Background: Atrial fibrillation (AF) poses a significant economic burden. An increasing number of interventions for AF require cost-effectiveness analysis with decision-analytic modeling to demonstrate value. However, high-quality cost estimates of AF that can be used to inform decision-analytic models are lacking.
Objectives: The objectives of this study were to determine whether phase-based costing methods are feasible and practical for informing decision-analytic models outside of oncology.
Methods: Patients diagnosed with AF between 1 January 2003 and 30 June 2011 in Ontario, Canada were identified based on a hospital admission for AF using administrative data housed at the Institute for Clinical Evaluative Sciences. Patient observations were then divided into phases based on clinical events typically used for decision-analytic modeling (i.e., minor stroke/transient ischemic attack [TIA], moderate to severe ischemic stroke, myocardial infarction, extracranial hemorrhage [ECH], intracranial hemorrhage [ICH], multiple events, death from an event, or death from other causes). First 30-day and greater than 30-day costs of healthcare resources in each health state were estimated based on a validated methodology. All costs are reported in 2013 Canadian dollars (Can$) and from a healthcare payer perspective.
Results: Patients (n = 109,002) with AF who did not experience a clinical event incurred costs of Can$1566 per 30 days, on average. The average 30-day cost of experiencing a fatal clinical event was Can$42,871, but the cost of dying from all other causes was much smaller (Can$12,800). The clinical events associated with the highest short-term costs were ICH (Can$22,347) and moderate to severe ischemic stroke (Can$19,937). The lowest short-term costs were due to minor ischemic stroke/TIA (Can$12,515) and ECH (Can$12,261). Patients who had experienced a moderate to severe ischemic stroke incurred the highest long-term costs.
Conclusions: Real-world Canadian data and a phase-based costing approach were used to estimate short- and long-term costs associated with AF-related major clinical events. The results of this study can also inform decision-analytic models for AF.