Key result
Predictive models for sarcopenia in dialysis patients demonstrated area under the curve values between 0.81 and 0.95 in derivation sets and 0.78 to 0.93 in external validation sets, though all 13 included studies exhibited a high risk of bias.
Why the study?
Many predictive models for sarcopenia risk in dialysis patients have been developed or validated, but the quality of model development and applicability remain unclear.
Are currently available predictive models for sarcopenia in dialysis patients accurate and of high quality?
Systematic Review
Are currently available predictive models for sarcopenia in dialysis patients accurate and of high quality?
Existing predictive models for sarcopenia in dialysis patients show good discrimination but suffer from high risk of bias, highlighting the need for rigorous model development and validation.
High-bias models warrant caution before clinical use in dialysis; rigorous low-bias development and validation studies are needed.
BACKGROUND: Many studies have developed or validated predictive models to estimate the risk of sarcopenia in dialysis patients, but the quality of model development and the applicability of the models remain unclear. OBJECTIVE: To systematically review and critically evaluate currently available predictive models for sarcopenia in dialysis patients. METHODS: We systematically searched five databases until March 2024. Observational studies that developed or validated predictive models or scoring systems for sarcopenia in dialysis patients were considered eligible. We included studies of adults (≥ 18 years of age) on dialysis and excluded studies that did not validate the predictive model. Data extraction was performed independently by two authors using a standardized data extraction table based on a checklist of key assessments and data extraction for systematic evaluation of predictive modeling research. The quality of the model was assessed using the Predictive Model Risk of Bias Assessment Tool. RESULTS: Of the 104,454 studies screened, 13 studies described 13 predictive models. The incidence of sarcopenia in dialysis patients ranged from 6.6 to 34.4%. The most commonly used predictors were age and body mass index. In the derivation set, the reported area under the curve or C-statistic is between 0.81 and 0.95. The area under the curve reported by the external validation set is between 0.78 and 0.93. All studies had a high risk of bias, mainly due to poor reporting in the outcome and the analysis domains, and three studies had a high risk of bias in terms of applicability. CONCLUSION: Future research should focus on validating and improving existing predictive models or developing new models using rigorous methods.
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Hou et al. (2025) conducted a systematic review in Sarcopenia in dialysis patients. Prediction models for sarcopenia was evaluated on Model discrimination (Area under the curve or C-statistic). Predictive models for sarcopenia in dialysis patients demonstrated area under the curve values between 0.81 and 0.95 in derivation sets and 0.78 to 0.93 in external validation sets, though all 13 included studies exhibited a high risk of bias.
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