Development and Validation of a Risk Nomogram Model for Predicting Community-Acquired Pressure Injury Among the Older Adults in China: A Case-Control Study.

Purpose: A predictive model of community-acquired pressure injury (CAPI) was established and validated to allow the early identification of the risk of pressure injuries by family caregivers and community workers. Patients and Methods: The participants were hospitalized patients 65 years and older from two branches of a tertiary hospital in China, one for model training set and the other for validation set. This study was a case-control study based on hospital electronic medical records. According to the presence of pressure injury at admission, patients were divided into a case group and a control group. In the model training set, LASSO regression was used to select the best predictors, and then logistic regression was used to construct a nomogram. The performance of the model was evaluated by drawing the receiver operating characteristic curve (ROC) and calculating the area under the curve (AUC), calibration analysis, and decision curve analysis. The model used a 10-fold crossover for internal and external validation. Results: The study included a total of 20,235 subjects, including 11,567 in the training set and 8668 in the validation set. The prevalence of CAPI in the training and validation sets was 2.5% and 1.8%, respectively. A nomogram was constructed including eight variables: age ≥ 80, malnutrition status, cerebrovascular accidents, hypoproteinemia, respiratory failure, malignant tumor, paraplegia/hemiplegia, and dementia. The AUC of the prediction model in the original model, internal validation, and external validation were 0.868 (95% CI: 0.847, 0.890), mean 0.867, and 0.840 (95% CI: 0.807,03.873), respectively. The nomogram showed acceptable calibration and clinical benefit. Conclusion: We constructed a nomogram to predict CAPI from the perspective of comorbidity that is suitable for use by non-specialists. This nomogram will help family caregivers and community workers with the early identification of PI risks.