Association Between Nutritional Status and Severe Sarcopenia in Maintenance Hemodialysis Patients: A Multicenter...

Sarcopenia is highly prevalent in maintenance hemodialysis (MHD) patients and is associated with adverse outcomes, yet simple bedside tools to identify those with severe sarcopenia remain scarce. We aimed to develop and internally validate a nomogram-based diagnostic model for severe sarcopenia using routine nutritional indicators.

This multicenter cross-sectional study enrolled 131 MHD patients from two Chinese tertiary hospitals (2024-2026). Sarcopenia was classified by AWGS 2019 criteria into non‑sarcopenia (n=68), sarcopenia (n=29), and severe sarcopenia (n=34). Nutritional parameters included albumin, BMI, PNI, and NRS2002. Ordinal logistic regression, restricted cubic splines, and subgroup analyses were performed. A combined diagnostic model was built with binary logistic regression and validated by bootstrapping. Performance was assessed via ROC, calibration, DCA, and incremental metrics (NRI/IDI).

Higher BMI was independently associated with lower odds of more severe sarcopenia (OR per 1 kg/m²=0.79, 95% CI:0.68--0.92, P=0.002). Individual nutritional indicators showed modest discrimination (AUCs 0.547-0.743), with BMI having the highest single‑indicator AUC, but none was sufficient to reliably identify severe sarcopenia at the bedside. In contrast, the combined model achieved an AUC of 0.809 for severe sarcopenia, with good calibration (Brier=0.119) and net clinical benefit. Center B was associated with substantially higher odds of severe sarcopenia compared with Center A (OR = 54.5, 95% CI: 17.5-169.8, P < 0.001), although this extreme estimate should be interpreted with caution given the marked imbalance in patient composition between centers and the wide confidence interval. The negative predictive value was 93.4%. Adding nutritional indicators to age and sex significantly improved discrimination (IDI=0.058, P=0.006).

A simple nomogram incorporating age, sex, albumin, BMI, and NRS2002 demonstrated good discriminative ability for identifying severe sarcopenia in MHD patients in our internal validation and may serve as a potential tool for bedside risk stratification by nurses. However, external validation is warranted before clinical implementation.