■PULsE-AI试验旨在确定筛查策略的有效性,该策略包括机器学习风险预测算法与诊断测试相结合,以识别初级保健中未诊断的房颤(AF)。本研究旨在评估在现实世界中实施筛查策略的成本效益。
■来自PULsE-AI试验的数据-前瞻性,随机化,2019年6月至2021年2月在英格兰6项一般实践中进行的对照试验用于成本效益分析,包括混合筛查决策树和MarkovAF疾病进展模型.模型结果在个人和人群水平(估计英国人口≥30岁,未诊断的房颤高危人群)报告,并包括筛查的患者人数。确定的房颤病例数,平均总成本和增量成本(筛查,事件,治疗),质量调整寿命年(QALYs),和增量成本效益比(ICER)。
■据估计,在英国高危人群(330万)中,筛查策略可导致45,493例新的房颤诊断。与仅常规护理相比,估计增加了14,004次终生诊断。接受筛查策略的高风险个体的每位患者费用估计为1,985英镑(而仅接受常规护理的个体为1,888英镑)。在人口层面,筛查策略的费用增加约3.22亿英镑,QALY增加81,000例.与常规护理相比,筛查策略仅在公认的ICER门槛为每QALY获得20,000英镑时显示出成本效益,ICER为3994英镑/QALY。
■与仅常规护理相比,它是成本效益的目标个人在高风险的未诊断的房颤,通过AF风险预测算法,然后谁应该接受诊断测试。这种房颤风险预测算法可以减少需要筛选以识别未确诊房颤的患者数量。从而减轻初级保健负担。
UNASSIGNED: The PULsE-AI
trial sought to determine the effectiveness of a screening strategy that included a machine learning risk prediction algorithm in conjunction with diagnostic testing for identification of undiagnosed atrial fibrillation (AF) in primary care. This
study aimed to evaluate the cost-effectiveness of implementing the screening strategy in a real-world setting.
UNASSIGNED: Data from the PULsE-AI
trial - a prospective, randomized, controlled
trial conducted across six general practices in England from June 2019 to February 2021 - were used to inform a cost-effectiveness analysis that included a hybrid screening decision tree and Markov AF disease progression model. Model outcomes were reported at both individual- and population-level (estimated UK population ≥30 years of age at high-risk of undiagnosed AF) and included number of patients screened, number of AF cases identified, mean total and incremental costs (screening, events, treatment), quality-adjusted-life-years (QALYs), and incremental cost-effectiveness ratio (ICER).
UNASSIGNED: The screening strategy was estimated to result in 45,493 new diagnoses of AF across the high-risk population in the UK (3.3 million), and an estimated additional 14,004 lifetime diagnoses compared with routine care only. Per-patient costs for high-risk individuals who underwent the screening strategy were estimated at £1,985 (vs £1,888 for individuals receiving routine care only). At a population-level, the screening strategy was associated with a cost increase of approximately £322 million and an increase of 81,000 QALYs. The screening strategy demonstrated cost-effectiveness versus routine care only at an accepted ICER threshold of £20,000 per QALY-gained, with an ICER of £3,994/QALY.
UNASSIGNED: Compared with routine care only, it is cost-effective to target individuals at high risk of undiagnosed AF, through an AF risk prediction algorithm, who should then undergo diagnostic testing. This AF risk prediction algorithm can reduce the number of patients needed to be screened to identify undiagnosed AF, thus alleviating primary care burden.