Predictive modeling of ICU-AW inflammatory factors based on machine learning

Abstract Background ICU-acquired weakness (ICU-AW) is a common complication among ICU patients, and we used machine learning techniques to construct an ICU-AW inflammatory factor prediction model to predict the risk of disease development and reduce the incidence of ICU-AW. Methods The Least Absolute Shrinkage and Selection Operator (LASSO) technique was used to screen key variables related to ICU-AW. Eleven indicators, such as the presence of sepsis, glucocorticoids(GC), neuromuscular blocking agents(NBAs), length of ICU stay, the Acute Physiology and Chronic Health Evaluation Score II (APACHE II), as well as the levels of albumin(ALB), lactate(LAC), glucose(GLU), interleukin-1β (IL-1β), interleukin-6 (IL-6), and interleukin-10 (IL-10), were used as variables to establish the prediction model. We divided the data into a dataset that included inflammatory factors and a dataset that excluded inflammatory factors. Separately, 70% of the participants in both datasets were used as the training set, and 30% of the participants were used as the test set. Three machine learning methods, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB), were used in the 70% participant training set to construct six different models, which were validated and evaluated in the remaining 30% participants as the test set. The optimal model was visualized for prediction using nomograms. Results The logistic regression model including the inflammatory factor demonstrated excellent performance on the test set with the area under the curve (AUC) of 82.1% and the best calibration curve fit, outperforming the other five models. The optimal model is represented visually in nomograms. Conclusion This study used easily accessible clinical characteristics and laboratory data that can help early clinical recognition of ICU-AW. inflammatory factors IL-1β, IL-6, and IL-10 have high predictive value for ICU-AW. Trial registration The trial was registered at the Chinese Clinical Trial Registry with the registration number: ChiCTR2300077968.