Conectar
Pressure ulcers represent a significant healthcare challenge among respiratory patients. This study aimed to develop and validate a predictive nomogram based on machine learning algorithms to identify patients at high risk for pressure ulcer development. We conducted a retrospective analysis of 263 respiratory patients (166 with pressure ulcers). Patients were randomly divided into training and testing cohorts at a 7:3 ratio. Potential risk factors were identified through univariate logistic regression. Least absolute shrinkage and selection operator (LASSO) regression selected 17 significant predictors, from which 10 variables with optimal predictive values were incorporated into a nomogram model. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis (DCA). The final nomogram incorporated 10 predictors: age, albumin, C-reactive protein, serum sodium, history of diabetes, chronic obstructive pulmonary disease, peripheral vascular disease, urinary incontinence, length of hospital stay and Braden sensory perception score. The model demonstrated excellent discriminative ability with AUCs of 0.865 (95% CI: 0.816–0.914) in the training cohort and 0.837 (95% CI: 0.783–0.891) in the testing cohort. Calibration curves showed good agreement between predicted and observed probabilities (Hosmer–Lemeshow test: training cohort χ 2 = 4.257, P = 0.833; testing cohort χ 2 = 12.350, P = 0.142). DCA confirmed the nomogram's superior clinical utility compared to individual predictors across a wide range of threshold probabilities. The machine learning–derived nomogram provides a practical, noninvasive tool for early identification of respiratory patients at risk for pressure ulcers. Implementation of this model could facilitate timely intervention strategies, potentially reducing the incidence of pressure ulcers and improving patient outcomes.
FreshRSS 