Development and validation of a sarcopenia risk prediction model for community-dwelling older adults

To develop and validate a model that predicts the risk of sarcopenia for community-dwelling older adults. Use of convenience sampling, a total of 1080 elderly people ≥ 60 years old in 10 communities in Zhejiang Province. Lasso regression analysis was used to select predictors, and multivariate logistic regression analysis was used to analysis influencing factors and develop the risk prediction model, and this was presented with nomogram and evaluate its predictive effect. Internal validation of the model was performed using 1000 Bootstrap self-sampling. The prevalence of sarcopenia in community-dwelling older adults was 14.54%. Gender, sleep duration at night, osteoarthropathy, cognitive status score, age, calf circumference and body mass index were influential factors of sarcopenia in community-dwelling elderly. The area under the ROC curve of the prediction model in the training set was 0.909 (95%CI: 0.883–0.934), and in the validation set was 0.873 (95%CI༚0.823–0.924). The Hosmer-Lemeshow test showed that the model had a good fit, calibration curve suggests good calibration, and clinical decision curve showed that the clinical validity was good. The model developed in this study has good discrimination, goodness-of-fit, calibration, and clinical validity, and can provide a convenient method for community healthcare workers and self-monitoring of the elderly, which is of great significance for the early detection, diagnosis, and intervention of sarcopenia in the elderly.