in patients with diabetes mellitus” (Bax et al, 2018) states that “In the 2018 Standards of Medical Care in Diabetes, the American Diabetes Association does not recommend routine screening for CHD in asymptomatic patients with diabetes, as outcomes are not improved as long as cardiovascular risk factors are treated.  However, in the 2013 ESC/EASD Guidelines on diabetes, pre-diabetes, and cardiovascular disease (CVD), the writing group concludes that in asymptomatic patients routine screening is controversial and still under debate.  In addition, the guidelines highlight the need for better definition of the characteristics of the patients who should be screened for CHD, stating that screening for silent myocardial ischemia may be considered in selected high-risk patients with diabetes, such as patients with peripheral artery disease or high coronary artery calcium (CAC) score or with proteinuria”.

The Cleerly Coronary Report

Cleerly Labs is a web-based software application that is intended to be used by trained medical professionals as an interactive tool for viewing and analyzing cardiac computed tomography (CT) data for determining the presence and extent of coronary plaques (i.e., atherosclerosis) and stenosis in patients who underwent coronary computed tomography angiography (CCTA) for evaluation of coronary artery disease (CAD) or suspected CAD.  This software is a post-processing tool that aids in determining treatment paths for patients suspected to have CAD.  The software provides tools for the measurement and visualization of coronary arteries.  The software is not intended to replace the skill and judgment of a qualified medical practitioner and should only be used by people who have been appropriately trained in the software’s functions, capabilities, and limitations.  Users should be aware that certain views make use of interpolated data.  This is data that is created by the software based on the original data set.  Interpolated data may give the appearance of healthy tissue in situations where pathology that is near or smaller than the scanning resolution may be present.

In Cleerly Labs, users can edit the lumen and vessel walls of the suggested segmentation, and demarcate stenosis and stents to more efficiently perform coronary analysis.  Users are provided with navigation and editing/visualization tools to aid in image analysis.  Plaque (i.e., atherosclerosis) and stenosis measurements are outputted based on the fully user-editable segmentation of the coronary artery.  The user is also provided with the ability to indicate coronary anatomical findings.  Following the completion of study analysis, an interactive Coronary Report is generated.  The Coronary Report summarizes the analysis data from Cleerly Labs by reporting them as findings on atherosclerosis and stenosis, which may be used as supporting data in the evaluation of CAD.  Components of the Coronary Report include data visualization and reporting features.

There is a clinical trial entitled "Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry (CONFIRM2)" that is sponsored by Cleerly, Inc. (Last updated February 27, 2020). This trial is designed to examine associations between CCTA imaging findings and clinical presentation and their ability to predict mortality and major adverse cardiac events in patients with chronic CAD.

Cleerly Labs was cleared by the Food and Drug Administration (FDA) via the 510(k) process on November 5, 2019; and Cleerly Labs v2.0 was cleared on October 2, 2020.

Choi et al (2021) stated that atherosclerosis evaluation by CCTA is promising for CAD risk stratification; however, it is time consuming and requires high expertise.  Artificial Intelligence (AI) applied to CCTA for comprehensive CAD assessment may overcome these limitations.  In a multi-center study, these researchers hypothesized AI-aided analysis would allow for rapid, accurate evaluation of vessel morphology and stenosis.  This trial included 232 patients undergoing CCTA.  Studies were analyzed by FDA-cleared software service that carries out AI-driven coronary artery segmentation and labeling, lumen and vessel wall determination, plaque quantification and characterization with comparison to ground truth of consensus by 3 Level 3 (L3) expert readers.  CCTAs were analyzed for: % maximal diameter stenosis, plaque volume and composition, presence of high-risk plaque and Coronary Artery Disease Reporting & Data System (CAD-RADS) category.  AI performance was excellent for accuracy, sensitivity, specificity, PPV and NPV as follows: greater than 70 % stenosis: 99.7 %, 90.9 %, 99.8 %, 93.3 %, and 99. 9%, respectively; greater than 50 % stenosis: 94.8 %, 80.0 %, 97.0 %, 80.0 %, and 97.0 %, respectively.  Bland-Altman plots depict agreement between expert reader and AI-determined maximal diameter stenosis for per-vessel (mean difference [MD] -0.8 %; 95 % confidence interval [CI]: 13.8 % to -15.3 %) and per-patient (MD -2.3 %; 95 % CI: 15.8 % to -20.4 %).  L3 and AI agreed within 1 CAD-RADS category in 228/232 (98.3 %) examinations per-patient and 923/924 (99.9 %) vessels on a per-vessel basis.  There was a wide range of atherosclerosis in the coronary artery territories assessed by AI when stratified by CAD-RADS distribution.  The authors concluded that AI-aided approach to CCTA interpretation determined coronary stenosis and CAD-RADS category in close agreement with consensus of L3 expert readers.  There was a wide range of atherosclerosis identified via AI.  Moreover, these researchers stated that the use of this FDA-cleared device as a clinical decision support tool in combination with enhanced CCTA education may improve the reproducibility of CCTA interpretation in various clinical and investigational settings.  They noted that the findings of this study provided an important foundational platform for future research in AI-guided atherosclerosis evaluation across a wide spectrum of disease and patients.

The authors stated that this study had several drawbacks.  Ground truth in this trial was the consensus of 3 L3 readers without validation to invasive approaches (e.g., IVUS or optical coherence tomography [OCT]).  Only 15 % of studied population of consecutive chest pain patients had anatomically obstructive stenosis.  Ongoing study will examine AI stenosis to patients referred to invasive angiography.  A guideline-based reference standard for CCTA atherosclerosis quantification was not available to include in this study and is currently under development by medical societies.  Furthermore, an ongoing multi-center study is examining the diagnostic performance of this AI-enabled approach to quantitative coronary angiography (CA), IVUS and OCT; examination of those results will further establish the role of AI in CCTA imaging.  AI was not carried out in CCTA studies of poor image quality deemed uninterpretable by L3 readers.  The prognostic significance of atherosclerotic plaque quantified by AI is unknown.  Established Hounsfield unit (HU) thresholds for plaque characterization were employed without adjustment, in the absence of a standardized methodology, for high luminal contrast enhancement.

Lin et al (2021) noted that the last 10 years has seen a rapid proliferation of AI developments for cardiovascular CT.  These algorithms aim to increase efficiency, objectivity, and performance in clinical tasks such as image quality improvement, structure segmentation, quantitative measurements, and outcome prediction.  By doing so, AI has the potential to streamline clinical workflow, increase interpretative speed and accuracy, and inform subsequent clinical pathways.  Moreover, these researchers stated that in order for AI techniques to become a reality in clinical practice, several challenges need to be overcome.  First, large-scale, labeled datasets with high quality CT image data are needed for training and testing new algorithms.  In healthcare, there are often legal barriers around data sharing, with only a limited number of datasets being made publicly available.  Encouragingly, international collaborative cardiac imaging databases such as the Cardiac Atlas Project have managed to clear many of the ethical, legal, and organizational hurdles to data sharing and distribution.  Beyond data availability, standardization of CT acquisition protocols and quantitative parameters is needed when aggregating data from different centers as input for an AI model.  Recent initiatives such as the Radiological Society of North America’s Quantitative Imaging Biomarkers Alliance have been established to improve the standardization and performance of quantitative imaging metrics.  Second, bias can arise in AI algorithms over time via learning from disparities in patient demographics or healthcare systems; thus, algorithms need to be monitored by institutions and regulatory authorities for biases in predictive performance and also for biases in the way their predictions are used in clinical care.  Furthermore, machine learning (ML) models trained and tested at a single center may not be generalizable to different cohorts, underscoring the importance of developing and validating AI models in multi-center, multi-vendor studies with diverse patient demographics.  Formal external validation of these models in independent datasets not used for training should also be performed.  Third, AI algorithms can often be viewed as “black boxes” that autonomously learn and make decisions.  An understanding of why decisions are made by an algorithm, the rigor of evaluation, and when and why errors occur should all be considered before any algorithm is adopted in clinical practice.  Recently, explainable AI models have emerged that have the ability to provide some explanation or justification for their decision-making.  Such algorithms may highlight a recognizable variable or combination of variables that contributed to its decision (e.g., an increase in age or number of calcified coronary lesions).  Fourth, there is the legal consideration of clinical clearance for AI-powered software applications.  Current diagnostic software tools are categorized by the FDA as Class II (medium risk) medical devices; to be concordant with regulatory requirements, the final results often need to be validated by the user.  If fully automated systems were to be ultimately used for end-to-end diagnosis or risk stratification, they would most likely be categorized as Class III (high risk) devices, which are held to much higher performance and validation standards.

