Effect of battery longevity on costs and health outcomes associated with cardiac implantable electronic devices: a Markov model-based Monte Carlo simulation.

Autor: Schmier JK; Exponent, Inc., 1800 Diagonal Road, Suite 500, Alexandria, VA, 22314, USA. jschmier@exponent.com., Lau EC; Exponent, Inc., Menlo Park, CA, USA., Patel JD; Exponent, Inc., Philadelphia, PA, USA., Klenk JA; Exponent, Inc., 1800 Diagonal Road, Suite 500, Alexandria, VA, 22314, USA., Greenspon AJ; Thomas Jefferson University, Philadelphia, PA, USA.
Jazyk: angličtina
Zdroj: Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing [J Interv Card Electrophysiol] 2017 Nov; Vol. 50 (2), pp. 149-158. Date of Electronic Publication: 2017 Nov 06.
DOI: 10.1007/s10840-017-0289-8
Abstrakt: Introduction: The effects of device and patient characteristics on health and economic outcomes in patients with cardiac implantable electronic devices (CIEDs) are unclear. Modeling can estimate costs and outcomes for patients with CIEDs under a variety of scenarios, varying battery longevity, comorbidities, and care settings. The objective of this analysis was to compare changes in patient outcomes and payer costs attributable to increases in battery life of implantable cardiac defibrillators (ICDs) and cardiac resynchronization therapy defibrillators (CRT-D).
Methods and Results: We developed a Monte Carlo Markov model simulation to follow patients through primary implant, postoperative maintenance, generator replacement, and revision states. Patients were simulated in 3-month increments for 15 years or until death. Key variables included Charlson Comorbidity Index, CIED type, legacy versus extended battery longevity, mortality rates (procedure and all-cause), infection and non-infectious complication rates, and care settings. Costs included procedure-related (facility and professional), maintenance, and infections and non-infectious complications, all derived from Medicare data (2004-2014, 5% sample). Outcomes included counts of battery replacements, revisions, infections and non-infectious complications, and discounted (3%) costs and life years. An increase in battery longevity in ICDs yielded reductions in numbers of revisions (by 23%), battery changes (by 44%), infections (by 23%), non-infectious complications (by 10%), and total costs per patient (by 9%). Analogous reductions for CRT-Ds were 23% (revisions), 32% (battery changes), 22% (infections), 8% (complications), and 10% (costs).
Conclusion: Based on modeling results, as battery longevity increases, patients experience fewer adverse outcomes and healthcare costs are reduced. Understanding the magnitude of the cost benefit of extended battery life can inform budgeting and planning decisions by healthcare providers and insurers.
Databáze: MEDLINE
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