Construction of a risk predictive model for ICU-acquired weakness in patients with mechanical ventilation based on machine learning
10.3760/cma.j.cn115682-20240529-03025
- VernacularTitle:基于机器学习的机械通气患者ICU获得性衰弱风险预测模型的构建
- Author:
Jinxia JIANG
1
;
Shuyang LIU
;
Xiao SUN
;
Meimei TIAN
;
Yi LIU
;
Jinling XU
Author Information
1. 同济大学附属第十人民医院护理部，上海 200072
- Publication Type:Journal Article
- Keywords:
Mechanical ventilation;
ICU acquired weakness;
Predictive model;
Machine learning
- From:
Chinese Journal of Modern Nursing
2025;31(8):1059-1065
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To screen risk factors for ICU-acquired weakness in patients with mechanical ventilation and construct a predictive model, so as to provide a basis for the health management of patients with mechanical ventilation.Methods:Convenience sampling was used to select 312 ICU patients with mechanical ventilation admitted to the Tenth People's Hospital of Tongji University from October 2019 to August 2020 for the study. Patients were divided into training set ( n=220) and test set ( n=92) in a 7∶3 ratio. Based on machine learning algorithms, decision random forest (DRF), extremely-randomized trees (XRT) and generalized linear model (GLM) were used to construct three ICU-acquired weakness risk prediction models for patients with mechanical ventilation, respectively. The performance of the prediction model was evaluated using the area under the receiver operating characteristic curve ( AUC), the area under the precision-recall curve ( AUPRC), and the root mean square error ( RMSE) . Results:There were 7 predictors of risk of ICU-acquired weakness in patients with mechanical ventilation, including age, gender, braking, duration of mechanical ventilation, blood glucose, lactic acid, and parenteral nutrition. Test set and training set validation showed that AUC and AUPRC of GLM prediction model were greater than those of DRF, XRT prediction model. Test set validation indicated that the RMSE, logarithmic loss of GLM prediction model was less than those of DRF, XRT prediction model. Conclusions:Machine learning algorithm based GLM prediction model has good prediction performance. Healthcare professionals can construct evidence-based decisions for interventions in areas such as braking, duration of mechanical ventilation, and blood glucose management.