In a post-hoc analysis, Jonas et al (2021) examined the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT).  This study included data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) Trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA).  In this study, a blinded core laboratory analyzing quantitative coronary angiography images classified lesions as obstructive (greater than or equal to 50 %) or non-obstructive (less than 50 %) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging.  Plaque measurements were normalized for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components.  Data were subsequently stratified by age less than 65 and 65 years or older.  The cohort was 64.4 ± 10.2 years and 29 % women.  Overall, patients older than 65 years had more PV and CP than patients less than 65 years of age.  On a lesion level, patients older than 65 years had more CP than younger patients in both obstructive (29.2 mm3 versus 48.2 mm3; p < 0.04) and non-obstructive lesions (22.1 mm3 versus 49.4 mm3; p < 0.004) while younger patients had more %PAV (LD-NCP) (1.5 % versus 0.7 %; p < 0.038).  Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions.  There were no differences observed between lesion types in older patients.  The authors concluded that AI-QCT identified a unique APC signature that differed by age and degree of stenosis and provided a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention, and treatment.

The authors stated that this study had several drawbacks.  First, while patients were enrolled prospectively from a large, multi-center clinical trial with evaluation by a blinded core laboratory, the evaluation was post-hoc and not powered to detect differences in plaque types.  About 1/3 of the patients had demonstrable atherosclerosis despite the absence of symptoms, which may not be fully representative of a stable chest pain population, but also represent a limitation in current guidelines for testing.  Second, the CCTAs evaluated reflect a single point in time rather than a longitudinal period; thus, limiting knowledge of plaque progression as reflected at various ages.  Although coronary lesions are dynamic, changes in PV composition as a function of worsening stenosis severity was not evaluated. Third, these researchers used angiographic coronary stenosis as a marker of CAD severity.  While large-scale prognosis of AI-QCT defined quantitative plaque composition has not yet been carried out, it remains the subject of future study in the upcoming CONFIRM2 study (Coronary CT Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry).

Lipkin et al (2022) noted that deep learning frameworks have been used for interpretation of CCTA carried out for evaluation of CAD.  In a retrospective study, these researchers compared the diagnostic performance of myocardial perfusion imaging (MPI) and CCTA with AI quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on CA and examined the down-stream impact of including CCTA with AI-QCT in diagnostic algorithms.  This trial entailed a post-hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) Trial.  The study included 301 patients (88 women and 213 men; mean age of 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for non-emergent CA.  Patients underwent CCTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and FFR.  CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination.  Diagnostic performance was evaluated; and diagnostic algorithms were compared.  Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (50 % or greater) stenosis in 54 % of patients, including severe (70 % or greater) stenosis in 20 %.  Among 199 patients with ischemia on MPI, AI-QCT identified non-obstructive (1 % to 49 %) stenosis in 23 %.  AI-QCT had significantly higher AUC (all p < 0.001) than MPI for predicting 50 % or greater stenosis by QCA (0.88 versus 0.66), 70 % or greater stenosis by QCA (0.92 versus 0.81), and FFR of less than 0.80 (0.90 versus 0.71).  An AI-QCT result of 50 % or greater stenosis and ischemia on stress MPI had sensitivity of 95 % versus 74 % and specificity of 63 % versus 43 % for detecting 50 % or greater stenosis by QCA measurement.  Compared with performing MPI in all patients and those showing ischemia undergoing CA, a scenario of performing CCTA with AI-QCT in all patients and those showing 70 % or greater stenosis undergoing CA would reduce invasive CA utilization by 39 %; a scenario of performing MPI in all patients and those showing ischemia undergoing CCTA with AI-QCT and those with 70 % or greater stenosis on AI-QCT undergoing CA would reduce invasive CA utilization by 49 %.  The authors concluded that CCTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD.  These researchers stated that these findings could help inform future approaches to the diagnostic work-up of patients with suspected CAD.

The authors stated that this study had several drawbacks.  First, It was a retrospective post-hoc analysis of data from a prospective trial; thus, the findings may have limited generalizability given that 71 % of the patients were men.  Second, the various sequential testing models were examined in a simulated fashion, and actual clinical outcomes resulting from these models are unknown.  Third, the models assumed that all positive diagnostic tests results would be referred to invasive CA and that invasive CA would have perfect sensitivity and specificity.  The models further assumed that patients would not undergo tests outside of the simulated pathway; however, real-world practice is expected to differ from these assumptions.  Fourth, AI-QCT was not compared with CCTA without AI assistance given previous studies validating AI-QCT in such a comparison.

Muscogiuri et al (2022) noted that technical advances in AI in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time needed to generate a report.  In CCTA the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia.  In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and especially on the segmentation of cardiac chambers during late gadolinium enhancement.  In echocardiography, the use of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities.  The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach.  These investigators stated that despite the methods in which the use of AI in cardiac imaging can be extremely helpful, several limitations need to be addressed.  In particular, it is not negligible that all these algorithms need to be approved by the FDA or the European Community before their use in clinical practice.  European Commission for use of AI in medicine suggested some rules regarding requirements on data collecting, analysis and transparency.  In addition, it is important to examine carefully the data collected considering that development of an AI algorithm need a heterogeneous population that should not be unbalanced in terms of ethnicity or gender.  Furthermore, it is important to consider that although many algorithms are available also as open source, the development of a robust algorithm needs training and validation on a large dataset; thus, vendors need a large amount of data in order to develop a reliable tool; the latter can be considered a limitation in terms of AI algorithms development.  These researchers stated that despite the bright future of cardiac imaging linked to the use of AI, it is important to consider the clinical safety of these algorithms should they be approved for use in clinical practice.

