Indirect Measurement of Left Ventricular Filling Pressure (LVFP)

Number: 0704

Table Of Contents

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

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Policy

Scope of Policy

This Clinical Policy Bulletin addresses indirect measurement of left ventricular filling pressure (LVFP).

1. Experimental and Investigational

   Aetna considers the following interventions experimental and investigational because the effectiveness of these approaches has not been established:

   1. Indirect measurement of left ventricular filling pressure by computerized calibration of arterial waveform response to the Valsalva maneuver (e.g., the VeriCor® System) because there is insufficient evidence on the clinical value of this measurement in the diagnosis and management of individuals with congestive heart failure or other indications;
   2. EchoGo Heart Failure for detection of heart failure with preserved ejection fraction, and all other indications.

2. Related Policies

   • CPB 0472 - Electrical Bioimpedance for Cardiac Output Monitoring and Other Selected Indications

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Table:

CPT Codes / HCPCS Codes / ICD-10 Codes

Code	Code Description
HCPCS codes not covered for indications listed in the CPB:
C9786	Echocardiography image post processing for computer aided detection of heart failure with preserved ejection fraction, including interpretation and report [EchoGo Heart Failure]
ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):
I50.1 - I50.9	Heart failure

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Background

The Vericor System (CVP Diagnostics, Boston, MA) involves the indirect measurement of left ventricular filling pressure (LVFP) by analysis of arterial waveform response to the Valsalva maneuver using a proprietary algorithm.  The Vericor System was cleared by the U.S. Food and Drug Administration based on a 510(k) premarket notification. 

To examine the relationship and the level of accuracy of a non-invasive system in directly determining LVEDP, Sharma et al (2002) assessed LVFP by measuring PCWP and LVEDP in 57 persons followed immediately by a Valsalva maneuver using the VeriCor System during elective right and left heart catheterization.  The VeriCor and PCWP measurements were then compared with results from the catheter-measured LVEDP.  VeriCor measurements correlated significantly with catheter-measured LVEDP (r = 0.86), comparable to the correlation of the PCWP with catheter-measured LVEDP (r = 0.81).  VeriCor measurements were within 4 mm Hg of direct LVEDP measurements 84 % of the time and within 6 mm Hg of these measurements 93 % of the time, whereas the corresponding values for PCWP were 41 % and 67 %, respectively.

According to the American College of Cardiology/American Heart Association (ACC/AHA) Practice Guidelines on Heart Failure (2001), the role of periodic invasive or non-invasive hemodynamic measurements in the management of heart failure remains uncertain.  The guidelines stated that, although hemodynamic measurements can be performed by non-invasive methods, these tests have not been shown to be more valuable than routine tests, including physical examination.  Moreover, it is not clear whether serial non-invasive hemodynamic measurements can be used to gauge the efficacy of treatment or to identify patients most likely to deteriorate symptomatically during long-term follow-up.

There is inadequate evidence of the clinical utility of these indirect measurements of LVFP by computerized calibration of the arterial waveform response to the Valsalva maneuver.  Clinical outcome studies published in the peer-reviewed medical literature are necessary to determine the value of this test in the clinical management of patients with CHF.

In a small, prospective study, Sharma and colleagues (2011) examined if non-invasive monitoring of LVEDP would reduce re-hospitalization rates in patients hospitalized for HF.  A total of 50 patients admitted for HF were randomized to management guided by daily non-invasive estimated LVEDP monitoring (group I, open) to a target LVEDP of less than 20 mm Hg or management based on clinical assessment alone without knowledge of the estimated LVEDP (group II, blinded).  Non-invasive estimated LVEDP was measured by the VeriCor monitor.  The primary endpoints were the reduction of estimated LVEDP at discharge and the HF re-hospitalization rate on follow-up.  Estimated LVEDP was significantly reduced at discharge in the open group compared with the blinded group (mean estimated LVEDP 19.7 +/- 1.3 mm Hg versus 25.6 +/- 1.5 mm Hg, respectively, p = 0.01).  The re-hospitalization rates for HF on follow-up were significantly improved in the open group compared with the blinded group (at 1 month: 0 % versus 25 %, respectively [p = 0.05]; at 3 months: 0 % versus 32 % [p = 0.01]; at 6 months: 4 % versus 36 % [p = 0.01]; at 1 year: 16 % versus 48 % [p = 0.03]).  The authors concluded that when HF is managed by clinical assessment only, estimated LVEDPs remain high at discharge, resulting in early and frequent re-hospitalization for HF.  Therapy guided by estimated LVEDP monitoring optimizes filling pressures and reduces HF re-hospitalization rates.  Findings of this small study need to be validated by well-designed studies.

