Hui Victor, Litton Edward, Edibam Cyrus, Geldenhuys Agneta, Hahn Rebecca, Larbalestier Robert, Wright Brian, Pavey Warren
Department of Anaesthesia and Pain Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia.
Heart Lung Research Institute of Western Australia, Perth, WA, Australia.
Eur J Cardiothorac Surg. 2023 Dec 1;64(6). doi: 10.1093/ejcts/ezad297.
The primary objective was to predict bleeding after cardiac surgery with machine learning using the data from the Australia New Zealand Society of Cardiac and Thoracic Surgeons Cardiac Surgery Database, cardiopulmonary bypass perfusion database, intensive care unit database and laboratory results.
We obtained surgical, perfusion, intensive care unit and laboratory data from a single Australian tertiary cardiac surgical hospital from February 2015 to March 2022 and included 2000 patients undergoing cardiac surgery. We trained our models to predict either the Papworth definition or Dyke et al.'s universal definition of perioperative bleeding. Our primary outcome was the performance of our machine learning algorithms using sensitivity, specificity, positive and negative predictive values, accuracy, area under receiver operating characteristics curve (AUROC) and area under precision-recall curve (AUPRC).
Of the 2000 patients undergoing cardiac surgery, 13.3% (226/2000) had bleeding using the Papworth definition and 17.2% (343/2000) had moderate to massive bleeding using Dyke et al.'s definition. The best-performing model based on AUPRC was the Ensemble Voting Classifier model for both Papworth (AUPRC 0.310, AUROC 0.738) and Dyke definitions of bleeding (AUPRC 0.452, AUROC 0.797).
Machine learning can incorporate routinely collected data from various datasets to predict bleeding after cardiac surgery.
主要目标是利用澳大利亚和新西兰心胸外科医师学会心脏手术数据库、体外循环灌注数据库、重症监护病房数据库及实验室结果中的数据，通过机器学习预测心脏手术后的出血情况。
我们获取了2015年2月至2022年3月间一家澳大利亚三级心脏外科医院的手术、灌注、重症监护病房及实验室数据，纳入2000例接受心脏手术的患者。我们训练模型以预测帕普沃思定义或戴克等人的围手术期出血通用定义。我们的主要结局是机器学习算法的性能，采用灵敏度、特异度、阳性和阴性预测值、准确率、受试者操作特征曲线下面积（AUROC）和精确召回率曲线下面积（AUPRC）。
在2000例接受心脏手术的患者中，按照帕普沃思定义，13.3%（226/2000）发生出血；按照戴克等人的定义，17.2%（343/2000）发生中度至大量出血。基于AUPRC表现最佳的模型，对于帕普沃思出血定义（AUPRC 0.310，AUROC 0.738）和戴克出血定义（AUPRC 0.452，AUROC 0.797）均为集成投票分类器模型。
机器学习可以整合来自各种数据集的常规收集数据，以预测心脏手术后的出血情况。