Cho et al (2022) noted that studies have shown that quantitative evaluation of coronary artery plaque on CCTA could identify patients at risk of cardiac events.  Recent demonstration of AI-assisted CCTA showed that it allows for evaluation of CAD and plaque characteristics.  Based on publications to-date, these investigators were the 1st group to carry out AI-augmented CCTA serial analysis of changes in coronary plaque characteristics over 13 years.  They examined if AI-assisted CCTA could accurately evaluate changes in coronary plaque progression, which has potential clinical prognostic value in CAD management.  This case entailed a 51-year-old man with hypertension, hyperlipidemia and family history of myocardial infarction (MI), who underwent CCTA examinations for anginal symptom evaluation and CAD assessment.  A total of 5 CCTAs were carried out between 2008 and 2021.  Quantitative atherosclerosis plaque characterization (APC) using an AI platform (Cleerly), was carried out to evaluate CAD burden.  Total plaque volume (TPV) change-over-time demonstrated decreasing low-density non-calcified plaque (LD-NCP) with increasing overall NCP and calcified-plaque (CP).  Examination of individual segments revealed a proximal-LAD lesion with decreasing NCP over-time and increasing CP.  In contrast, although the D2/D1/ramus lesions showed increasing stenosis, CP, and total plaque, there were no significant differences in NCP over-time, with stable NCP and increased CP.  In addition, these researchers consistently visualized small plaques, which typically readers may interpret as false positives due to artifacts.  However, in this case, they re-appeared each study in the same locations, generally progressing in size and demonstrating expected plaque transformation over-time.  The authors conducted the 1st AI-augmented CCTA based serial analysis of changes in coronary plaque characteristics over 13 years.  They were able to consistently evaluate progression of plaque volumes, stenosis, and APCs with this novel methodology.  These investigators found a significant increase in TPV composed of decreasing LD-NCP, and increasing NCP and CP, with variations in the evolution of APCs between vessels.  They noted that although the significance of evolving APCs needs to be further investigated, this case demonstrated AI-based CCTA analysis could serve as valuable clinical tool to accurately define unique CAD characteristics over time.  Moreover, these researchers stated that prospective clinical trials are needed to examine if quantification of APCs would provide prognostic capabilities to improve clinical care.

Liao et al (2022) stated that CHD is the leading cause of mortality worldwide; thus, early diagnosis and treatment of CHD are critical.  To-date, CCTA has been the primary choice for CHD screening and diagnosis; however, it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis.  In recent years, AI has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD.  The authors stated that AI has been employed to automate the CCTA workflow, such as assessing coronary artery calcium, segmenting automatically, identifying plaques, and calculating the severity of stenosis.  They concluded that AI would play a greater role in the accurate assessment and prognosis analysis of CHD; however, before AI is widely used in clinical practice, there must be adequate measures performed to ensure data security, and data standardization.

Gudigar et al (2022) noted that CAD is a major cause of morbidity and mortality worldwide; IVUS- and intravascular OCT (IV-OCT) performed during invasive CA are reference standards for characterizing the atherosclerotic plaque.  Fine image spatial resolution attainable with contemporary CCTA has enabled non-invasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events.  Manual interpretation of IVUS, IV-OCT and CCTA images demands scarce physician expertise and high time cost.  This has motivated recent research into and development of AI-assisted methods for image processing, feature extraction, plaque identification and characterization.  These investigators performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques.  A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods – machine learning (ML) versus deep learning (DL) -- and performance metrics.  Trends in AI-assisted plaque characterization were detailed and prospective research challenges were discussed.  The authors proposed future directions for the development of accurate and efficient computer aided diagnosis systems to characterize plaque non-invasively using CCTA.  They noted that experimental results showed that AI algorithms using M-L and DL-based methods have merits for identifying plaques; and could be used as a valuable resource in the medical decision-making process.  In the future, these AI methods can be employed to achieve better results by addressing future challenges and implementing the AI models in real clinical trials.

The authors stated that this review had several drawbacks.  First, this review was performed based on manuscripts written in English.  Other language manuscripts were not included during the review process.  Second, the review process included a plaque grading system using various modalities, and analysis of various AI algorithms to develop computer aided diagnosis for plaque categorization.  However, review on grading during plaque deposition and after treatment was not given substantial consideration.  Third, the specific reasons to select the algorithms based on AI were not mentioned.  It was also unclear whether the proposed computer aided diagnosis could improve the survival of patients.

Jonas et al (2022a) noted that the difference between expert level (L3) reader and AI performance for quantifying coronary plaque and plaque components is unknown.  In a retrospective study, these researchers examined the inter-observer variability among expert readers for quantifying the volume of coronary plaque and plaque components on CCTA using an artificial intelligence enabled quantitative CCTA analysis software as a reference (AI-QCT).  This study used CCTA imaging obtained from 232 patients enrolled in the CLARIFY (CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY) Trial.  Readers quantified overall plaque volume and the % breakdown of noncalcified plaque (NCP) and calcified plaque (CP) on a per vessel basis.  Readers categorized high risk plaque (HRP) based on the presence of low-attenuation-noncalcified plaque (LA-NCP) and positive remodeling (PR; 1.10 or higher).  All CCTAs were analyzed by an FDA-cleared software service that performs AI-driven plaque characterization and quantification (AI-QCT) for comparison to L3 readers.  Reader generated analyses were compared among readers and to AI-QCT generated analyses.  When evaluating plaque volume on a per vessel basis, expert readers achieved moderate-to-high inter-observer consistency with an intra-class correlation coefficient (ICC) of 0.78 for a single reader score and 0.91 for mean scores.  There was a moderate trend between readers 1, 2, and 3 and AI with spearman coefficients of 0.70, 0.68 and 0.74, respectively.  There was high discordance between readers and AI plaque component analyses.  When quantifying %NCP versus %CP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.23, 0.34 and 0.24, respectively, compared to AI with a spearman coefficient of 0.38, 0.51, and 0.60, respectively.  The ICC among readers for plaque composition assessment was 0.68.  With respect to HRP, readers 1, 2, and 3 achieved a weighted kappa coefficient of 0.22, 0.26, and 0.17, respectively, and a spearman coefficient of 0.36, 0.35, and 0.44, respectively.  The authors concluded that expert readers performed moderately well quantifying total plaque volumes with high consistency; however, there was both significant inter-observer variability and high discordance with AI-QCT when quantifying plaque composition.

The authors stated that this study had several drawbacks.  First, this study was a post-hoc analysis of the CLARIFY Trial and, while it was unexpected that significant bias would be introduced in a retrospective evaluation leveraging blinded core laboratory readers, it nevertheless emphasized the absence of a prospective clinical trial that could be performed in the future.  Second, in this study, established Hounsfield unit (HU) thresholds for plaque characterization were employed without adjustment in the absence of a standardized methodology for high luminal contrast enhancement.  Ultimately, AI was not carried out on CCTAs of poor image quality deemed uninterpretable by expert readers, further emphasizing limitations by human readers versus AI.  Third, while the prognostic significance of atherosclerotic plaque quantified by AI is still unknown, AI's high performance and the expanding knowledge surrounding the prognostic value of adverse plaque substantiate that further investigation is needed.

Jonas et al (2022b) noted that CCTA is a non-invasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD).  The use of AI-QCT analysis software enhances the diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters.  These researchers examined if CCTA scanning, scan preparation, contrast, and patient based parameters would influence the diagnostic performance of an AI-based analysis software for identifying coronary lesions with 50 % or greater stenosis.  CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71 % men) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination.  The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of 50 % or greater stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single versus dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI).  Within the patient cohort, 13 % demonstrated 50 % or greater stenosis in 3 vessel territories, 21 % in 2 vessel territories, 35 % in 1 vessel territory; while 32 % had less than 50 % stenosis in all vessel territories evaluated by QCA.  Average AI analysis time was 10.3 ± 2.7 mins.  On a per vessel basis, there were significant differences only in sensitivity for 50 % or greater stenosis based on contrast type (iso-osmolar 70.0 % versus non-iso-osmolar 92.1 % p = 0.0345) and iodine concentration (less than 350 mg/ml 70.0 %, 350 to 369 mg/ml 90.0 %, 370 to 400 mg/ml 90.0 %, greater than 400 mg/ml 95.2 %; p = 0.0287) in the context of low injection flow rates.  On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters.  The authors concluded that the diagnostic performance of AI-QCT analysis software for detecting moderate-to-high grade stenosis were unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast, and patient variables.

