Risk prediction models for malnutrition in dialysis patients in China: a systematic review and meta-analysis.
The study found a 41% prevalence of malnutrition in dialysis patients, with eight significant predictors identified for risk assessment. However, existing models have high bias risks that limit their clinical use.
Where it sits
this study against the rest of the vip (vasoactive intestinal polypeptide) corpusSummary and findings
This study evaluated risk prediction models for malnutrition in dialysis patients in China. A total of 12 studies were included, revealing a pooled prevalence of malnutrition at 41%. The analysis identified eight significant predictors of malnutrition risk.
Abstract
Although multiple risk prediction models have been developed to identify malnutrition in dialysis patients, their quality and performance remain unclear, limiting their practicality in current clinical practice and future research. Therefore, we conducted a systematic review and meta-analysis to evaluate these models. Searches were conducted in PubMed, Embase, Web of Science, The Cochrane Library, CINAHL, SinoMed, CNKI, Wanfang, and VIP Database from inception to January 26, 2026. Two investigators independently screened the literature, extracted data, and assessed quality using the Prediction model Risk of Bias Assessment Tool (PROBAST). Meta-analyses of the prevalence of malnutrition, common predictors and model performance were performed using Stata 18.0 and R 4.5.1. A total of 12 eligible studies conducted in China were included, and the pooled prevalence of malnutrition in dialysis patients was 41%. Meta-analysis identified age, serum calcium, Kt/V, triglycerides, sex, vitamin D, NT-proBNP, and comorbid diabetes as statistically significant predictors. The pooled effect of the nine internal validated models was 0.83, indicating good discriminatory performance. However, all included models were rated at high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis. The current analysis reveals a high prevalence of malnutrition among dialysis patients. Eight significant predictors were identified, guiding future selection for constructing predictive models of malnutrition risk in this population. Although existing models demonstrate adequate discriminatory performance, their methodological limitations constrain clinical applicability. Future studies should prioritize the development of standardized, externally validated models to enable early identification and intervention, thereby improving outcomes in this vulnerable group.
Background
This paper addresses the clinical question of malnutrition risk in dialysis patients, an important issue given the high prevalence of malnutrition in this population. Prior studies have developed various risk prediction models, but their quality and performance have not been systematically evaluated. Understanding the effectiveness of these models is crucial for improving clinical practice and patient outcomes.
Methods
The study conducted a systematic review and meta-analysis of risk prediction models for malnutrition in dialysis patients. Searches were performed in multiple databases from inception to January 26, 2026. A total of 12 eligible studies were included, and data were extracted and assessed for quality using the Prediction model Risk of Bias Assessment Tool (PROBAST). Meta-analyses were performed to evaluate the prevalence of malnutrition and model performance.
Results
The primary finding was a pooled prevalence of malnutrition in dialysis patients at 41%. The meta-analysis identified eight statistically significant predictors of malnutrition risk. The pooled effect of the nine internal validated models was 0.83, indicating good discriminatory performance.
Interpretation
The findings suggest that while the existing models show adequate discriminatory performance, the high risk of bias limits their clinical applicability. The identified predictors align with previous literature, but the effect size may not be clinically meaningful without external validation. The methodological limitations of the included studies necessitate caution in applying these findings in practice.
Key findings
- 41% pooled prevalence of malnutrition in dialysis patients.
- Pooled effect of nine internal validated models was 0.83, indicating good discriminatory performance.
- Statistically significant predictors included age, serum calcium, Kt/V, triglycerides, sex, vitamin D, NT-proBNP, and comorbid diabetes.
Limitations
- All models rated at high risk of bias.
- Inappropriate data sources used.
- Poor reporting of analysis methods.
- Short follow-up period for assessing outcomes.