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Cost‐Effectiveness of Remote Cardiac Monitoring With the CardioMEMS Heart Failure System
Author(s) -
Schmier Jordana K.,
Ong Kevin L.,
Fonarow Gregg C.
Publication year - 2017
Publication title -
clinical cardiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.263
H-Index - 72
eISSN - 1932-8737
pISSN - 0160-9289
DOI - 10.1002/clc.22696
Subject(s) - medicine , heart failure , champion , cost effectiveness , cohort , intensive care medicine , risk analysis (engineering) , political science , law
Heart failure ( HF ) is a leading cause of cardiovascular mortality in the United States and presents a substantial economic burden. A recently approved implantable wireless pulmonary artery pressure remote monitor, the CardioMEMS HF System, has been shown to be effective in reducing hospitalizations among New York Heart Association ( NYHA ) class III HF patients. The objective of this study was to estimate the cost‐effectiveness of this remote monitoring technology compared to standard of care treatment for HF . A Markov cohort model relying on the CHAMPION ( CardioMEMS Heart Sensor Allows Monitoring of Pressure to Improve Outcomes in NYHA Class III Heart Failure Patients) clinical trial for mortality and hospitalization data, published sources for cost data, and a mix of CHAMPION data and published sources for utility data, was developed. The model compares outcomes over 5 years for implanted vs standard of care patients, allowing patients to accrue costs and utilities while they remain alive. Sensitivity analyses explored uncertainty in input parameters. The CardioMEMS HF System was found to be cost‐effective, with an incremental cost‐effectiveness ratio of $44,832 per quality‐adjusted life year ( QALY ). Sensitivity analysis found the model was sensitive to the device cost and to whether mortality benefits were sustained, although there were no scenarios in which the cost/ QALY exceeded $100,000. Compared with standard of care, the CardioMEMS HF System was cost‐effective when leveraging trial data to populate the model.

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