Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database

作者：Jun Li, Yiwu Sun, Jie Ren, Yifan Wu, Zhaoyi He · 发表于：Journal of Cardiothoracic and Vascular Anesthesia · 年份：2024 · DOI：10.1053/j.jvca.2024.12.016 · 被引用次数：13 · 研究领域：Sepsis Diagnosis and Treatment、Heart Failure Treatment and Management、Machine Learning in Healthcare

BACKGROUND: The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in the intensive care unit (ICU) is higher. However, there is currently no method to accurately predict the mortality of AHF patients. METHODS: The Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-Ⅳ database and randomly divided into a training set (n = 3,580, 70%) and a validation set (n = 1,534, 30%). The variates collected include demographic data, vital signs, comorbidities, laboratory test results, and treatment information within 24 hours of ICU admission. By using the least absolute shrinkage and selection operator (LASSO) regression model in the training set, variates that affect the in-hospital mortality of AHF patients were screened. Subsequently, in the training set, five common machine learning (ML) algorithms were applied to construct models using variates selected by LASSO to predict the in-hospital mortality of AHF patients. The predictive ability of the models was evaluated for sensitivity, specificity, accuracy, the area under the curve of receiver operating characteristics, and clinical net benefit in the validation set. To obtain a model with the best predictive ability, the predictive ability of common scoring systems was compared with the best ML model. RESULTS: Among the 5,114 patie...