The authors stated that this study had several drawbacks.  First, this study was a post-hoc analysis of the CREDENCE Trial and, while it was unexpected that significant bias would be introduced in a retrospective evaluation leveraging blinded core laboratory readers, it nevertheless emphasized the absence of a prospective clinical trial that should be performed in the future.  Second, this study examined the AI-based evaluation for measures of stenosis severity, rather than for plaque volume, composition, vascular remodeling, and other important CAD metrics.  Third, ground truth in this study was core-lab interpreted QCA for any stenosis of greater than 30 %, in keeping with previous multi-center studies employing QCA.  Because of this, these investigators were unable to report the diagnostic performance of the AI-based evaluation for the presence of stenosis lower than this range.  Stenoses in this range have been historically considered inconsequential by QCA, although newer data suggest a prognostic significance to these “mild” lesions that may nevertheless possess high-risk atherosclerotic characteristics.

Cho et al (2022) noted that adverse cardiovascular events are a significant cause of mortality in end-stage renal disease (ESRD) patients.  High-risk plaque anatomy may be a significant contributor; however, their atherosclerotic phenotypes have not been described.  These investigators defined APC in dialysis patients using AI-augmented CCTA.  They retrospectively analyzed ESRD patients referred for CCTA using an FDA-approved AI-augmented-CCTA program (Cleerly).  Coronary lesions were evaluated for APCs by CCTA.  APCs included PAV, LD-NCP, NCP, CP, length, and HRP, defined by LD-NCP and positive arterial remodeling of greater than 1.10 (PR).  A total of 79 ESRD patients were enrolled, mean age of 65.3 years, 32.9 % women.  Disease distribution was non-obstructive (65.8 %), 1-vessel disease (21.5 %), 2-vessel disease (7.6 %), and 3-vessel disease (5.1 %).  Mean total plaque volume (TPV) was 810.0 mm3, LD-NCP 16.8 mm3, NCP 403.1 mm3, and CP 390.1 mm3.  HRP was present in 81.0 % patients.  Patients with at least 1 greater than 50 % stenosis, or obstructive lesions, had significantly higher TPV, LD-NCP, NCP, and CP.  Patients old than 65 years had more CP and higher PAV.  The authors concluded that the findings of this study provided novel insight into ESRD plaque phenotypes and showed that AI-augmented CCTA analysis was feasible for CAD characterization despite severe calcification.  These researchers showed elevated plaque burden and stenosis caused by predominantly non-calcified-plaque.  Furthermore, the quantity of calcified-plaques increased with age, with men exhibiting increased number of 2-feature plaques and higher plaque volumes.  These investigators stated that AI augmented CCTA analysis of APCs may be a promising metric for cardiac risk stratification and warrants further prospective investigation.

The authors stated that that this study had several drawbacks.  First, the study population was sampled retrospectively and subject to the limitations inherent in all such study design, namely, a high risk of referral, spectrum, and ascertainment bias.  Second, for the accuracy of AI CCTA in CAD analysis, direct comparison between invasive coronary angiography, automated AI-enabled web-based software platform, and standard interpretation was not performed; however, direct coronary angiogram to CCTA correlation has been carried out to show correlation/accuracy previously in non-AI CCTA studies, and subsequently AI CCTA to human CCTA, which has been performed by this research group and co-authors, and given that there were patients in these studies with higher plaque burden (similar to the authors’ patient population), these results should be generalizable, but indeed that further studies in this specific ESRD dialysis population are needed to confirm this with certainty.  Third, and most importantly, the prognostic implications and clinical relevance of CCTA detection of coronary atherosclerosis in this population are yet to be determined prospectively.

Min et al (2022) stated that atherosclerotic plaque characterization by CCTA enables quantification of CAD burden and type, which has been shown as the strongest discriminant of future risk of major adverse cardiovascular events (MACE).  To-date, there are no clinically useful thresholds to assist with understanding a patient's disease burden and guide diagnosis and management, as there exists with coronary artery calcium (CAC) scoring.  These investigators attempted to establish clinically relevant plaque stages and thresholds based on evidence from ICA and fractional flow reserve (FFR) data.  A total of 303 patients underwent CCTA before ICA and FFR for an AHA/ACC clinical indication; QCT was carried out for TPV (mm3) and PAV (%).  These researchers segmented atherosclerosis by composition for LD-NCP, NCP, and CP.  ICAs were evaluated by QCA for all coronary segments for % diameter stenosis.  The relationship of atherosclerotic plaque burden and composition by QCT to ICA stenosis extent and severity by QCA and presence of ischemia by FFR was assessed to develop 4 distinct disease stages.  The mean age of the patients was 64.4 +/- 10.2 years; 71 % men.  At the 50 % QCA stenosis threshold, QCT revealed a mean PAV of 9.7 (+/- 8.2) % and TPV of 436 (+/- 444.9) mm3 for those with non-obstructive CAD; PAV of 11.7 (+/-8.0) % and TPV of 549.3 (+/- 408.3) mm3 for 1 vessel disease (1VD), PAV of 17.8 (+/- 9.8)% and TPV of 838.9 (+/-550.7) mm3 for 2VD, and PAV of 19.2 (+/-8.2) % and TPV of 799.9 (+/- 357.4) mm3 for 3VD/left main disease (LMD).  Non-ischemic patients (FFR greater than 0.8) had a mean PAV of 9.2 (+/- 7.3) % and TPV of 422.9 (+/- 387.9 mm3) while patients with at least 1 vessel ischemia (FFR 0.8) had a PAV of 15.2 (+/- 9.5) % and TPV of 694.6 (+/- 485.1).  Definition of plaque stage thresholds of 0, 250, 750 mm3 and 0, 5, and 15 % PAV resulted in 4 clinically distinct stages in which patients with no, non-obstructive, single VD and multi-vessel disease were optimally distributed.  The authors concluded that atherosclerotic plaque burden by QCT was related to stenosis severity and extent as well as ischemia.  These investigators proposed staging of CAD atherosclerotic plaque burden using the following definitions: Stage 0 (normal, 0 % PAV, 0 mm3 TPV), Stage 1 (mild, greater than 0 to 5 % PAV or greater than 0 to 250 mm3 TPV), Stage 2 (moderate, greater than 5 to 15 % PAV or greater than 250 to 750 mm3 TPV), and Stage 3 (severe, greater than 15 % PAV or greater than 750 mm3 TPV).  These researchers stated that these findings may better inform future studies regarding the precise relationship among atherosclerosis, vascular morphology, and adverse cardiovascular events.

The authors stated that this study had several drawbacks.  First, in the study eligibility criteria, patients were enrolled only after a clinical referral for ICA was made based upon an AHA/ACC class II indications; most patients manifested symptoms suspicious of CAD or abnormal stress test findings before enrollment; therefore, widespread applicability to asymptomatic patients warrants research.  Second, while these researchers stratified their study findings by age and gender, they did not incorporate age or gender considerations into the plaque stages.  The interaction of age and gender together may influence atherosclerosis and stenosis findings; and age- and sex-dependent stages may be both more clinically useful and prognostic.  Future studies should examine this issue.  Third, these investigators chose to define the disease stages based upon angiographic stenosis and FFR; however, these categories might be better defined in the context of events; thus, these stage thresholds should be considered preliminary or pilot data and future investigations based on events and not angiographic stenosis or FFR may lead to modifications of the stage thresholds.  Fourth, the cohort was predominately (71 %) male and these investigators did not account for prior statin and other medication usage.  Fifth, these researchers examined clinical risk factors but did not uniformly account for risk factor duration, severity, and treatment.  Each of these factors may have influenced the phenotypic appearance of atherosclerosis in any individual; ongoing studies are examining how risk factor duration, severity and treatment would influence the natural history of atherosclerosis and stenosis.  The authors elected to use a 50 % angiographic threshold to define stenosis, this could have also been 70 %.  Finally, these researchers leveraged a latest-generation AI-enabled software platform that allows for highly accurate measures of CAD, as evidenced in previous multi-center clinical trials compared expert level III readers, QCA, FFR and IVUS, additional validation trials are underway.