EchoGo Heart Failure

EchoGo Heart Failure is an artificial intelligence (AI)-based platform that enables precision detection of heart failure with preserved ejection fraction (HFpEF).  The device employs AI to detect HFpEF from a single echocardiogram image, which accounts for 50 % of the 64 million cases of HF worldwide and has overtaken heart failure with reduced ejection fraction (HFrEF) as the most prevalent form of the deadly disease.

Wang et al (2022) noted that cardiovascular risk factors, biomarkers, and diseases are associated with poor prognosis in COVID-19 infections.  Significant progress in artificial intelligence (AI) applied to cardiac imaging has recently been made.  In a single-center study, these researchers examined the use of AI analytic software EchoGo in COVID-19 inpatients.  A total of 50 consecutive COVID-19+ inpatients (age of 66 ± 13 years, 22 women) who had echocardiography in April 17, 2020 to August 5, 2020 were analyzed with EchoGo software, with output correlated against standard echocardiography measurements.  After adjustment for the APACHE-4 score, associations with clinical outcomes were assessed.  Mean EchoGo outputs were left ventricular end-diastolic volume (LVEDV) 121 ± 42 ml, end-systolic volume (LVESV) 53 ± 30 ml, ejection fraction (LVEF) 58 ± 11 %, and global longitudinal strain (GLS) −16.1 ± 5.1 %.  Pearson correlation coefficients (p-value) with standard measurements were 0.810 (< 0.001), 0.873 (< 0.001), 0.528 (< 0.001), and 0.690 (< 0.001).  The primary endpoint occurred in 26 (52 %) patients.  Adjusting for APACHE-4 score, EchoGo LVEF and LVGLS were associated with the primary endpoint, odds ratios 0.92; (95 % confidence intervals [CI}: 0.85 to 0.99) and 1.22 (95 % CI: 1.03 to 1.45) per 1 % increase, respectively.  The authors concluded that automated AI software is a new clinical tool that may assist with patient care.  EchoGo LVEF and LVGLS were associated with adverse outcomes in hospitalized COVID-19 patients and can play a role in their risk stratification. 

The authors stated that this study had several drawbacks. First, it was an observational, cohort, single-center study with inherent biases.  Second, study power and multi-variable analyses were restrained by the number of patients and clinical events, so the APACHE-IV score was used as a surrogate to measure global clinical risk.  Third, a minority of patients were excluded because of suboptimal image quality, which was to be expected for bedside TTE studies of sick COVID-19 patients, some of whom were in the intensive care unit.  Fourth, the EchoGo software currently only analyzes a limited number of TTE parameters, although there is ongoing software development to expand its analytic capabilities.  The Velocity Vector Imaging technique was used for standard strain measurement analysis as it is a vendor neutral method, although it is known to have a slightly lower magnitude of LVGLS than other vendors such as GE EchoPAC and may also have explained its slightly lower LVGLS values than EchoGo.  These researchers also focused on evaluating associations between in-hospital outcomes and TTE, including EchoGo measurements, rather than longer-term outcomes beyond hospital discharge, where further research is needed.

