Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2026 Jan;38(1):166-171. doi: 10.3760/cma.j.cn121430-20250319-00278.
ABSTRACT
OBJECTIVE: Predict the occurrence of pressure injury in critically ill patients by using machine learning models and conducting internal validation.
METHODS: A prospective cohort study was conducted. Critically ill patients admitted to the intensive care unit (ICU) of the North District of Hangzhou First People’s Hospital from January 2023 to March 2024 were enrolled using convenience sampling. Patients were divided into two groups based on the occurrence of pressure injury during their ICU stay, and the differences in pressure injury related indicators were compared between the groups. The dataset was randomly divided into a training set (75%) and a validation set (25%). Feature selection was performed using the Lasso regression. Independent risk factors were then identified via multivariate Logistic regression analysis. An extreme gradient boosting (XG-Boost) machine learning model was developed to predict pressure injury risk. The model’s performance was comprehensively evaluated using receiver operator characteristic curve (ROC curve), calibration curve, and clinical decision curve analysis (DCA). The Shapley Additive exPlanations (SHAP) method was used to rank feature importance.
RESULTS: A total of 350 critically ill patients were included, of whom 102 (29.1%) developed pressure injuries. There were statistically significant differences in consciousness status, mechanical ventilation, sedative use, length of ICU stay, Braden score, use of warm blankets, white blood cell count, neutrophil count, blood glucose, and lactate level between the pressure injury and non-pressure injury groups (all P<0.05). Lasso regression analysis identified six predictive variables: consciousness status, mechanical ventilation, use of warm blankets, length of ICU stay, neutrophil count, and blood glucose. Multivariate Logistic regression analysis subsequently revealed that mechanical ventilation, use of warm blankets, prolonged ICU stay, elevated neutrophil count, and elevated blood glucose were independent risk factors for pressure injuries [mechanical ventilation: odds ratio (OR)=2.338, 95% confidence interval (95%CI) was 1.768-3.089, P=0.002; use of warm blankets: OR=1.772, 95%CI was 1.341-2.338, P=0.039; prolonged ICU stay: OR=1.081, 95%CI was 1.067-1.097, P<0.001; elevated neutrophil count: OR=1.044, 95%CI was 1.022-1.067, P=0.036; elevated blood glucose: OR=1.062, 95%CI was 1.031-1.094, P=0.027]. Based on these six risk factors, a predictive model was constructed using the XG-Boost method. The ROC curve analysis demonstrated the model has high predictive performance, with an area under the curve (AUC) of 0.896 (95%CI was 0.863-0.929) in the training set and 0.835 (95%CI was 0.761-0.908) in the validation set. The calibration curve indicated good agreement between predicted probabilities and actual outcomes. DCA further suggested that the model had clinical utility across a wide range of threshold probabilities. SHAP analysis ranked feature importance in descending order as follows: length of ICU stay, mechanical ventilation, neutrophil count, consciousness status, blood glucose, and use of warm blankets.
CONCLUSIONS: The constructed XG-Boost machine learning model has high performance in predicting the occurrence of pressure injury in critically ill patients. Identify key predictive factors can aid clinical risk assessment and intervention.
PMID:41876243 | DOI:10.3760/cma.j.cn121430-20250319-00278