Griffin et al (2023) stated that clinical reads of CCTA, especially by less experienced readers, may result in over-estimation of CAD stenosis severity compared with expert readers.  AI-based solutions applied to CCTA may overcome these limitations.  In a retrospective, sub-study of the CREDENCE Trial, these investigators compared the performance for detection and grading of coronary stenoses using AI-QCT analyses to core lab-interpreted CCTA, core lab QCA, and invasive FFR.  Coronary CTA, FFR, and QCA data from 303 stable patients (age of 64 ± 10 years, 71 % men) from the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that conducts AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination.  Disease prevalence was high, with 32.0 %, 35.0 %, 21.0 %, and 13.0 % demonstrating 50 % or greater stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively.  Average AI-QCT analysis time was 10.3 ± 2.7 mins.  AI-QCT evaluation demonstrated per-patient sensitivity, specificity, PPV, NPV, and accuracy of 94 %, 68 %, 81 %, 90 %, and 84 %, respectively, for 50 % or greater stenosis, and of 94 %, 82 %, 69 %, 97 %, and 86 %, respectively, for detection of 70 % or greater stenosis.  There was high correlation between stenosis detected on AI-QCT evaluation versus QCA on a per-vessel and per-patient basis (intra-class correlation coefficient = 0.73 and 0.73, respectively; p < 0.001 for both).  False positive AI-QCT findings were noted in in 62 of 848 (7.3 %) vessels (stenosis of 70 % or greater by AI-QCT and QCA of less than 70 %); however, 41 (66.1 %) of these had an FFR of less than 0.8.  The authors concluded that in this analysis of the multi-national CREDENCE Trial, an AI-based evaluation showed high diagnostic performance for the identification, exclusion, discrimination, and correlation to a QCA reference standard.  These researchers stated that given the rapid turn-around time of this AI-QCT and its superior performance to previous CCTA core lab and site readers, this approach may augment clinical CCTA interpretation.

The authors stated that this study had several drawbacks.  This study was a post-hoc analysis of the CREDENCE Trial, and while it was unexpected that significant bias would be introduced in a retrospective evaluation leveraging blinded core lab readers, it nevertheless emphasized the absence of a prospective clinical trial that should be conducted in the future.  In addition, this study examined the AI-based evaluation for measures of stenosis severity, rather than for plaque volume, composition, vascular re-modeling, and other important CAD metrics.  This is currently being examined in the multi-center INVICTUS (A Retrospective and Prospective, Multicenter Registry of Coronary Computed Tomography Angiography, Intravenous Ultrasound and Optical Coherence Tomography to Compare Invasive and Non-invasive Imaging Modalities for the Determination of Severity, Volume and Type of Coronary Atherosclerosis) Trial, which is enrolling patients undergoing CCTA and IVUS, OCT, or near-field infrared spectroscopy, and these researchers plan to report these findings upon study completion.  Furthermore, the ground truth in this present study was core lab-interpreted QCA for any stenosis of greater than 30 %, in keeping with previous multi-center studies employing QCA.  Because of this, these investigators were unable to report the diagnostic performance of the AI-based evaluation for the presence of stenosis in this range.  Stenoses in this range have been historically considered inconsequential by QCA, although newer data suggest a prognostic significance to these “mild” lesions that may nevertheless possess high-risk atherosclerotic characteristics.  The INVICTUS trial will aid in addressing this limitation of the present study.

In an editorial commentary on the study by Griffin et al (2023), Nicol (2023) much of AI and ML technology is overhyped and, despite its potential, one must balance the strengths and weakness of both human interpretation and ML-derived algorithms.  Machines have great potential in pattern recognition, and produce highly reproducible results, often in a fraction of the time it takes a hu man; whereas humans can adapt, extrapolate, and make high-level semantic decisions based on knowledge and experience.  In coronary CTA analysis they can acknowledge the step artifact, adapt their interpretation in the knowledge that calcified lesions usually look worse than they appear on ICA, and can contextualize findings considering a multitude of other factors.  The synergy of the human and machine is where the true value lies, combining rapid, reproducible, and accurate automated reading, using contemporary technologies that now boast a raft of tools that improve image quality and reduce artifact, combined with the judgement of a human, for the cases that will remain technically challenging and suboptimal.  The Griffin et al (2023) study described an important but still early step in this evolution, and highlighted many of the technical challenges researchers still need to overcome.

Nurmohamed et al (2023) stated that the recent development of AI-QCT has enabled rapid analysis of atherosclerotic plaque burden and characteristics.  These investigators examined the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and compared the spectrum of plaque to manually assessed CCTA, CAC scoring (CACS), and clinical risk characteristics.  This was a long-term follow-up study of 536 patients referred for suspected CAD.  CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0 % PAV; stage 1: greater than 0 % to 5 % PAV; stage 2: greater than 5 % to 15 % PAV; stage 3: greater than 15% PAV).  The primary MACE outcome was a composite of non-fatal MI, non-fatal stroke, coronary re-vascularization, and all-cause mortality.  The mean age at baseline was 58.6 years and 297 patients (55 %) were men.  During a median follow-up of 10.3 (IQR: 8.6 to 11.5) years, 114 patients (21 %) experienced the primary outcome.  Compared to stages 0 and 1, patients with stage 3 PAV and percentage of non-calcified plaque volume of greater than 7.5 % had a more than 3-fold (adjusted HR: 3.57; 95 % CI: 2.12 to 6.00; p < 0.001) and 4-fold (adjusted HR: 4.37; 95 % CI: 2.51 to 7.62; p < 0.001) increased risk of MACE, respectively.  Addition of AI-QCT improved a model with clinical risk factors and CACS at different time-points during follow-up (10-year AUC: 0.82 [95 % CI: 0.78 to 0.87] versus 0.73 [95 % CI: 0.68 to 0.79]; p < 0.001; net re-classification improvement: 0.21 [95 % CI: 0.09 to 0.38]).  In addition, AI-QCT achieved an improved AUC compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95 % CI: 0.73 to 0.83; p = 0.023) and manual QCT (10-year AUC: 0.78; 95 % CI: 0.73 to 0.83; p = 0.040), although net re-classification improvement was modest (0.09 [95 % CI: -0.02 to 0.29] and 0.04 [95 % CI: -0.05 to 0.27], respectively).  The authors concluded that through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.  These investigators stated that these findings suggested that current clinical risk assessment in patients suspected of CAD could benefit from implementation of quantitative atherosclerosis assessment that includes plaque burden.

The authors stated that this study had several drawbacks.  First, this trial had a relatively small sample size, which was partially compensated by the long follow-up duration of more than 10 years.  Second, given the single-center nature of the study, CCTA scans were obtained using a single vendor.  Third, because this was a clinical cohort, a significant number of the patients underwent early re-vascularization on findings of severe obstructive disease, which had to be excluded from the re-vascularization component of the primary outcome.  In the AI-QCT analysis and subsequent quality assurance adjustment, several segments had to be excluded because of poor image quality, which may affect findings in future cohorts.  Fourth, 5 % of patients had no full follow-up available and were censored at the time of last contact for the previous follow-up study.  More importantly, as the current analysis used a single novel AI-guided approach to quantitative plaque analysis, the specific plaque volume thresholds may require re-calibration when used with different software platforms.  Fifth, although this study has unique 10-year follow-up, the field will benefit from large, prospective, multi-center studies with long-term follow-up to further validate these findings and assessment of the benefit of treatment.

