Construction of a prediction model for sarcopenia risk in patients with coronary heart disease and its validation

ZHAO Jing; WANG Yanzhe; JI Chunxiao; WANG Pingfan; LIU Wencai; LIU Qingning; HAO En'gang; YANG Xiu

doi:10.3969/j.issn.1674-3768.2025.08.001

Abstract：Objective To explore the influencing factors of sarcopenia in discharged coronary heart disease patients,so as to construct a predictive model.Methods A total of 472 patients with coronary heart disease who received treatment in the cardiovascular medicine departments of three general hospitals in Shandong province between June 2023 and July 2023 were selected as the study subjects by convenience sampling.Logistic regression was used to screen for influencing factors,while R 4.1.2 software was employed to construct a risk prediction model and draw a nomogram to visualize the model.Then the predictive performance of the model was validated using the receiver operating characteristics(ROC)and Hosmer-Lemeshow test.Results Among the 330 cases in the modeling group,73 cases developed sarcopenia,with an incidence of 22.12％.Logistic regression analysis showed that age(OR=1.054),Charlson comorbidity index(OR=1.413),body mass index(OR=0.545),albumin(OR=0.485),diabetes(OR=2.144),nutritional status(OR=2.003),NYHA cardiac function grade(OR=1.685)were the influencing factors for sarcopenia in patients with coronary heart disease after discharge.The area under the receiver operating characteristic curve was 0.820 in internal validation and 0.837 in external validation.The Hosmer-Lemeshow test results showed:x2=14.281(P=0.075)for internal validation and x2=7.734(P=0.460)for external validation.Conclusion The risk prediction model for sarcopenia in coronary heart disease patients constructed in this study has a good predictive effect,which can help clinical nursing staff identify high-risk patients early and provide guidance for timely intervention.