O'Driscoll et al (2022) examined if LVEF and global longitudinal strain (GLS), automatically calculated by AI, would increase the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection.  In a multi-center study, SEs from 512 subjects who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from 7 hospitals in the U.K. and U.S. were studied.  Visual wall motion scoring (WMS) was carried out to identify inducible ischemia.  Furthermore, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical 2 and 4 chamber images only) with Ultromics EchoGo Core 1.0.  Receiver operator characteristic (ROC) curves and multi-variable risk models were used to examine accuracy for identification of participants subsequently found to have CAD on angiography.  Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all p < 0.001).  The areas under the receiver operating characteristics (AUC) for WMS index, AI-LVEF, and AI-GLS at peak stress were 0.92, 0.86, and 0.82, respectively, with cut-offs of 1.12, 64 %, and -17.2 %, respectively.  Multi-variable analysis showed that addition of peak AI-LVEF or peak AI-GLS to WMS significantly improved model discrimination of CAD [C-statistic (bootstrapping 2.5th, 97.5th percentile)] from 0.78 (0.69 to 0.87) to 0.83 (0.74 to 0.91) or 0.84 (0.75 to 0.92), respectively.  The authors concluded that they have demonstrated that automated AI quantification of LVEF and GLS in contrast-enhanced and unenhanced SE images was feasible both at rest and with different modes of stress in a multi-center study.  These measures conferred additional independent prognostic information in participants with suspected obstructive CAD, above and beyond inducible wall motion abnormalities alone.  These findings supported the increased use of quantification in SE in order to improve its diagnostic performance and use in identifying and managing CAD.  These investigators stated that future research is needed to examine the impact AI may have on predicting long-term adverse outcomes in patients undergoing echocardiography.

The authors stated that this study had several drawbacks.  First, the method of CAD classification, whereby presence of inducible ischemia was used to determine whether participants underwent coronary angiography, introduced a case selection bias for diagnosis of significant CAD.  In addition, no central reading or quality control of readers was carried out before entering data in the data bank, and the classification of CAD did not include data on fractional flow reserve.  Second, additional case selection bias might also have been introduced by the retrospective nature of the RAINIER study.  This resulted in high absolute AUROCs and odds ratio for diagnostic performance of WMSI but still allows relative evaluation of WMSI versus LVEF and GLS.  Third, the derivation of GLS used only the apical 2- and 4-chamber views and the effect of including the 3-chamber view is unclear.  Fourth, previous studies have shown that quantification of transient ischemic dilatation is an independent predictor of mortality in patients with CAD, and a marker of multi-vessel disease, whereas this study has focused on ischemic dilatation at end-diastole and end-systole and shown they are useful for identifying significant CAD.  Fifth, those undergoing pre-operative assessment were excluded from this analysis and evaluation for this patient group would also be of interest.  Sixth, although these researchers have demonstrated an incremental benefit of the use of LVEF and GLS in SE, they have not compared this increase in accuracy to their parallel developments in the use of AI to provide autonomous diagnostic assessment of the likelihood of CAD based on combinations of multiple parameters.  These researchers are currently performing the multi-center PROspective randomized control Trial Evaluating the Use of AI in Stress echocardiography trial in 2,500 participants (PROTEUS, ISRCTN registry ID 15113915) to examine the performance of the EchoGo platform for identifying significant CAD and on the rate of unnecessary angiography and healthcare costs.  Furthermore, whether the use of GLS and LVEF may have value in those who do not achieve peak stress may be of interest to study.

Woodward et al (2023) stated that SE is one of the most commonly used diagnostic imaging tests for CAD; however, it requires clinicians to visually examine scans to identify patients who may benefit from invasive investigation and treatment.  EchoGo Pro provides an automated interpretation of SE based on AI image analysis.  In reader studies, use of EchoGo Pro when making clinical decisions improves diagnostic accuracy and confidence.  Prospective evaluation in real world practice is now important to understand the impact of EchoGo Pro on the patient pathway and outcome.  The PROTEUS Trial is a randomized, 2-armed, non-inferiority, multi-center study aiming to recruit 2,500 participants from National Health Service (NHS) hospitals in the U.K. referred to SE clinics for investigation of suspected CAD.  All participants will undergo a stress echocardiogram protocol as per local hospital policy.  Participants will be randomized 1:1 to a control group, representing current practice, or an intervention group, in which clinicians will receive an AI image analysis report (EchoGo Pro, Ultromics Ltd) to use during image interpretation, indicating the likelihood of severe CAD.  The primary outcome will be appropriateness of clinician decision to refer for coronary angiography.  Secondary outcomes will evaluate other health impacts including appropriate use of other clinical management approaches, impact on variability in decision-making, patient and clinician qualitative experience and a health economic analysis.  The authors concluded that the PROTEUS Trial will be the 1st study to examine the impact of introducing an AI medical diagnostic aid into the standard care pathway of patients with suspected CAD being examined with SE.

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References