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Prediction Models for Sarcopenia in Patients With Maintenance Hemodialysis: A Systematic Review and Meta-Analysis
Xiaonv Lin1, Weige Sun2, Jiejing Cheng1
1Emergency Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background:
This systematic review and meta-analysis investigated all prediction models for sarcopenia in maintenance hemodialysis patients.
Methods:
This study used the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement for systematic review. Data sources were PubMed, Web of Science, Embase, Cochrane Library, and Medline databases up to September 2023.
Data Analysis:
risk of bias (ROB) was evaluated using the Prediction model Risk Of Bias ASsessment Tool. Random-effect models were calculated due to high heterogeneity identified.
Results:
Fifteen models from twelve studies were analyzed. All studies had high ROB, and three of them posed a high risk in terms of applicability. The pooled area under the curve (AUC), sensitivity, and specificity were 0.715, 0.583 and 0.656, respectively. The diagnostic criteria (P = .0046), country (P = .0046), and study design (P = .0087) were significant sources of the heterogeneity. Analyzing purely from the data perspective, grouping by diagnostic criteria, the AUC, and specificity [(0.773, 95% CI 0.12-0.99, (0.652, 95% CI 0.641-0.664)] of the Asian Working Group for Sarcopenia group was lower than the European Working Group on Sarcopenia in Older People group [(0.859, 95% CI 0.12-1.00) and (0.874, 95% CI 0.803-0.926)]. Grouping by styles of research, the AUC, sensitivity, and specificity in development group [(0.890, 95% CI 0.16-1.00), (0.751, 95% CI 0.697-0.800), and (0.875, 95% CI 0.854-0.895)] were all higher than the validation group [(0.715, 95% CI 0.09-0.98), (0.550, 95% CI 0.524-0.576), and (0.617, 95% CI 0.604-0.629)].
Conclusions:
Moving forward, there is a critical need to create low-ROB, high-applicability, and more accurate sarcopenia prediction models for maintenance hemodialysis patients customized for diverse global populations.
