Application of a machine learning model for early prediction of in-hospital cardiac arrests: Retrospective observational cohort study.

Autor: Socias Crespí L; Intensive Care Department, Son Llàtzer University Hospital, Crta. Manacor Km 4, 07198, Palma, Spain; Department of Medicine, Faculty of Medicine, University of the Balearic Islands, Crta. Valldemossa Km. 7.5, Palma, Spain; Group of Critic Patient, Health Research Institute of the Balearic Islands (IdISBa), 07198, Palma, Spain. Electronic address: lsocias@hsll.es., Gutiérrez Madroñal L; Intensive Care Department, Son Llàtzer University Hospital, Crta. Manacor Km 4, 07198, Palma, Spain; Department of Medicine, Faculty of Medicine, University of the Balearic Islands, Crta. Valldemossa Km. 7.5, Palma, Spain; Research Unit, Son Llàtzer University Hospital, Crta. Manacor Km 4, 07198, Palma, Spain., Fiorella Sarubbo M; Research Unit, Son Llàtzer University Hospital, Crta. Manacor Km 4, 07198, Palma, Spain; Department of Biology, Faculty of Science, University of the Balearic Islands, Crta. Valldemossa Km. 7.5, Palma, Spain., Borges-Sa M; Intensive Care Department, Son Llàtzer University Hospital, Crta. Manacor Km 4, 07198, Palma, Spain; Department of Medicine, Faculty of Medicine, University of the Balearic Islands, Crta. Valldemossa Km. 7.5, Palma, Spain., Serrano García A; Knowledge Engineering Institute, Universidad Autónoma de Madrid, Madrid, Spain., López Ramos D; Knowledge Engineering Institute, Universidad Autónoma de Madrid, Madrid, Spain., Pruenza Garcia-Hinojosa C; Knowledge Engineering Institute, Universidad Autónoma de Madrid, Madrid, Spain., Martin Garijo E; Knowledge Engineering Institute, Universidad Autónoma de Madrid, Madrid, Spain.
Jazyk: angličtina
Zdroj: Medicina intensiva [Med Intensiva (Engl Ed)] 2024 Aug 29. Date of Electronic Publication: 2024 Aug 29.
DOI: 10.1016/j.medine.2024.07.004
Abstrakt: Objective: To describe the results of the application of a Machine Learning (ML) model to predict in-hospital cardiac arrests (ICA) 24 hours in advance in the hospital wards.
Design: Retrospective observational cohort study.
Setting: Hospital Wards.
Patients: Data were extracted from the hospital's Electronic Health Record (EHR). The resulting database contained a total of 750 records corresponding to 620 different patients (370 patients with ICA and 250 control), between may 2009 and december 2021.
Interventions: No.
Main Variables of Interest: As predictors of ICA, a set of 28 variables including personal history, vital signs and laboratory data was employed.
Models: For the early prediction of ICA, predictive models based on the following ML algorithms and using the mentioned variables, were developed and compared: K Nearest Neighbours, Support Vector Machine, Multilayer Perceptron, Random Forest, Gradient Boosting and Custom Ensemble of Gradient Boosting estimators (CEGB).
Experiments: Model training and evaluation was carried out using cross validation. Among metrics of performance, accuracy, specificity, sensitivity and AUC were estimated.
Results: The best performance was provided by the CEGB model, which obtained an AUC = 0.90, a specificity = 0.84 and a sensitivity = 0.81. The main variables with influence to predict ICA were level of consciousness, haemoglobin, glucose, urea, blood pressure, heart rate, creatinine, age and hypertension, among others.
Conclusions: The use of ML models could be of great support in the early detection of ICA, as the case of the CEGB model endorsed, which enabled good predictions of ICA.
(Copyright © 2024. Published by Elsevier España, S.L.U.)
Databáze: MEDLINE
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