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.