Bristol-Myers Squibb, Uxbridge, UK.
Pfizer, Tadworth, UK.
J Med Econ. 2020 Apr;23(4):386-393. doi: 10.1080/13696998.2019.1706543. Epub 2020 Jan 10.
As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effectiveness of targeted screening remains uncertain. This study aimed to assess the cost-effectiveness of targeted screening, informed by a machine learning (ML) risk prediction algorithm, to identify patients with AF. Cost-effectiveness analyses were undertaken utilizing a hybrid screening decision tree and Markov disease progression model. Costs and outcomes associated with the detection of AF compared traditional systematic and opportunistic AF screening strategies to targeted screening informed by a ML risk prediction algorithm. Model analyses were based on adults ≥50 years and adopted the UK NHS perspective. Targeted screening using the ML risk prediction algorithm required fewer patients to be screened (61 per 1,000 patients, compared to 534 and 687 patients in the systematic and opportunistic strategies) and detected more AF cases (11 per 1,000 patients, compared to 6 and 8 AF cases in the systematic and opportunistic screening strategies). The targeted approach demonstrated cost-effectiveness under base case settings (cost per QALY gained of £4,847 and £5,544 against systematic and opportunistic screening respectively). The targeted screening strategy was predicted to provide an additional 3.40 and 2.05 QALYs per 1,000 patients screened versus systematic and opportunistic strategies. The targeted screening strategy remained cost-effective in all scenarios evaluated. The analysis relied on assumptions that include the extended period of patient life span and the lack of consideration for treatment discontinuations/switching, as well as the assumption that the ML risk-prediction algorithm will identify asymptomatic AF. Targeted screening using a ML risk prediction algorithm has the potential to enhance the clinical and cost-effectiveness of AF screening, improving health outcomes through efficient use of limited healthcare resources.
由于许多心房颤动（AF）病例是无症状的，因此患者通常在出现并发症（如中风）之前未被诊断出来。风险预测算法可以帮助有效地识别未被诊断的 AF 患者。但是，靶向筛查的成本效益仍然不确定。本研究旨在评估基于机器学习（ML）风险预测算法的靶向筛查在识别 AF 患者中的成本效益。成本效益分析利用混合筛查决策树和马尔可夫疾病进展模型进行。与传统的系统性和机会性 AF 筛查策略相比，与检测 AF 相关的成本和结果与基于 ML 风险预测算法的靶向筛查进行了比较。模型分析基于年龄≥50 岁的成年人，并采用英国国民保健制度的观点。基于 ML 风险预测算法的靶向筛查需要筛查的患者更少（每 1000 名患者筛查 61 人，而系统性和机会性筛查策略每 1000 名患者分别筛查 534 人和 687 人），并且检测到更多的 AF 病例（每 1000 名患者筛查 11 例，而系统性和机会性筛查策略每 1000 名患者分别检测到 6 例和 8 例 AF 病例）。在基本情况下，靶向方法具有成本效益（每获得一个质量调整生命年的成本分别为 4847 英镑和 5544 英镑，而系统性和机会性筛查策略则分别为 4847 英镑和 5544 英镑）。与系统性和机会性筛查策略相比，靶向筛查策略每筛查 1000 名患者可额外获得 3.40 和 2.05 个 QALYs。在所有评估的情况下，靶向筛查策略均具有成本效益。该分析依赖于一些假设，包括患者寿命的延长期以及未考虑治疗中断/转换，以及假设 ML 风险预测算法将识别无症状的 AF。使用 ML 风险预测算法进行靶向筛查有可能增强 AF 筛查的临床和成本效益，通过有效利用有限的医疗保健资源改善健康结果。