Kim et al (2023) compared diagnostic performance, costs, and association with MACE of clinical CCTA interpretation versus semi-automated approach that use AI and machine learning (ML) for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for non-emergent invasive coronary angiography (ICA).  CCTA data from individuals enrolled into the randomized controlled CONSERVE Trial for an ACC/AHA guideline indication for ICA were analyzed.  Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc, New York, NY) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque.  CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up.  A total of 747 stable patients (60 ± 12.2 years, 49 % women) were included.  Using AI-QCT, 9 % of patients had no CAD compared with 34 % for clinical CCTA interpretation.  Application of AI-QCT to identify obstructive coronary stenosis at the greater than 50 % and greater than 70 % threshold would have reduced ICA by 87 % and 95 %, respectively.  Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78 % of patients with maximum stenosis of less than 50 %, no cardiovascular death or acute myocardial infarction (MI) occurred.  When applying an AI-QCT referral management approach to avoid ICA in patients with less than 50 % or less than 70 % stenosis, overall costs were reduced by 26 % and 34 %, respectively.  The authors concluded that in stable patients referred for ACC/AHA guideline-indicated non-emergent ICA, application of AI and ML for AI-QCT could significantly reduce ICA rates and costs with no change in 1-year MACE. 

The authors stated that this study had several drawbacks.  First, the current analyses were carried out in post-hoc fashion from an international, multi-center, RCT.  Second, AI-QCT was compared to clinical site interpretation by expert readers; however,  no blinded CCTA core laboratory was employed.  Third, as the CONSERVE Trial evaluation of ICA was carried out in pragmatic fashion, no blinded quantitative QCA analysis was performed and AIQCT could not be directly compared to QCA for diagnostic accuracy measures.  However, in previous multi-center clinical trials, AI-QCT has previously showed as having robust diagnostic performance compared to expert readers and QCA.  The present decision-model assumed that all severe stenoses would trigger referral to ICA and that ICA holds perfect sensitivity and specificity.

There is currently a lack of evidence that the use of the Cleerly Coronary Report would improve health outcomes of patients with CAD.

Computed Tomography (CT) Angiography Post Heart Transplantation Management

On behalf of the European Association of Cardiovascular Imaging/Cardiovascular Imaging Department of the Brazilian Society of Cardiology, Bodano and associates (2015) stated that despite the fact that coronary angiography is the current gold-standard method for the detection of cardiac allograft vasculopathy (CAV), the use of intra-vascular ultrasound (IVUS) should also be considered when there is a discrepancy between non-invasive imaging tests and coronary angiography concerning the presence of CAV.  In experienced centers, CT coronary angiography is a good alternative to coronary angiography to detect CAV.  In patients with a persistently high heart rate, scanners that provide high temporal resolution, such as dual-source systems, provide better image quality.  Finally, in patients with insufficient acoustic window, cardiac MRI is an alternative to echocardiography to evaluate cardiac chamber volumes and function and to exclude acute graft rejection and CAV in a surveillance protocol.

Ajluni and colleagues (2022) noted that CAV is the leading cause of long-term graft dysfunction in patients with heart transplantation and is linked with significant morbidity and mortality.  Currently, the gold standard for diagnosing CAV is coronary imaging with IVUS during traditional ICA.  Invasive imaging, however, carries increased procedural risk and expense to patients in addition to requiring an experienced interventionalist.  With the improvements in non-invasive cardiac imaging modalities such as transthoracic echocardiography (TTE), CT, MRI and PET, an alternative non-invasive imaging approach for the early detection of CAV may be feasible.  In a systematic review, these researchers examined the use of non-invasive imaging in diagnosis of CAV in more than 3,000 patients across 49 studies.  Furthermore, they discussed the strengths and weaknesses for each imaging modality.  Overall, all 4 imaging modalities show good-to-excellent accuracy for identifying CAV with significant variations across studies.  Majority of the studies compared non-invasive imaging with ICA without intravascular imaging.  The authors concluded that non-invasive imaging modalities offer an alternative approach to invasive coronary imaging for CAV.

Radiological Computer-Assisted Prioritization / Artificial Intelligence (AI) Software

Nanox.AI’s HealthCCSng (Nano-X Imaging LTD) is FDA-cleared for quantitative computed tomography (CT) tissue characterization using artificial intelligence (AI) software to detect coronary artery calcium (CAC), a biomarker used to detect cardiovascular disease. Chest CT scans are a common imaging modality used in cardiovascular medicine. HealthCCSng is used adjunctively with a chest CT to help aid in the identification and quantification of CAC levels in patients. It stratifies those patients based on their risk of cardiovascular disease and potential need for cardiac work up and treatment. HealthCCSng analyzes commonly ordered non-gated CT scans, which enables the quantification of CAC as an incidental finding. It automatically categorizes CT-scanned patients into three risk categories, based on the extent of cardiac calcium detected.  

Xu et al (2021) evaluated the risk category performance of artificial intelligence-based coronary artery calcium score (AI-CACS) software used in non-gated chest computed tomography (CT) on three types of CT machines, considering the manual method as the standard. A total of 901 patients who underwent both chest CT and electrocardiogram (ECG)-gated non-contrast-enhanced cardiac CT with the same equipment within a 3-month period were enrolled in the study. AI-CACS software was based on a deep learning algorithm and was trained on multi-vendor, multi-scanner, and multi-hospital anonymized data from the chest CT database. The AI-CACS was automatically obtained from chest CT data by the AI-CACS software, while the manual CACS was obtained from cardiac CT data by the manual method. The correlation of the AI-CACS and manual CACS, concordance rate and kappa value of the risk categories determined by the two methods were calculated. The chi-square test was used to evaluate the differences in risk categories among the three types of CT machines from different manufacturers. The risk category performance of the AI-CACS for dichotomous risk categories bounded by 0, 100 and 400 was assessed. The correlation of the AI-CACS with the manual CACS was ρ = 0.893 (p < 0.001). The Bland-Altman plot (AI-CACS minus manual CACS) showed a mean difference of -27.2 and 95% limits of agreement of -290.0 to 235.6. The agreement of risk categories for the CACS was kappa (κ) = 0.679 (p < 0.001), and the concordance rate was 80.6%. The risk categories determined by the AI-CACS software on three types of CT machines were not significantly different (p = 0.7543). As dichotomous risk categories bounded by 0, 100 and 400, the accuracy, kappa value, and area under the curve of the AI-CACS were 88.6% vs. 92.9% vs. 97.9%, 0.77 vs. 0.77 vs. 0.83, and 0.885 vs. 0.964 vs. 0.981, respectively. The authors concluded that there was good correlation and agreement between the AI-CACS and manual CACS in terms of the risk category. It is feasible to obtain the CACS using AI software based on non-gated chest CT data in a short time without increasing the radiation dose or economic burden. The AI-CACS software algorithm has good clinical universality and can be applied to CT machines from different manufacturers. 

