Operations Research Center and Sloan School of Management, 2167Massachusetts Institute of Technology, Cambridge, MA, USA.
Alexandria Health, Cambridge, MA.
World J Pediatr Congenit Heart Surg. 2021 Jul;12(4):453-460. doi: 10.1177/21501351211007106. Epub 2021 Apr 28.
Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence techniques, we sought to develop nonlinear models for predicting outcomes in CHS.
We built machine learning (ML) models to predict mortality, postoperative mechanical ventilatory support time (MVST), and hospital length of stay (LOS) for patients who underwent CHS, based on data of more than 235,000 patients and 295,000 operations provided by the European Congenital Heart Surgeons Association Congenital Database. We used optimal classification trees (OCTs) methodology for its interpretability and accuracy, and compared to logistic regression and state-of-the-art ML methods (Random Forests, Gradient Boosting), reporting their area under the curve (AUC or c-statistic) for both training and testing data sets.
Optimal classification trees achieve outstanding performance across all three models (mortality AUC = 0.86, prolonged MVST AUC = 0.85, prolonged LOS AUC = 0.82), while being intuitively interpretable. The most significant predictors of mortality are procedure, age, and weight, followed by days since previous admission and any general preoperative patient risk factors.
The nonlinear ML-based models of OCTs are intuitively interpretable and provide superior predictive power. The associated risk calculator allows easy, accurate, and understandable estimation of individual patient risks, in the theoretical framework of the average performance of all centers represented in the database. This methodology has the potential to facilitate decision-making and resource optimization in CHS, enabling total quality management and precise benchmarking initiatives.
先天性心脏病手术（CHS）中常用的风险评估工具假设各种可能的风险因素以线性和相加的方式相互作用，这种假设可能并不反映现实。我们使用人工智能技术，试图为 CHS 的结果预测开发非线性模型。
我们基于欧洲先天性心脏病外科医生协会先天性数据库提供的超过 235000 名患者和 295000 次手术的数据，构建了机器学习（ML）模型，以预测 CHS 患者的死亡率、术后机械通气支持时间（MVST）和住院时间（LOS）。我们使用最优分类树（OCT）方法因其可解释性和准确性，并与逻辑回归和最先进的 ML 方法（随机森林、梯度提升）进行比较，报告了它们在训练和测试数据集的曲线下面积（AUC 或 c 统计量）。
最优分类树在所有三个模型中都表现出色（死亡率 AUC=0.86，MVST 延长 AUC=0.85，LOS 延长 AUC=0.82），同时具有直观的可解释性。死亡率的最重要预测因子是手术、年龄和体重，其次是上次入院后的天数和任何一般术前患者风险因素。
基于 OCT 的非线性 ML 模型具有直观的可解释性，并提供了卓越的预测能力。相关的风险计算器允许在数据库中代表的所有中心的平均表现的理论框架内，轻松、准确和理解地估计个体患者的风险。这种方法有可能促进 CHS 中的决策制定和资源优化，实现全面质量管理和精确的基准测试计划。
