Construction and evaluation of machine learning-based delirium prediction models for ICU patients with multiple trauma
10.3760/cma.j.cn501098-20240408-00266
- VernacularTitle:基于机器学习的ICU多发伤患者发生谵妄预测模型的构建与评估
- Author:
Dongxue HU
1
;
Chengzhi NIU
;
Chunyu ZHAO
;
Lili ZHAO
;
Xin WANG
Author Information
1. 郑州大学第一附属医院重症医学科，郑州 450052
- Keywords:
Multiple trauma;
Delirium;
Prognosis;
Machine learning
- From:
Chinese Journal of Trauma
2024;40(11):1016-1021
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct machine learning-based delirium prediction models for ICU patients with multiple trauma and evaluate their prediction efficiency.Methods:A retrospective case-control study was conducted to analyze the clinical data of 417 ICU multiple trauma patients admitted to the First Affiliated Hospital of Zhengzhou University from July 2019 to June 2022, including 305 males and 112 females, aged 18-88 years [(47.8±15.7)years]. The score of acute physiology and chronic health status assessment II (APACHE II) was 0-50 points [(9.80±0.29)points]. The patients were randomly divided into training set ( n=291) and test set ( n=126) with a ratio of 7∶3. The demographic data, past history, treatment and laboratory results of the patients were collected. Lasso regression analysis was applied to screen variables that were significantly correlated to the incidence of delirium in the training set and the variables were then included into the machine learning models. Six machine learning methods including the random forest, gradient boosting tree, extreme gradient boosting, logistic regression, support vector machine and K nearest neighbor were used to construct the delirium prediction models for ICU multiple trauma patients. The accuracy, sensitivity, precision, F1 fraction and area under the curve (AUC) of the receiver′s operating characteristics (ROC) curve were calculated by using the data in the test set to evaluate the prediction efficiency of the models. Results:With regards to the six prediction models, namely random forests, gradient boosting tree, extreme gradient boosting, logistic regression, support vector machine and K nearest neighbor prediction models, the accuracy in the test set was 0.70, 0.68, 0.69, 0.73, 0.70 and 0.60 respectively; the sensitivity was 0.74, 0.80, 0.81, 0.86, 0.85 and 0.69 respectively; the precision was 0.72, 0.69, 0.70, 0.73, 0.71 and 0.65 respectively; the F1 fraction was 0.73, 0.74, 0.75, 0.79, 0.78 and 0.67 respectively; the AUC was 0.72, 0.73, 0.72, 0.80, 0.74 and 0.64 respectively. Among them, the logistic regression model had the best discriminability.Conclusion:Delirium prediction models for ICU patients with multiple trauma have been successfully constructed, among which the logistic regression model has the best prediction efficiency and can serve as an effective tool for early prediction and prevention of delirium in the clinical care of patients with multiple trauma.
