Department of Electrical and Systems Engineering, McKelvey School of Engineering, Washington University in St Louis, St Louis, Missouri.
Department of Computer Science and Engineering, McKelvey School of Engineering, Washington University in St Louis, St Louis, Missouri.
JAMA Netw Open. 2021 Mar 1;4(3):e212240. doi: 10.1001/jamanetworkopen.2021.2240.
Postoperative complications can significantly impact perioperative care management and planning.
To assess machine learning (ML) models for predicting postoperative complications using independent and combined preoperative and intraoperative data and their clinically meaningful model-agnostic interpretations.
DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study assessed 111 888 operations performed on adults at a single academic medical center from June 1, 2012, to August 31, 2016, with a mean duration of follow-up based on the length of postoperative hospital stay less than 7 days. Data analysis was performed from February 1 to September 31, 2020.
Outcomes included 5 postoperative complications: acute kidney injury (AKI), delirium, deep vein thrombosis (DVT), pulmonary embolism (PE), and pneumonia. Patient and clinical characteristics available preoperatively, intraoperatively, and a combination of both were used as inputs for 5 candidate ML models: logistic regression, support vector machine, random forest, gradient boosting tree (GBT), and deep neural network (DNN). Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Model interpretations were generated using Shapley Additive Explanations by transforming model features into clinical variables and representing them as patient-specific visualizations.
A total of 111 888 patients (mean [SD] age, 54.4 [16.8] years; 56 915 [50.9%] female; 82 533 [73.8%] White) were included in this study. The best-performing model for each complication combined the preoperative and intraoperative data with the following AUROCs: pneumonia (GBT), 0.905 (95% CI, 0.903-0.907); AKI (GBT), 0.848 (95% CI, 0.846-0.851); DVT (GBT), 0.881 (95% CI, 0.878-0.884); PE (DNN), 0.831 (95% CI, 0.824-0.839); and delirium (GBT), 0.762 (95% CI, 0.759-0.765). Performance of models that used only preoperative data or only intraoperative data was marginally lower than that of models that used combined data. When adding variables with missing data as input, AUROCs increased from 0.588 to 0.905 for pneumonia, 0.579 to 0.848 for AKI, 0.574 to 0.881 for DVT, 0.5 to 0.831 for PE, and 0.6 to 0.762 for delirium. The Shapley Additive Explanations analysis generated model-agnostic interpretation that illustrated significant clinical contributors associated with risks of postoperative complications.
The ML models for predicting postoperative complications with model-agnostic interpretation offer opportunities for integrating risk predictions for clinical decision support. Such real-time clinical decision support can mitigate patient risks and help in anticipatory management for perioperative contingency planning.
术后并发症会显著影响围手术期护理管理和计划。
评估机器学习 (ML) 模型在使用独立和联合术前及术中数据预测术后并发症方面的表现，并对其具有临床意义的模型不可知解释进行评估。
设计、设置和参与者：本回顾性队列研究评估了 2012 年 6 月 1 日至 2016 年 8 月 31 日在一家学术医疗中心接受的 111888 例成人手术，基于术后住院时间的平均随访时间不到 7 天。数据分析于 2020 年 2 月 1 日至 9 月 31 日进行。
结局包括 5 种术后并发症：急性肾损伤 (AKI)、谵妄、深静脉血栓形成 (DVT)、肺栓塞 (PE) 和肺炎。将术前、术中以及两者组合的患者和临床特征作为 5 个候选 ML 模型（逻辑回归、支持向量机、随机森林、梯度提升树 [GBT] 和深度神经网络 [DNN]）的输入。使用接受者操作特征曲线下的面积 (AUROC) 比较模型性能。使用 Shapley 加法解释来生成模型解释，即将模型特征转化为临床变量，并将其表示为患者特定的可视化效果。
共有 111888 例患者（平均[标准差]年龄，54.4[16.8]岁；56915 例[50.9%]女性；82533 例[73.8%]白人）纳入本研究。每种并发症的表现最佳模型均结合了术前和术中数据，AUROCs 如下：肺炎 (GBT)，0.905（95%CI，0.903-0.907）；AKI（GBT），0.848（95%CI，0.846-0.851）；DVT（GBT），0.881（95%CI，0.878-0.884）；PE（DNN），0.831（95%CI，0.824-0.839）；谵妄（GBT），0.762（95%CI，0.759-0.765）。仅使用术前数据或仅使用术中数据的模型的性能略低于使用联合数据的模型。当将具有缺失数据的变量添加为输入时，肺炎的 AUROC 从 0.588 增加到 0.905，AKI 从 0.579 增加到 0.848，DVT 从 0.574 增加到 0.881，PE 从 0.5 增加到 0.831，谵妄从 0.6 增加到 0.762。Shapley 加法解释分析生成了具有临床意义的模型不可知解释，说明了与术后并发症风险相关的重要临床因素。
具有模型不可知解释的预测术后并发症的 ML 模型为临床决策支持提供了整合风险预测的机会。这种实时临床决策支持可以降低患者风险，并有助于对围手术期意外情况进行预期管理。
