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已发表论文
非透析慢性肾衰竭患者营养风险预测列线图的开发与验证
Authors Yu C, Xu F, Lin Q, Li L
Received 15 September 2025
Accepted for publication 26 January 2026
Published 7 February 2026 Volume 2026:19 567783
DOI https://doi.org/10.2147/RMHP.S567783
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Keon-Hyung Lee
Chenxin Yu,1 Fei Xu,2 Qin Lin,3 Ling Li3
1The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, People’s Republic of China; 2School of Nursing, Zhejiang Chinese Medical University, Hangzhou, 310053, People’s Republic of China; 3School of Nursing, Zhejiang Shuren University, Hangzhou, 310015, People’s Republic of China
Correspondence: Ling Li, School of Nursing, Zhejiang Shuren University, No. 8 Shuren Street, Gongshu District, Hangzhou, 310015, People’s Republic of China, Tel +86 17364527138, Email 59788937@qq.com
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.
Keywords: non-dialysis chronic renal failure, malnutrition, model development, model validation, predictive model
1The Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, People’s Republic of China; 2School of Nursing, Zhejiang Chinese Medical University, Hangzhou, 310053, People’s Republic of China; 3School of Nursing, Zhejiang Shuren University, Hangzhou, 310015, People’s Republic of China
Correspondence: Ling Li, School of Nursing, Zhejiang Shuren University, No. 8 Shuren Street, Gongshu District, Hangzhou, 310015, People’s Republic of China, Tel +86 17364527138, Email 59788937@qq.com
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.
Keywords: non-dialysis chronic renal failure, malnutrition, model development, model validation, predictive model