• Ran Liu, Haiwang Liu, Ling Li, Zhixue Wang, and Yan Li.
• Department of Anesthesiology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
• Medicine (Baltimore). 2022 Oct 21; 101 (42): e31251e31251.

AbstractPredicting the mortality of patients provides a reference for doctors to judge their physical condition. This study aimed to construct a nomogram to improve the prediction accuracy of patients' mortality. Patients with severe diseases were screened from the Medical Information Mart for Intensive Care (MIMIC) III database; 70% of patients were randomly selected as the training set for the model establishment, while 30% were used as the test set. The least absolute shrinkage and selection operator (LASSO) regression method was used to filter variables and select predictors. A multivariable logistic regression fit was used to determine the association between in-hospital mortality and risk factors and to construct a nomogram. A total of 9276 patients were included. The area under the curve (AUC) for the clinical nomogram based on risk factors selected by LASSO and multivariable logistic regressions were 0.849 (95% confidence interval [CI]: 0.835-0.863) and 0.821 (95% CI: 0.795-0.846) in the training and test sets, respectively. Therefore, this nomogram might help predict the in-hospital mortality of patients admitted to the intensive care unit (ICU).Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.

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