Waltz et al (2020) state that low-dose computed tomography (LDCT) has been extensively validated for lung cancer screening in selected patient populations. Additionally, the use of gated cardiac CT to assess coronary artery calcium (CAC) burden has been validated to determine a patient's risk for major cardiovascular adverse events. This is typically performed by calculating an Agatston score based on density and overall burden of calcified plaque within the coronary arteries. Patients that qualify for LDCT for lung cancer screening commonly share major risk factors for coronary artery disease and would frequently benefit from an additional gated cardiac CT for the assessment of CAC. Given the widespread use of LDCT for lung cancer screening, the authors evaluated current literature regarding the use of non-gated chest CT, specifically LDCT, for the detection and grading of coronary artery calcifications. Additionally, given the evolving and increasing use of artificial intelligence (AI) in the interpretation of radiologic studies, current literature for the use of AI in CAC assessment was reviewed.  The authors reviewed primary scientific literature dating up to April 2020 using PubMed and Google Scholar, with the search terms low dose CT, lung cancer screening, coronary artery calcium, EKG/cardiac gated CT, deep learning, machine learning, and AI. The authors state that most studies note the inherent problems with the evaluation of the density of coronary calcifications on LDCT to give an accurate numeric calcium or Agatston score. The current method of evaluating CAC on LDCT involves using a qualitative categorical system (none, mild, moderate, or severe). When performed by cardiac imaging experts, this method broadly correlates with traditional CAC score groups (0, 1 to 100, 101 to 400, and > 400). Furthermore, given the high sensitivity of a properly protocolled LDCT for coronary calcium, a negative study for CAC precludes the need for a dedicated gated CT assessment. However, qualitative methods are not as accurate or reproducible when performed by general radiologists. The implementation of AI in the LDCT screening process has the potential to give a quantifiable and reproducible numeric value to the calcium score, based on whole heart volume scoring of calcium. This more closely aligns with the Agatston score and serves as a better guide for treatment and risk assessment using current guidelines. The authors concluded that CAC should be assessed on all LDCT performed for lung cancer screening and that a qualitative categorical scoring system should be provided in the impression for each patient. Early studies involving AI for the assessment of CAC are promising, but more extensive studies are needed before a final recommendation for its use can be given. The implementation of an accurate, automated AI CAC assessment tool would improve radiologist compliance and ease of overall workflow. Ultimately, the potential end result would be improved turnaround time, better patient outcomes, and reduced healthcare costs by maximizing preventative care in this high-risk population.

CT Angiography of the Coronary Arteries for Evaluation of Coronary Ectasia

In a retrospective study, Leschka et al (2008) examined the prevalence and morphological characteristics of coronary artery ectasia (CAE) with CTCA in comparison to conventional catheter angiography (CCA).  Dual-source CTCA examinations from 677 consecutive patients (223 women; median age of 57 years) were evaluated by 2 blinded observers for the presence of CAE defined as a diameter enlargement of greater than or equal to 1.5 times the diameter of adjacent normal coronary segments.  Vessel diameters and contrast attenuation within and proximal to ectatic segments were measured.  CCA was used to compare measurements obtained from CTCA with the coronary flow velocity by using the thrombolysis in myocardial infarction (TIMI) frame count.  CTCA identified CAE in 20 of 677 (3 %) patients.  CCA was performed in 10 of these patients.  CAE diameter measurements with CTCA (10.0 +/- 5.4 mm) correlated significantly (r = 0.92, p < 0.001) with the CCA measurements (8.8 +/- 4.9 mm); but had higher diameters (levels of agreement: -1.0 to 3.4 mm).  Contrast attenuation was significantly lower in the ectatic (343 +/- 63 HU) than in the proximal (394 +/- 60 HU) segments (p < 0.01).  The attenuation difference significantly correlated with the CAE ratio (r = 0.67, p < 0.01) and the TIMI frame count (r = 0.58, p < 0.05).  The prevalence of CAE in a population examined by CTCA was around 3 %.  The authors concluded that contrast attenuation measurements with CTCA correlated well with the flow alterations assessed with CCA.

Summaria et al (2011) noted that CAE is often considered an incidental finding during CA, however, several reports have shown an association with myocardial ischemia and MI.  When acute MI (AMI) occurs in cases of CAE, current re-perfusion therapies, due to the large arterial size and the massive intra-coronary thrombus, when used alone are limited in preventing the development of distal embolization and “no reflow phenomenon”.   In this study, these investigators described the case of a multiple sclerosis (MS) patient with diffuse CAE and ST elevation AMI, treated by coronary de-thrombosis multi-strategy (mechanical and pharmacologic) during a trans-radial primary angioplasty.  The higher thrombotic burden in MS with CAE was analyzed and possible common pathophysiologic pathways were discovered in the imbalance between proteolytic activities of metalloproteinases and endogenous tissue inhibitor, with subsequent increased proteolysis leading to a risk for coronary plaque rupture.  The 1-year clinical and angiographic follow-up with CCTA, together with long-term anti-platelet therapy, was also evaluated.

Hakim et al (2013) stated that CAE is rare in patients with Noonan syndrome.  When suspected during echocardiography more common causes including Kawasaki disease in children and atherosclerosis CAD in adults should be ruled out.  CTCA, a non-invasive imaging tool, may be preferred over conventional CA in the initial diagnosis and monitoring the progression of coronary ectasia in such patients.  Aspirin may be considered to prevent coronary thrombosis.

Al-Zakhari et al (2021) noted that the localized or diffused dilation of a coronary artery lumen is referred to as CAE.  Although it is well recognized, CAE is a rare finding that is encountered in the diagnostic procedure of CA.  This form of atherosclerotic CAD can be found in 1.4 % to 4.9 % of all CA patients.  CAE can manifest in combination with stenotic lesions or present as an isolated condition.  Its risk factors are similar to those of atherosclerosis.  The underlying pathophysiology entails a vascular re-modeling response to atherosclerosis.  Enzymatic degradation by matrix metalloproteinases (MMP) and accumulation of lipoproteins play an important role in the re-modeling process.  CAE can be diagnosed with the help of imaging modalities such as CCTA and coronary magnetic resonance angiogram (MRA); CA is considered the gold standard procedure.  The management strategies include treating the cardiovascular risk factors, prevention of thrombo-embolic events, and percutaneous/vascular re-vascularization.  CAE can be managed medically, but percutaneous/surgical re-vascularization (coronary artery bypass grafting (CABG)) is an option to treat patients with co-existing symptomatic obstructive lesion refractory to medical treatment.  Further trials are needed to optimize the management guidelines related to CAE.  The authors described the case of a 42-year-old man with a past medical history of hypertension, hyperlipidemia, and asthma who presented with shortness of breath and minimally elevated troponin level.  Coronary angiography revealed 3 vessels with ectasia and severe left ventricular dysfunction on ventriculography.

Rizzo et al (2022) presented a case of an asymptomatic patient with a giant coronary artery aneurysm developed in the context of diffuse CAE.  Giant coronary artery aneurysm was complicated by the presence of a large thrombus.  The heart team settled for surgical treatment of the lesion.  In this case, an invasive CA was carried out to confirm the echocardiographic finding.  A CTA of intra-cranial and neck vessels was carried out as well, revealing a 3-mm wide focal ectasia of the proximal branch of the middle cerebral artery (M1), and another 3-mm wide focal infundibular ectasia of the posterior communicating cerebral artery.  The authors noted that CTCA better defined anatomical features of the giant right coronary artery aneurysm.

