Xiaonv Lin, Weige Sun, Jiejing Cheng, Yue Du, Bin Xu
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引用次数: 0
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.
Data sources: 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 (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.
背景：本系统综述和荟萃分析调查了维持性血液透析（MHD）患者肌少症的所有预测模型：本研究采用系统综述与荟萃分析声明（PRISMA）进行系统综述：截至2023年9月的PubMed、Web of Science、Embase、Cochrane Library和Medline数据库：使用预测模型偏倚风险评估工具（PROBAST）评估偏倚风险（ROB）。由于发现的异质性较高，因此计算了随机效应模型：对 12 项研究的 15 个模型进行了分析。所有研究的 ROB 都很高，其中三项研究的适用性风险很高。汇总的AUC、灵敏度和特异性分别为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。单纯从数据角度分析，按诊断标准分组，亚洲肌少症工作组（AWGS）组的AUC和特异性[（0.773，95% CI 0.12-0.99，（0.652，95% CI 0.641-0.664）]低于欧洲老年人肌少症工作组（EWGSOP）组[（0.859，95% CI 0.12-1.00），（0.874，95% CI 0.803-0.926）]。按研究风格分组，发展组的 AUC、灵敏度和特异性[（0.890，95% CI 0.16-1.00），（0.751，95% CI 0.697-0.800），（0.875，95% CI 0.854-0.895）]均高于验证组[（0.715，95% CI 0.09-0.98），（0.550，95% CI 0.524-0.576），（0.617，95% CI 0.604-0.629）]：展望未来，我们亟需为 MHD 患者创建低 ROB、高适用性和更准确的肌少症预测模型，并为全球不同人群量身定制。
期刊介绍：
The Journal of Renal Nutrition is devoted exclusively to renal nutrition science and renal dietetics. Its content is appropriate for nutritionists, physicians and researchers working in nephrology. Each issue contains a state-of-the-art review, original research, articles on the clinical management and education of patients, a current literature review, and nutritional analysis of food products that have clinical relevance.
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