用机器学习帮医生选最合适的心脏瓣膜，降低术后起搏器植入风险。
Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach
- 融合多源数据构建跨国家患者数据库，提升模型泛化能力。
- 在美国内部数据中降低26%、希腊验证中降低16%的起搏器植入率。
- 首个个性化瓣膜选型方案，适合心血管手术决策支持场景。
经导管主动脉瓣置换术（TAVR）已成为治疗严重主动脉瓣狭窄这一致命性心血管疾病的重要微创手段。尽管已有多种经导管心脏瓣膜（THV）获批使用，但瓣膜类型的选择标准仍存争议。本文提出一种数据驱动的临床辅助工具，旨在通过优化瓣膜选择以最小化永久性起搏器植入（PPI）风险——这是TAVR术后主要并发症。我们整合美国与希腊患者群体数据，融合患者人口学信息、计算机断层扫描及超声心动图三类数据源，并统一两国医疗系统差异。引入叶级分析方法，利用人群异质性，避免依赖不确定的反事实风险估计。最终的处方模型在内部美国队列中将PPI率降低26%，在外部希腊验证队列中降低16%。据我们所知，这是首个统一的、个性化的THV选型策略。
原文摘要 · Abstract (English)
Transcatheter Aortic Valve Replacement (TAVR) has emerged as a minimally invasive treatment option for patients with severe aortic stenosis, a life-threatening cardiovascular condition. Multiple transcatheter heart valves (THV) have been approved for use in TAVR, but current guidelines regarding valve type prescription remain an active topic of debate. We propose a data-driven clinical support tool to identify the optimal valve type with the objective of minimizing the risk of permanent pacemaker implantation (PPI), a predominant postoperative complication. We synthesize a novel dataset that combines U.S. and Greek patient populations and integrates three distinct data sources (patient demographics, computed tomography scans, echocardiograms) while harmonizing differences in each country's record system. We introduce a leaf-level analysis to leverage population heterogeneity and avoid benchmarking against uncertain counterfactual risk estimates. The final prescriptive model shows a reduction in PPI rates of 26% and 16% compared with the current standard of care in our internal U.S. population and external Greek validation cohort, respectively. To the best of our knowledge, this work represents the first unified, personalized prescription strategy for THV selection in TAVR.
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