Screening Accuracy of SARC-F for Sarcopenia in the Elderly: A Diagnostic Meta-Analysis
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SCREENING ACCURACY OF SARC-F FOR SARCOPENIA IN THE ELDERLY: A DIAGNOSTIC META-ANALYSIS J.-L. LU1,*, L.-Y. DING1,*, Q. XU1, S.-Q. ZHU1, X.-Y. XU2, H.-X. HUA1, L. CHEN3, H. XU3 1. School of Nursing, Nanjing Medical University, Nanjing, China; 2. Faculty of Health, Queensland University of Technology, Brisbane, Australia; 3. Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China; * These authors are co-first author. Corresponding author: Qin Xu, Professor of Nursing, School of Nursing, Nanjing Medical University, 140 Hanzhong Road, Gulou District, Nanjing, China (211166), E-mail: [email protected]; Shu-qin Zhu, Associate Professor of Nursing, School of Nursing, Nanjing Medical University, 140 Hanzhong Road, Gulou District, Nanjing, China (211166), E-mail: [email protected]
Abstract: Background: Sarcopenia is an age-related disease, which is characterized by a decline in muscle mass and function. It is one of the most important health issues in the elderly and often leads to a high rate and variety of adverse outcomes. Objectives: To evaluate the screening accuracy of SARC-F for sarcopenia in the elderly. Design: We conducted a meta-analysis using articles available in 6 databases including PubMed (Medline), Web of Science, Embase, Cochrane Controlled Register of Trials (CENTRAL), China Knowledge Resource Integrated Database (CNKI), and Wanfang databases from inception to May 2020. Participants: Adults aged 60 years and older. Measurements: Sarcopenia was defined by EWGSOP2, EWGSOP, AWGS, FNIH and IWGS. Two authors independently extracted data based on predefined criteria. Where data were available we calculated pooled summary estimates of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and their 95% confidence interval (CI) based on different criteria using the hierarchical logistic regression modeling including bivariate modeling and hierarchical summary receiver operating characteristic (HSROC) modeling. Results: We included 20 studies, with the prevalence of sarcopenia ranging from 6.42% to 21.56%. The number of the literatures using EWGSOP, EWGSOP2, AWGS, IWGS and FNIH as diagnostic criteria was 13, 4, 13, 8, 7, respectively. Bivariate analysis yielded a pooled sensitivity of 32% (95%CI:19%-47%), 77% (95%CI: 49%-92%), 27% (95%CI: 16%-42%), 39% (95%CI: 27%-52%), 35% (95%CI: 23%-49%) and a pooled specificity of 86% (95%CI:77%-92%), 63% (95%CI: 43%-79%), 91% (95%CI: 85%-95%), 86% (95%CI: 76%-92%), 89% (95%CI: 81%-93%), respectively. The area under the HSROC curve were 0.68 (95%CI: 0.64-0.72), 0.75 (95%CI: 0.71-0.78), 0.73 (95%CI: 0.69-0.77), 0.67 (95%CI: 0.62-0.71), 0.70 (95%CI: 0.65-0.73), respectively. Conclusions: The screening accuracy of SARC-F was various based on different diagnostic criteria. There were some limitations for SARC-F, however, considering the higher practicability and specificity for screening sarcopenia in practice, SARC-F was still an effective screening tool for sarcopenia
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