Outcomes and costs of remote patient monitoring among patients with implanted cardiac defibrillators: An economic model based on the PREDICT RM database.

BACKGROUND: Remote monitoring of implantable cardioverter-defibrillators has been associated with reduced rates of all-cause rehospitalizations and mortality among device recipients, but long-term economic benefits have not been studied. METHODS AND RESULTS: An economic model was developed using the PREDICT RM database comparing outcomes with and without remote monitoring. The database included patients ages 65 to 89 who received a Boston Scientific device from 2006 to 2010. Parametric survival equations were derived for rehospitalization and mortality to predict outcomes over a maximum time horizon of 25 years. The analysis assessed rehospitalization, mortality, and the cost-effectiveness (expressed as the incremental cost per quality-adjusted life year) of remote monitoring versus no remote monitoring. Remote monitoring was associated with reduced mortality; average life expectancy and average quality-adjusted life years increased by 0.77 years and 0.64, respectively (6.85 life years and 5.65 quality-adjusted life years). When expressed per patient-year, remote monitoring patients had fewer subsequent rehospitalizations (by 0.08 per patient-year) and lower hospitalization costs (by $554 per patient year). With longer life expectancies, remote monitoring patients experienced an average of 0.64 additional subsequent rehospitalizations with increased average lifetime hospitalization costs of $2784. Total costs of outpatient and physician claims were higher with remote monitoring ($47 515 vs $42 792), but average per patient-year costs were lower ($6232 vs $6244). The base-case incremental cost-effectiveness ratio was $10 752 per quality-adjusted life year, making remote monitoring high-value care. CONCLUSION: Remote monitoring is a cost-effective approach for the lifetime management of patients with implantable cardioverter-defibrillators.

Inclusion of compliance and persistence in economic models: past, present and future.

Economic models are developed to provide decision makers with information related to the real-world effectiveness of therapeutics, screening and diagnostic regimens. Although compliance with these regimens often has a significant impact on real-world clinical outcomes and costs, compliance and persistence have historically been addressed in a relatively superficial fashion in economic models. In this review, we present a discussion of the current state of economic modelling as it relates to the consideration of compliance and persistence. We discuss the challenges associated with the inclusion of compliance and persistence in economic models and provide an in-depth review of recent modelling literature that considers compliance or persistence, including a brief summary of previous reviews on this topic and a survey of published models from 2005 to 2012. We review the recent literature in detail, providing a therapeutic-area-specific discussion of the approaches and conclusions drawn from the inclusion of compliance or persistence in economic models. In virtually all publications, variation of model parameters related to compliance and persistence was shown to have a significant impact on predictions of economic outcomes. Growing recognition of the importance of compliance and persistence in the context of economic evaluations has led to an increasing number of economic models that consider these factors, as well as the use of more sophisticated modelling techniques such as individual simulations that provide an avenue for more rigorous consideration of compliance and persistence than is possible with more traditional methods. However, we note areas of continuing concern cited by previous reviews, including inconsistent definitions, documentation and tenuous assumptions required to estimate the effect of compliance and persistence. Finally, we discuss potential means to surmount these challenges via more focused efforts to collect compliance and persistence data.