Your browser doesn't support javascript.
loading
Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.
Yoon, Hyun-Kyu; Kim, Hyun Joo; Kim, Yi-Jun; Lee, Hyeonhoon; Kim, Bo Rim; Oh, Hyongmin; Park, Hee-Pyoung; Lee, Hyung-Chul.
Afiliación
  • Yoon HK; Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
  • Kim HJ; Department of Anesthesiology and Pain Medicine, Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
  • Kim YJ; Institute of Convergence Medicine, Ewha Womans University Mokdong Hospital, Seoul, South Korea.
  • Lee H; Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea.
  • Kim BR; Department of Anesthesiology and Pain Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea.
  • Oh H; Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
  • Park HP; Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
  • Lee HC; Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea. Electronic address: vital@snu.ac.kr.
Br J Anaesth ; 132(6): 1304-1314, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38413342
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.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Complicaciones Posoperatorias / Insuficiencia Respiratoria / Aprendizaje Automático Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Br J Anaesth Año: 2024 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Complicaciones Posoperatorias / Insuficiencia Respiratoria / Aprendizaje Automático Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Br J Anaesth Año: 2024 Tipo del documento: Article País de afiliación: Corea del Sur Pais de publicación: Reino Unido