{Reference Type}: Journal Article
{Title}: Prediction Models for Sarcopenia in Patients with Maintenance Hemodialysis: A Systematic Review and Meta-Analysis.
{Author}: Lin X;Sun W;Cheng J;Du Y;Xu B;
{Journal}: J Ren Nutr
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jul 10
{Factor}: 4.354
{DOI}: 10.1053/j.jrn.2024.07.005
{Abstract}: <B>BACKGROUND: </B>This systematic review and meta-analysis investigated all prediction models for sarcopenia in Maintenance Hemodialysis (MHD) patients.<BR><B>METHODS: </B>This study used the Systematic Reviews and Meta-Analysis statement (PRISMA) for systematic review.<BR><B>METHODS: </B>PubMed, Web of Science, Embase, Cochrane Library and Medline databases up to September 2023.<BR><B>METHODS: </B>Risk of bias (ROB) was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Random effect models were calculated due to high heterogeneity identified.<BR><B>RESULTS: </B>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 AUC, sensitivity, and specificity were 0.715, 0.583 and 0.656 respectively. The diagnostic criteria (P=0.0046), country (P=0.0046), and study design (P=0.0087) were significant sources of the heterogeneity. Analysing purely from the data perspective, grouping by diagnostic criterias, 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 (AWGS) group was lower than the European Working Group on Sarcopenia in Older People (EWGSOP) group [(0.859, 95% CI 0.12-1.00), (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), (0.875, 95% CI 0.854-0.895)] were all higher than validation group [(0.715, 95% CI 0.09-0.98), (0.550, 95% CI 0.524-0.576), (0.617, 95% CI 0.604-0.629)].<BR><B>CONCLUSIONS: </B>Moving forward, there is a critical need to create low-ROB, high-applicability, and more accurate sarcopenia prediction models for MHD patients, customized for diverse global populations.