Malnutrition risk prediction models in patients with chronic heart failure: a systematic review and Meta-analysis

Objective To systematically review risk prediction models for malnutrition in patients with chronic heart failure (CHF), and to provide evidence for model optimization and clinical practice.Methods Databases including PubMed, The Cochrane Library, Embase, Web of Science, CNKI, Wanfang, VIP, and SinoMed were searched from inception to March 2025 for studies on malnutrition risk prediction models in CHF patients. Model quality was assessed using the PROBAST tool, and Meta analysis of predictors was performed using R version 4.4.1.Results Nine studies reporting nine models were included. All models were rated as having high risk of bias and good applicability. The area under the receiver operating characteristic curve (AUC) of the models ranged from 0.730 to 0.996. The pooled AUC was 0.84 [95%CI (0.79, 0.90)]. Meta analysis identified the following independent predictors of malnutrition risk in CHF patients: age, smoking, edema, and C reactive protein (P