Cost Effectiveness of an Electrocardiographic Deep Learning Algorithm to Detect Asymptomatic Left Ventricular Dysfunction

• Published in: Mayo Clinic proceedings Vol. 96; no. 7; pp. 1835 - 1844
• Main Authors: Tseng, Andrew S., Thao, Viengneesee, Borah, Bijan J., Attia, Itzhak Zachi, Medina Inojosa, Jose, Kapa, Suraj, Carter, Rickey E., Friedman, Paul A., Lopez-Jimenez, Francisco, Yao, Xiaoxi, Noseworthy, Peter A.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-07-2021; Elsevier, Inc
• Subjects: Cardiovascular diseases; Diagnosis; Electrocardiogram; Electrocardiography; Left ventricular function; Prices and rates; Technology application; Testing; United States; AI-ECG; TTE; ICER; ALVD; LVEF; QALY; PSA; QOL
• DOI: 10.1016/j.mayocp.2020.11.032
• EISSN: 1942-5546
• ISSN: 0025-6196

To evaluate the cost-effectiveness of an artificial intelligence electrocardiogram (AI-ECG) algorithm under various clinical and cost scenarios when used for universal screening at age 65.
We used decision analytic modeling to perform a cost-effectiveness analysis of the use of AI-ECG to screen for asymptomatic left ventricular dysfunction (ALVD) once at age 65 compared with no screening. This screening consisted of an initial screening decision tree and subsequent construction of a Markov model. One-way sensitivity analysis on various disease and cost parameters to evaluate cost-effectiveness at both $50,000 per quality-adjusted life year (QALY) and $100,000 per QALY willingness-to-pay threshold.
We found that for universal screening at age 65, the novel AI-ECG algorithm would cost $43,351 per QALY gained, test performance, disease characteristics, and testing cost parameters significantly affect cost-effectiveness, and screening at ages 55 and 75 would cost $48,649 and $52,072 per QALY gained, respectively. Overall, under most of the clinical scenarios modeled, coupled with its robust test performance in both testing and validation cohorts, screening with the novel AI-ECG algorithm appears to be cost-effective at a willingness-to-pay threshold of $50,000.
Universal screening for ALVD with the novel AI-ECG appears to be cost-effective under most clinical scenarios with a cost of <$50,000 per QALY. Cost-effectiveness is particularly sensitive to both the probability of disease progression and the cost of screening and downstream testing. To improve cost-effectiveness modeling, further study of the natural progression and treatment of ALVD and external validation of AI-ECG should be undertaken.