“Heart failure (HF) is a major public health issue that is associated with high prevalence, morbidity, and mortality rates and high financial and social burdens,” write Chris Boodoo, BSc, MSc, and colleagues. Although telemonitoring
has been shown to improve all-cause mortality and hospitalization rates in patients with HF and the Medly program—a telemonitoring intervention integrated as standard of care at a large Canadian academic hospital for ambulatory
patients with HF—has been found to improve patient outcomes, the cost-effectiveness of the Medly program is yet to be determined.
A Cost-Utility Analysis of Medly
For a study published in the Journal of Medical Internet Research, Boodoo and colleagues conducted a cost-utility analysis of the Medly program compared with the standard of care for HF in Ontario, Canada, from the perspective of the public healthcare payer. Using a microsimulation model, individual patient data were simulated over a 25-year time horizon to compare the costs and quality-adjusted life-years (QALYs) between the Medly program and standard care for patients with HF treated in the ambulatory care setting. Data were sourced from a Medly Program Evaluation study and literature to inform model parameters, such as Medly’s effectiveness in reducing mortality and hospitalizations, healthcare and intervention costs, and model transition probabilities. Scenario analyses were conducted in relation to HF severity and telemonitoring deployment models. One-way deterministic effectiveness analysis and probabilistic sensitivity analysis were performed to explore the impact on the results of uncertainty in model parameters.
Cost-effective Compared With Standard Care
Over a 25-year time horizon, the average total costs were Canadian (Can) $97,497 (US $73,547.84) for the comparator group and Can $102,508 (US $77,327.93) for patients using Medly. The average total QALYs gained were 4.95 and 5.51 for the comparator group and Medly patients, respectively. When comparing the 2 groups, there was an incremental cost of Can $5011 (US $3,780.10) with an incremental QALY gained of 0.566. This resulted in an incremental cost-effectiveness ratio (ICER) of Can $8,850 (US $6,676.09)/QALY (Table). On the basis of 1,000 simulations of the reference case scenario in which each parameter was sampled from their respective distribution, 81.6% showed that Medly was costlier and more effective, whereas 17.3% showed that Medly was less costly and more effective. Approximately 90% of the simulations resulted in an ICER below the Can $50,000 (US $37,718)/QALY threshold. As New York Health Association (NYHA) functional class increased, the average total costs and incremental costs increased. In addition, as NYHA functional class increased, total QALYs per population decreased. This led to a decreasing trend in ICERs with increasing NYHA functional class. At a willingness-to-pay threshold of $50,000 (US $37,718), the probability of cost-effectiveness for NYHA functional classes I, II, and
III was 90.5%, 90.6%, and 87.5%, respectively. As the only difference between scenarios was the total cost incurred by the Medly group, all comparator groups had the same average total costs and average total QALYs. The average total QALYs for patients using Medly were also the same for all scenarios. Moreover, as expected, when the proportion of FK users increased, so did the average total costs for patients using Medly. This led to ICERs following the same trend. When the relative risk (RR) for mortality was set to its lower range, the ICER increased to Can
$18,556 (US $13,997.90)/QALY. When the RR for mortality was set to its upper range, Medly became dominant. When the RR for hospitalization was set to its lower range, Medly became dominant. When the RR for hospitalization was set to its upper range, the ICER increased to Can $29,240 (US $22,057.49)/QALY.
“The significance of the study findings are 3-fold,” write Boodoo and colleagues. “1) providing evidence
for health care decision-makers on the use of TM for HF, 2) supporting the use of a nurse-led model of TM using clinically validated algorithms within HF clinics, and 3) informing the use of economic modeling for future evaluation of early-stage health informatics technology.”