ArticleScientific reports2024
Establishment and evaluation of a nomogram prediction model for the risk of vascular calcification in stage 5 chronic kidney disease patients.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Development and validation of a nomogram for predicting calcification of arteriovenous access in hemodialysis patients.Renal failure · 2025Article
- Diabetes mediates the association between uric acid to high-density lipoprotein cholesterol ratio and abdominal aortic calcification: a cross-sectional study.Scientific reports · 2025Article
- Establishment of an Early Prediction Model for Severe Fever With Thrombocytopenia Syndrome-Associated Encephalitis.Immunity, inflammation and disease · 2024Article
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10 authors at 1 institution in 1 country.
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Abstract
Vascular calcification (VC) is a common complication of chronic kidney disease (CKD) that has a detrimental effect on patients' survival and prognosis. The aim of this study was to develop and validate a practical and reliable prediction model for VC in CKD5 patients. The medical records of 544 CKD5 patients were reviewed retrospectively. Multivariate logistic regression analysis was used to identify the independent risk factors for vascular calcification in patients with CKD5 and then created a nomogram prediction model. The area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow test, and decision curve analysis (DCA) were used to assess model performance. The patients were split into groups with normal and high serum uric acid levels, and the factors influencing these levels were investigated. Age, BUN, SUA, P and TG were independent risk factors for vascular calcification in CKD5 patients in the modeling group (P < 0.05). In the internal validation, the results of model showed that the AUC was 0.917. No significant divergence between the predicted probability of the nomogram and the actual incidence rate (x
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