Construction of a prediction model for the risk of sarcopenia in community and hospitalized elderly patients with chronic diseases
Objective To analyze the factors influencing sarcopenia in older patients with chronic diseases,both in community settings and hospitals,and to develop a risk prediction model for sarcopenia.Methods We recruited a total of 403 older adults with chronic diseases,consisting of 251 individuals from a community in Nanjing,Jiangsu Province,and 152 hospitalized patients from the Department of Geriatrics at Jiangsu Province Hospital.Assessments were conducted using a general information questionnaire,serum sample collection,the mini nutritional assessment-short form(MNA-SF),the mini-mental state examination(MMSE),and the geriatric depression scale(GDS).Binary Logistic regression analysis was employed to identify influencing factors and to construct a risk prediction model for sarcopenia,which was illustrated using a nomogram.The model's discrimination was evaluated using the receiver operating characteristic(ROC)curve and the area under the curve(AUC).Results The prevalence of sarcopenia among community-dwelling older adults with chronic conditions was found to be 4.0％(10/251).In contrast,the prevalence in hospitalized older adults with chronic conditions was significantly higher at 36.2％(55/152).Binary Logistic regression analysis identified several independent risk factors for sarcopenia,including hospitalization(OR=14.391、95％CI:6.284-32.955、P＜0.001),male gender(OR=3.321、95％CI:1.587-6.950、P=0.001),lower low-density lipoprotein cholesterol(LDL-C)levels(OR=2.542、95％CI:1.160-5.572、P=0.020),cognitive impairment(OR=2.654、95％CI:1.269-5.550、P=0.010),and the use of four or more types of medication(OR=2.328、95％CI:1.952-5.689、P=0.044).Based on these risk factors,a nomogram was developed as a predictive model for assessing sarcopenia risk.The AUC for this prediction model was 0.860(95％CI:0.815-0.912),indicating a sensitivity of 0.831 and a specificity of 0.760.Conclusions The incidence of sarcopenia is notably high among older patients with chronic diseases.A risk prediction model that incorporates factors such as hospitalization history,gender,LDL-C levels,cognitive function,and types of medication demonstrates significant potential for predicting sarcopenia.This model serves as a valuable foundation for the early screening and intervention of sarcopenia.
万方数据
维普
