Athens Heart Surgery Institute, Athens, Greece.
Alexandria Health, Cambridge, Massachusetts.
Ann Thorac Surg. 2024 Jul;118(1):199-206. doi: 10.1016/j.athoracsur.2023.10.034. Epub 2023 Dec 6.
We previously showed that machine learning-based methodologies of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery and assess case-mix-adjusted performance after benchmark procedures. We extend this methodology to provide interpretable, easily accessible, and actionable hospital performance analysis across all procedures.
The European Congenital Heart Surgeons Association Congenital Cardiac Database data subset of 172,888 congenital cardiac surgical procedures performed in European centers between 1989 and 2022 was analyzed. OCT models (decision trees) were built predicting hospital mortality (area under the curve [AUC], 0.866), prolonged postoperative mechanical ventilatory support time (AUC, 0.851), or hospital length of stay (AUC, 0.818), thereby establishing case-adjusted benchmarking standards reflecting the overall performance of all participating hospitals, designated as the "virtual hospital." OCT analysis of virtual hospital aggregate data yielded predicted expected outcomes (both aggregate and for risk-matched patient cohorts) for the individual hospital's own specific case-mix, readily available on-line.
Raw average rates were hospital mortality, 4.9%; mechanical ventilatory support time, 14.5%; and length of stay, 15.0%. Of 146 participating centers, compared with each hospital's overall case-adjusted predicted hospital mortality benchmark, 20.5% statistically (<90% CI) overperformed and 20.5% underperformed. An interactive tool based on the OCT analysis automatically reveals 14 hospital-specific patient cohorts, simultaneously assessing overperformance or underperformance, and enabling further analysis of cohort strata in any chosen time frame.
Machine learning-based OCT benchmarking analysis provides automatic assessment of hospital-specific case-adjusted performance after congenital heart surgery, not only overall but importantly, also by similar risk patient cohorts. This is a tool for hospital self-assessment, particularly facilitated by the user-accessible online-platform.
我们之前已经证明，基于机器学习的最优分类树（OCT）方法可以准确预测先天性心脏病手术后的风险，并评估基准手术后的病例组合调整后表现。我们将这种方法扩展到提供跨所有手术的可解释、易于访问和可操作的医院绩效分析。
对欧洲先天性心脏外科协会先天性心脏数据库中 1989 年至 2022 年间在欧洲中心进行的 172888 例先天性心脏手术的子数据集进行了分析。建立 OCT 模型（决策树），预测医院死亡率（曲线下面积 [AUC]，0.866）、术后机械通气支持时间延长（AUC，0.851）或住院时间延长（AUC，0.818），从而建立反映所有参与医院整体表现的病例调整基准标准，指定为“虚拟医院”。对虚拟医院汇总数据进行 OCT 分析，为单个医院特定的病例组合生成预测的预期结果（汇总结果和风险匹配的患者队列结果），这些结果可在网上随时获得。
原始平均率为医院死亡率 4.9%、机械通气支持时间 14.5%和住院时间 15.0%。在 146 家参与中心中，与每家医院的整体病例调整后预测的医院死亡率基准相比，有 20.5%的医院（90%CI 以下）表现统计上优于基准，20.5%的医院表现低于基准。基于 OCT 分析的交互式工具自动显示 14 个医院特定的患者队列，同时评估表现优于或低于基准，并允许在任何选定的时间框架内进一步分析队列分层。
基于机器学习的 OCT 基准分析提供了先天性心脏病手术后医院特定病例调整后表现的自动评估，不仅是整体表现，而且重要的是，还可以按类似风险的患者队列进行评估。这是一种用于医院自我评估的工具，特别是通过用户可访问的在线平台得到了极大的便利。
