Patrick Daly

DocwireNews

In a recent article in the <a href="https://www.tandfonline.com/journals/ijme20">Journal of Medical Economics</a>, researchers evaluated the cost-effectiveness of implementing a machine learning risk prediction algorithm to identify patients with undiagnosed atrial fibrillation (AF) in primary care. According to the lead author Nathan R. Hill, &ldquo;compared with routine care only, it is cost-effective to target individuals at high risk of undiagnosed AF through an AF risk prediction algorithm [to determine] who should then undergo diagnostic testing.&rdquo;The researchers utilized data from the PULsE-AI (prediction of undiagnosed atrial fibrillation using a machine learning algorithm) trial, which was conducted across 6 general practice centers in England from June 2019 to February 2021. Hybrid decision tree and Markov disease models were used to evaluate the cost-effectiveness of the risk algorithm. Outcomes included number of patients screened, number of AF cases identified, mean total and incremental costs, quality-adjusted life-years (QALYS), and the incremental cost-effectiveness ratio (ICER).<h3>Cost Efficacy and Outcomes of the PULsE-AI Algorithm</h3>According to the authors, the screening strategy resulted in an estimated 45,483 new diagnoses of AF across the high-risk population of 3.3 million individuals in the UK, as well as an estimated 14,004 lifetime diagnoses compared to routine care alone.The individual-level costs for high-risk patients who underwent the study&rsquo;s screening strategy were estimated to be &pound;1,985, compared to &pound;1,888 for patients receiving routine care only. Additionally, the screening strategy was attributed to a cost increase of &pound;322 million and an increase of 81,000 QALYs.Ultimately, the authors reported that the <a href="https://www.docwirenews.com/condition-center/atrial-fibrillation/impact-of-renal-function-on-mortality-in-recently-hospitalized-patients-with-atrial-fibrillation/">atrial fibrillation</a> screening strategy was cost-effective compared to routine care &ldquo;only at an accepted ICER threshold of &pound;120,000 per QALY gained, with an ICER of &pound;3,994/QALY.&rdquo; They suggested that their study &ldquo;highlights the potential value to both patients and the healthcare system in AI-based methods to support screening for AF.&rdquo;Find More Research at the <a href="https://www.docwirenews.com/atrial-fibrillation/">Atrial Fibrillation Knowledge Hub</a>

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Cost Analysis of Machine Learning-Based Atrial Fibrillation Screening Strategy

Atrial Fibrillation

In a recent article in the Journal of Medical Economics, researchers evaluated the cost-effectiveness of implementing a machine learning risk prediction algorithm to identify ...

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