Prognostic Value of Coronary Computed Tomography Angiography in Patients with Coronary Artery Bypass Graft

In a systematic review and meta-analysis, Hakimjavadi et al (2023) examined the prognostic value of CCTA in patients with CABG.  Medline, Embase, Cochrane Central Register of Controlled Trials, and Scopus were searched for relevant original studies published up to July 2021.  CCTA prognostic studies enrolling patients with CABG were screened and included if outcomes included all-cause mortality or major adverse cardiac events.  Maximally adjusted hazard ratios (HRs) were extracted for CCTA-derived prognostic factors.  Hazard ratios were log-transformed and pooled across studies using the DerSimonian-Laird random-effects model and statistical heterogeneity was assessed using the I2 statistic.  Of 1,576 screened articles, 4 retrospective studies fulfilled all inclusion criteria.  A total of 1,809 patients with CABG underwent CCTA (mean [SD] age of 67.0 [8.5] years across 3 studies, 81.5 % men across 4 studies).  Coronary artery disease severity and re-vascularization were categorized using 2 models: unprotected coronary territories and coronary artery protection score.  The pooled hazard ratios from the random-effects models using the most highly adjusted study estimate were 3.64 (95 % CI: 2.48 to 5.34, I2 = 57.8 %, p < 0.001; 4 studies) and 4.85 (95 % CI: 3.17 to 7.43, I2 = 39.9 %, p < 0.001; 2 studies) for unprotected coronary territories and coronary artery protection score, respectively.  Th authors concluded that in a limited number of studies, CCTA was an independent predictor of adverse events (AEs) in patients with CABG.  Moreover, these researchers stated that larger studies using uniform models and endpoints are needed.

Appendix

The Table can be used to assess if a person has a low or very low pre-test probability of CAD.  Alternatively, pre-test probability of CAD can be assessed using the Framingham Risk Scoring Tool available at the following website, with low risk defined as a 10-year risk of less than 10 %: Framingham Risk Scoring Tool. (For details on Framingham Risk Scoring, see appendix to CPB 0381 - Cardiac Disease Risk Tests.) Or 10-year pretest probability of atherosclerotic cardiovascular disease (ASCVD) can be estimated using Pooled Cohort equations from a downloadable spreadsheet and a web-based available at Cardio Vascular Risk Calculator and Cardio Vascular Prevention Guideline Tools.

Table: ACC Criteria for Pre-test Probability of CAD by Age, Gender, and SymptomsFootnotes for coronary artery disease (CAD)*
Age() Gender Typical / Definite Angina Pectoris Atypical / Probable Angina Pectoris Nonanginal Chest Pain Asymptomatic
Less than 39 Men Intermediate Intermediate Low Very Low
Less than 39 Women Intermediate Very Low Very Low Very Low
40-49 Men High Intermediate Intermediate Low
40-49 Women Intermediate Low Very Low Very Low
50-59 Men High Intermediate Intermediate Low
50-59 Women Intermediate Intermediate Low Very Low
60-69 Men High Intermediate Intermediate Low
60-69 Women High Intermediate Intermediate Low

Key:

  • High: greater than 90 % pre-test probability
  • Intermediate: between 10 % and 90 % pre-test probability
  • Low: between 5 % and 10 % pre-test probability
  • Very low: less than 5 % pre-test probability

Footnotes for coronary artery disease (CAD)* No data exist for patients less than 30 years or greater than 69 years, but it can be assumed that prevalence of CAD increases with age.  In a few cases, patients with ages at the extremes of the decades listed may have probabilities slightly outside the high or low range.

Source: Adapted from Taylor et al, 2010

Clinical Classification of Chest Pain

Typical angina (definite)

  • Substernal chest discomfort with a characteristic quality and duration; and
  • Provoked by exertion or emotional stress; and
  • Relieved by rest or nitroglycerin

Atypical angina (probable)

Meets 2 of the above criteria.

Non-cardiac chest pain

Meets 1 or none of the above criteria.

Source: Snow et al, 2004.

Contraindications to Exercise Stress Testing

The following contraindications to exercise stress testing are from the AHA/ACC guidelines:

  • Acute aortic dissection
  • Acute myocardial infarction (within 2 days)
  • Acute myocarditis or pericarditis
  • Acute pulmonary embolus or pulmonary infarction
  • Symptomatic severe aortic stenosis
  • Uncontrolled cardiac arrhythmias causing symptoms or hemodynamic compromise
  • Uncontrolled symptomatic heart failure
  • Unstable angina not previously stabilized by medical therapy.

In addition, exercise stress testing is not useful in persons who are unable to exercise, persons on digoxin, persons who have a cardiac conduction abnormality that prevents achievement of an adequate heart rate response, persons on a medication (e.g., beta blockers, other negative chronotropic agents) that can not be stopped which prevent achievement of an adequate heart rate response, and persons with an uninterpretable electrocardiogram.  The American College of Cardiology defines an uninterpretable electrocardiogram as a ventricular paced rhythm, complete left bundle branch block, ventricular preexcitation arrhythmia (Wolfe Parkinson White syndrome), or greater than 1 mm ST segment depression at rest.

Contraindications to Pharmacological Stress Testing

The following are contraindications to adenosine or dipyridamole (Persantine) stress testing:

  • Active bronchospasm or reactive airway disease;
  • Patients taking Persantine (contraindication to adenosine stress testing);
  • Patients using methylxanthines (e.g., caffeine and aminophylline) (In general, patients should refrain from ingesting caffeine for at least 24 hours prior to adenosine or dipyridamole administration);
  • Severe bradycardia (heart rate less than 40 beats/min);
  • Sick sinus syndrome or greater than first-degree heart block (in persons without a ventricular-demand pacemaker);
  • Systolic blood pressure less than 90 mm Hg.

The following are contraindications to dobutamine stress testing:

  • Atrial tachyarrhythmias with uncontrolled ventricular response;
  • History of ventricular tachycardia;
  • Left bundle branch block;
  • Recent (within the past week) myocardial infarction;
  • Significant aortic stenosis or obstructive cardiomyopathy;
  • Thoracic aortic aneurysm;
  • Uncontrolled hypertension;
  • Unstable angina.

References

The above policy is based on the following references:

  1. Abbara S, Blanke P, Maroules CD, et al. SCCT guidelines for the performance and acquisition of coronary computed tomographic angiography: A report of the Society of Cardiovascular Computed Tomography Guidelines Committee: Endorsed by the North American Society for Cardiovascular Imaging (NASCI). J Cardiovasc Comput Tomogr. 2016;10(6):435-449.
  2. Adelaide Health Technology Assessment (AHTA). Computed tomography coronary angiography. Horizon Scanning Prioritising Summary - Volume 12. Adelaide, SA: AHTA on behalf of National Horizon Scanning Unit (HealthPACT and MSAC); 2006.
  3. Agatston AS, Janowitz WR, Hildner FJ, et al. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15:827-832. 
  4. Al-Zakhari R, Aljammali S, Galligan S, Rotatori F. Coronary artery ectasia: A case report discussing the causes, diagnosis, and treatment. Cureus. 2021;13(4):e14431.
  5. Ajluni SC, Jr, Mously H, Chami T, et al. Non-invasive imaging in the evaluation of cardiac allograft vasculopathy in heart transplantation: A systematic review.  Curr Probl Cardiol. 2022;47(8):101103.
  6. Alsheikh-Ali AA, Kitsios GD, Balk EM, et al. The vulnerable atherosclerotic plaque: Scope of the literature. Ann Intern Med. 2010;153(6):387-395.
  7. American College of Cardiology Foundation Task Force on Expert Consensus Documents, Mark DB, Berman DS, Budoff MJ, et al. ACCF/ACR/AHA/NASCI/SAIP/SCAI/SCCT 2010 expert consensus document on coronary computed tomographic angiography: A report of the American College of Cardiology Foundation Task Force on Expert Consensus Documents. Circulation. 2010;121(22):2509-2543.
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