ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2022
Development and External Validation of a Nomogram and a Risk Table for Prediction of Type 2 Diabetic Kidney Disease Progression Based on a Retrospective Cohort Study in China.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis.Frontiers in endocrinology · 2024Pooled it
- Vitamin D deficiency and metabolic disorders increase albuminuria risk in type 2 diabetes (ACR 0.1-300 mg/g): a nomogram-based stratification.Frontiers in endocrinology · 2026Article
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- Review
- Establishment and comparison of prediction models for early-stage diabetic kidney disease.Digital healthArticle
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9 authors.
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Abstract
Purpose: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. Risk assessment provides information about patient prognosis, contributing to the risk stratification of patients and the rational allocation of medical resources. We aimed to develop a model for individualized prediction of renal function decline in patients with type 2 DKD (T2DKD). Patients and Methods: In a retrospective observational study, we followed 307 T2DKD patients and evaluated the determinants of 1) risk of doubling in serum creatinine (Scr), 2) risk of eGFR<15 mL/min/1.73m Results: Four predictors were selected to establish the final model: Scr, urinary albumin/creatinine ratio, plasma albumin, and insulin treatment. The nomogram achieved satisfactory prediction performance, with a C-index of 0.791 [95% confidence interval (CI) 0.762-0.820] in the derivation cohort and 0.793 (95% CI 0.746-0.840) in the external validation cohort. Then, all predictors were scored according to their weightings. A risk table with the highest score of 11.5 was developed. The C-index of the risk table was 0.764 (95% CI: 0.731-0.797), which was similar to the external validation cohort (0.763; 95% CI: 0.714-0.812). Additionally, the patients were divided into two groups based on the risk table, and significant differences in the probability of outcome events were observed between the high-risk (score >2) and low-risk (score ≤2) groups in the derivation and external validation cohorts ( Conclusion: The nomogram and the risk table using readily available clinical parameters could be new tools for bedside prediction of renal function decline in T2DKD patients.
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