Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission.
Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission.
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DOI：
10.1097/sla.0000000000003297
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发表时间：
2020-12
影响因子：
9
通讯作者：
Abdullah HR
中科院分区：
文献类型：
--
作者：
Chiew CJ;Liu N;Wong TH;Sim YE;Abdullah HR
将机器学习模型与传统得出的手术风险综合评估 (CARES) 模型和美国麻醉医师协会身体状况 (ASA-PS) 模型在预测 30 天术后死亡率和重症监护病房 (ICU) 停留 > 24 小时的需求方面的性能进行比较。术前预测手术风险对于临床共享决策和ICU床位等卫生资源规划具有重要意义。当前电子病历的增长与机器学习相结合，为提高已建立的风险模型的性能提供了机会。纳入2012年1月1日至2016年10月31日期间在新加坡中央医院（SGH）接受非心脏和非神经系统手术的所有年龄18岁及以上的患者。患者的人口统计数据、合并症、术前实验室结果和手术详细信息是从他们的电子病历中获得的。 70% 的观察结果被随机选择用于训练，剩下 30% 用于测试。基线模型是 CARES 和 ASA-PS。使用随机森林、自适应增强、梯度增强和支持向量机对候选模型进行训练。根据受试者工作特征曲线下面积（AUROC）和精确回忆曲线下面积（AUPRC）评估模型。总共纳入了 90,785 例患者，其中 539 例（0.6%）在 30 天内死亡，1264 例（1.4%）术后 24 小时以上需要入住 ICU。尽管灵敏度较差，但基线模型通过预测以负数为主的数据集中的所有负数，实现了较高的 AUROC。梯度提升是表现最好的模型，死亡率和 ICU 入院结果的 AUPRC 分别为 0.23 和 0.38。与传统风险计算器相比，机器学习可用于改进手术风险预测。当数据集不平衡时，应使用 AUPRC 而不是 AUROC 来评估模型预测性能。
To compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the prediction of 30-day postsurgical mortality and need for intensive care unit (ICU) stay >24 hours. Prediction of surgical risk preoperatively is important for clinical shared decision-making and planning of health resources such as ICU beds. The current growth of electronic medical records coupled with machine learning presents an opportunity to improve the performance of established risk models. All patients aged 18 years and above who underwent noncardiac and nonneurological surgery at Singapore General Hospital (SGH) between 1 January 2012 and 31 October 2016 were included. Patient demographics, comorbidities, preoperative laboratory results, and surgery details were obtained from their electronic medical records. Seventy percent of the observations were randomly selected for training, leaving 30% for testing. Baseline models were CARES and ASA-PS. Candidate models were trained using random forest, adaptive boosting, gradient boosting, and support vector machine. Models were evaluated on area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). A total of 90,785 patients were included, of whom 539 (0.6%) died within 30 days and 1264 (1.4%) required ICU admission >24 hours postoperatively. Baseline models achieved high AUROCs despite poor sensitivities by predicting all negative in a predominantly negative dataset. Gradient boosting was the best performing model with AUPRCs of 0.23 and 0.38 for mortality and ICU admission outcomes respectively. Machine learning can be used to improve surgical risk prediction compared to traditional risk calculators. AUPRC should be used to evaluate model predictive performance instead of AUROC when the dataset is imbalanced.