Abstract: Objective: To analyze the influencing factors of sarcopenia for elderly patients with chronic diseases in a community, establish a sarcopenia risk prediction model, and verify it. Methods: A total of 460 elderly people with chronic diseases in a community in Linghe District, Jinzhou City from September 2021 to February 2022 were conveniently selected and enrolled. The general information questionnaire, Mini Nutritional Assessment–Short Form (MNA-SF), the self-rated Fall Risk Questionnaire (self-rated FRQ), the Chalder Fatigue Scale (CFS) and the Sarcopenia Five-item Scale were utilized for assessment, and the influencing factors were determined by binary logistic regression analysis. The risk prediction model of sarcopenia was established by R software, and the discrimination and calibration degree of the model were evaluated by ROC curve and calibration curve. Results: The incidence of sarcopenia in elderly patients with chronic diseases in the community was 31.5%. Binary Logistic regression analysis showed that age, exercise habits, number of illnesses, malnutrition, risk of falling and fatigue were independent risk factors for sarcopenia (P<0.05) and a nomogram based on the above risk factors were obtained. The area under the ROC curve was 0.955 (95%CI: 0.937-0.973), Hosmer-Lemeshow test showed that χ2=1.951 (P=0.377). Conclusion: The incidence of sarcopenia in elderly patients with chronic diseases in the community is high, which is affected by age, exercise habits, number of illnesses, malnutrition, risk of falling and fatigue. The prediction model has a certain predictive effect and provides a reference for early screening and intervention.

Key words: elderly; chronic diseases; sarcopenia; risk prediction; nomogram