Abstract：
BACKGROUND: This systematic review and meta-analysis investigated all prediction models for sarcopenia in Maintenance Hemodialysis (MHD) patients.
METHODS: This study used the Systematic Reviews and Meta-Analysis statement (PRISMA) for systematic review.
METHODS: PubMed, Web of Science, Embase, Cochrane Library and Medline databases up to September 2023.
METHODS: 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.
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 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)].
CONCLUSIONS: 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.
METHODS: This study used the Systematic Reviews and Meta-Analysis statement (PRISMA) for systematic review.
METHODS: PubMed, Web of Science, Embase, Cochrane Library and Medline databases up to September 2023.
METHODS: 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.
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 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)].
CONCLUSIONS: 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.
摘要：
背景：本系统综述和荟萃分析调查了维持性血液透析（MHD）患者的所有肌肉减少症预测模型。
方法：本研究采用系统评价和Meta分析（PRISMA）进行系统评价。
方法：PubMed，WebofScience,Embase,截至2023年9月，Cochrane图书馆和Medline数据库。
方法：使用预测模型偏差风险评估工具（PROBAST）评估偏差风险（ROB）。由于确定的高度异质性，计算了随机效应模型。
结果：分析了来自12项研究的15个模型。所有研究都有高ROB，其中三项在适用性方面存在高风险。合并的AUC，灵敏度,特异性分别为0.715、0.583和0.656。诊断标准(P=0.0046),国家（P=0.0046），和研究设计(P=0.0087)是异质性的重要来源。纯粹从数据角度分析，按诊断标准分组，亚洲肌肉减少症工作组（AWGS）组的AUC和特异性[（0.773，95％CI0.12-0.99，（0.652，95％CI0.641-0.664）]低于欧洲老年人肌肉减少症工作组（EWGSOP）组[（0.859，95％CI0.12-1.00），（0.874，95%CI0.803-0.926）]。按研究风格分组，AUC,灵敏度,和发展组的特异性[（0.890，95%CI0.16-1.00），（0.751，95%CI0.697-0.800），(0.875,95%CI0.854-0.895)]均高于验证组[(0.715,95%CI0.09-0.98),（0.550，95%CI0.524-0.576），（0.617，95%CI0.604-0.629）]。
结论：展望未来，迫切需要创造低ROB，高适用性，和更准确的MHD患者的肌肉减少症预测模型，为不同的全球人口定制。
方法：本研究采用系统评价和Meta分析（PRISMA）进行系统评价。
方法：PubMed，WebofScience,Embase,截至2023年9月，Cochrane图书馆和Medline数据库。
方法：使用预测模型偏差风险评估工具（PROBAST）评估偏差风险（ROB）。由于确定的高度异质性，计算了随机效应模型。
结果：分析了来自12项研究的15个模型。所有研究都有高ROB，其中三项在适用性方面存在高风险。合并的AUC，灵敏度,特异性分别为0.715、0.583和0.656。诊断标准(P=0.0046),国家（P=0.0046），和研究设计(P=0.0087)是异质性的重要来源。纯粹从数据角度分析，按诊断标准分组，亚洲肌肉减少症工作组（AWGS）组的AUC和特异性[（0.773，95％CI0.12-0.99，（0.652，95％CI0.641-0.664）]低于欧洲老年人肌肉减少症工作组（EWGSOP）组[（0.859，95％CI0.12-1.00），（0.874，95%CI0.803-0.926）]。按研究风格分组，AUC,灵敏度,和发展组的特异性[（0.890，95%CI0.16-1.00），（0.751，95%CI0.697-0.800），(0.875,95%CI0.854-0.895)]均高于验证组[(0.715,95%CI0.09-0.98),（0.550，95%CI0.524-0.576），（0.617，95%CI0.604-0.629）]。
结论：展望未来，迫切需要创造低ROB，高适用性，和更准确的MHD患者的肌肉减少症预测模型，为不同的全球人口定制。
