ArticleFrontiers in nutrition2024
Identification of independent risk factors for hypoalbuminemia in patients with CKD stages 3 and 4: the construction of a nomogram.
Article in Frontiers in nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Development and validation of a machine learning-based risk prediction model for hyperkalemia in patients with chronic kidney disease.Scientific reports · 2026Article
- Early prognostication of septic shock in Korean adults aged 80 years and over: serum albumin combined with Sequential Organ Failure Assessment Score.Acute and critical care · 2026Article
- Article
- Construction and validation of a cross-sectional risk classification model for hypoproteinemia in single-center maintenance hemodialysis patient.Scientific reports · 2025Article
- Identification of shared lactylation-related gene signatures between osteoporosis and chronic kidney disease.Frontiers in cell and developmental biology · 2025Article
- Development and validation of nursing-oriented risk prediction models for anxiety and depression in hospitalized patients with chronic kidney disease: a retrospective cross-sectional study in Southwest China.Frontiers in psychiatry · 2025Article
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Abstract
Background: Hypoalbuminemia is a common complication in patients with chronic kidney disease (CKD) and is associated with various adverse clinical events. Currently, there are few studies focused on identifying risk factors and constructing models for hypoalbuminemia in patients with CKD stages 3 and 4. This study aims to identify independent risk factors for hypoalbuminemia in patients with CKD stages 3 and 4 and construct a nomogram for predicting the risk of hypoalbuminemia in these patients. Methods: A total of 237 patients with CKD stages 3 and 4 treated at Anning First People's Hospital from January to December 2023 were included. Univariate and bidirectional stepwise multivariate logistic regression analyses were used to identify independent risk factors for hypoalbuminemia in these patients. Based on the results of logistic regression analyses, a nomogram was constructed. The model performance was assessed using area under the receiver operator characteristic curve, calibration curves, and decision curve analysis. Results: Hemoglobin, red blood cells, serum sodium, and serum calcium were identified as independent risk factors for hypoalbuminemia in these patients. The contributions of each independent risk factor to hypoalbuminemia were visualized in a nomogram. The area under the receiver operator characteristic curve of the model was 0.819, indicating good discrimination. The calibration curves showed good agreement between predicted and observed outcomes. The decision curve analysis also verified that the model had the good clinical utility. Conclusion: Hemoglobin, red blood cells, serum sodium, and serum calcium were identified as independent risk factors of hypoalbuminemia in patients with CKD stages 3 and 4. The nomogram exhibits good discrimination, calibration, and clinical utility, offering a reliable tool for the early prediction and identification of hypoalbuminemia in these patients.
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