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

Authors
 Hyun-Kyu Yoon  ;  Hyun Joo Kim  ;  Yi-Jun Kim  ;  Hyeonhoon Lee  ;  Bo Rim Kim  ;  Hyongmin Oh  ;  Hee-Pyoung Park  ;  Hyung-Chul Lee 
Citation
 BRITISH JOURNAL OF ANAESTHESIA, Vol.132(6) : 1304-1314, 2024-06 
Journal Title
BRITISH JOURNAL OF ANAESTHESIA
ISSN
 0007-0912 
Issue Date
2024-06
MeSH
Adult ; Aged ; Cohort Studies ; Electronic Health Records ; Female ; Humans ; Machine Learning* ; Male ; Middle Aged ; Postoperative Complications* / diagnosis ; Predictive Value of Tests ; Reproducibility of Results ; Respiration, Artificial ; Respiratory Insufficiency* ; Risk Assessment / methods ; Surgical Procedures, Operative / adverse effects
Keywords
machine learning ; noncardiac surgery ; postoperative complications ; reintubation ; respiratory failure
Abstract
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.
Full Text
https://www.sciencedirect.com/science/article/pii/S0007091224000503
DOI
10.1016/j.bja.2024.01.030
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Anesthesiology and Pain Medicine (마취통증의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hyun Joo(김현주) ORCID logo https://orcid.org/0000-0003-1963-8955
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/200070
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