ArticleRisk management and healthcare policy2026
Development and Validation of a Risk-Prediction Nomogram for Nutritional Risk in Non-Dialysis Chronic Renal Failure Patients.
Article in Risk management and healthcare policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to develop and validate a risk prediction model for malnutrition in non-dialysis chronic renal failure (ND-CRF) patients, and to explore its status and influencing factors. Methods: A total of 421 ND-CRF patients treated at a tertiary general hospital in Zhejiang Province from August 1, 2022, to September 30, 2023, were enrolled. By comparing various indicators between the malnutrition group (119 patients) and the normal nutrition group (302 patients), univariate and multivariate logistic regression analyses were performed to identify influencing factors. A risk prediction model was developed and internally validated using the Bootstrap resampling method. Subsequently, 117 ND-CRF patients from another tertiary hospital in Zhejiang Province (October 1, 2023, to January 31, 2024) were selected for external validation. Calibration plots and decision curve analysis (DCA) were used to assess discrimination and clinical utility. Results: The incidence of malnutrition was 27.70%. The final nomogram incorporated four clinical predictors (gender, serum albumin, glomerular filtration rate, triglycerides) and two psychological predictors (anxiety and depression), with an AUC of 0.795 (95% CI: 0.745-0.844). Internal and external validation yielded AUCs of 0.776 (95% CI: 0.836-0.904) and 0.825 (95% CI: 0.742-0.908), respectively. The calibration curve indicated good agreement between predicted and actual probabilities of malnutrition. DCA demonstrated the model's clinical net benefit across a range of threshold probabilities in both the development and validation cohorts. Conclusion: A nomogram-based risk prediction model for malnutrition in ND-CRF patients has been successfully developed and validated. This model shows good predictive performance and can assist clinicians in early identification of high-risk individuals, providing a foundation for developing and implementing personalized intervention strategies.
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