A machine learning algorithm-based predictive model for pressure injury risk in emergency patients: A prospective cohort study.

OBJECTIVES: To construct pressure injury risk prediction models for emergency patients based on different machine learning algorithms, to optimize the best model, and to provide a suitable assessment tool for preventing the occurrence of pressure injuries in emergency patients.

METHODS: A convenience sampling was used to select 312 patients admitted to the emergency department of a tertiary care hospital in Tianjin, China, from May 2022 to March 2023, and the patients were divided into a modeling group (n = 218) and a validation group (n = 94) in a 7:3 ratio. Based on the results of one-factor logistic regression analysis in the modeling group, three machine learning models, namely, logistic regression, decision tree, and neural network, were used to establish a prediction model for pressure injury in emergency patients and compare their prediction effects. The optimal model was selected for external validation of the model.

RESULTS: The incidence of pressure injuries in emergency patients was 8.97 %, 64.52 % of pressure injuries occurred in the sacrococcygeal region, and 64.52 % were staged as stage 1. Serum albumin level, incontinence, perception, and mobility were independent risk factors for pressure injuries in emergency patients (P < 0.05), and the area under the ROC curve of the three models was 0.944-0.959, sensitivity was 91.8-95.5 %, specificity was 72.2-90.9 %, and the Yoden index was 0.677-0.802; the decision tree was the best model that The area under the ROC curve for the validation group was 0.866 (95 % CI: 0.688-1.000), with a sensitivity of 89.8 %, a specificity of 83.3 %, and a Yoden index of 0.731.

CONCLUSIONS: The decision tree model has the best predictive efficacy and is suitable for individualized risk prediction of pressure injuries in emergency medicine specialties, which provides a reference for the prevention and early intervention of pressure injuries in emergency patients.