Reducing Healthcare Costs by Predicting the Spontaneous Termination of Atrial Fibrillation: A Simulation Study

Atrial fibrillation (AF) significantly contributes to rising healthcare costs in Australia, with inpatient care accounting for most expenses. Recent literature has explored the use of a "wait-and-see" approach to managing patients presenting to emergency departments with primary AF given the high rate of spontaneous cardioversion (SCV), thereby avoiding invasive cardioversion and costly hospital admission. Limited adoption of this model of care may stem from challenges in identifying patients who truly need admission. To address this, predictive models for SCV are being explored. Our study aims to determine the accuracy threshold at which such models achieve cost savings by preventing unnecessary AF admissions.

A decision-analytic model was used alongside Monte Carlo simulations to estimate the variability in cost per patient with changes in prediction model accuracy and expected rates of SCV. Estimated costs were derived from a sample of patients presenting to Flinders Medical Centre or Noarlunga Hospital, South Australia in 2022-2023 with primary AF.

There were 669 admissions at Flinders Medical Centre or Noarlunga Hospital for primary AF in 2022-2023. SCV occurred in 240 (35.9%) cases, representing potentially avoidable admissions. The base case cost per admission was AUD$5,793.94, further increasing to $7,009.42 if interhospital transfer was required. The point at which cost benefit would be observed in our patient cohort was between 60% and 70% accuracy. There was an incremental reduction in cost in relation to increasing prediction model accuracy or population SCV rate.

Predicting SCV with an accuracy of 60%-70% in patients presenting with primary AF results in cost savings and reduced hospital bed utilisation through avoiding unnecessary admissions.