Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery

急性呼吸衰竭 医学 呼吸衰竭 重症监护医学 可靠性工程 外科 计算机科学 工程类 麻醉 机械通风
作者
Hyun‐Kyu Yoon,Hyun Joo Kim,Yi‐Jun Kim,Hyeonhoon Lee,Bo Rim Kim,Hyongmin Oh,Hee‐Pyoung Park,Hyung‐Chul Lee
出处
期刊:BJA: British Journal of Anaesthesia [Elsevier]
卷期号:132 (6): 1304-1314 被引量:1
标识
DOI:10.1016/j.bja.2024.01.030
摘要

Background Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery. Methods Easily extractable electronic health record (EHR) variables that do not require subjective assessment by clinicians were used. From EHR data of 307,333 noncardiac surgical cases, the model, trained with a gradient boosting algorithm, utilised a derivation cohort of 99,025 cases from Seoul National University Hospital (2013–9). External validation was performed using three separate cohorts A–C from different hospitals comprising 208,308 cases. Model performance was assessed by area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC), a measure of sensitivity and precision at different thresholds. Results The model included eight variables: serum albumin, age, duration of anaesthesia, serum glucose, prothrombin time, serum creatinine, white blood cell count, and body mass index. Internally, the model achieved an AUROC of 0.912 (95% confidence interval [CI], 0.908–0.915) and AUPRC of 0.113. In external validation cohorts A, B, and C, the model achieved AUROCs of 0.879 (95% CI, 0.876–0.882), 0.872 (95% CI, 0.870–0.874), and 0.931 (95% CI, 0.925–0.936), and AUPRCs of 0.029, 0.083, and 0.124, respectively. Conclusions Utilising just eight easily extractable variables, this machine learning model demonstrated excellent discrimination in both internal and external validation for predicting postoperative respiratory failure. The model enables personalised risk stratification and facilitates data-driven clinical decision-making.
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