ArticleDiabetes care2013
Derivation and validation of a renal risk score for people with type 2 diabetes.
Article in Diabetes care, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 7 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 7 syntheses or guidelines pooled it, 87 citations in OpenAlex.
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- External validation of prognostic models for chronic kidney disease among type 2 diabetes.Journal of nephrology · 2022Pooled it
- Derivation and Validation of a Prediction Model of End-Stage Renal Disease in Patients With Type 2 Diabetes Based on a Systematic Review and Meta-analysis.Frontiers in endocrinology · 2022Pooled it
- Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis.Systematic reviews · 2021Pooled it
- Sodium-glucose cotransporter protein-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists for type 2 diabetes: systematic review and network meta-analysis of randomised controlled trials.BMJ (Clinical research ed.) · 2021Pooled it
- Pooled it
- Pooled it
- Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients.Scientific reports · 2019Trial
- Evaluation of FABP1 as a biomarker of diabetic kidney disease in children with type 1 diabetes.European journal of pediatrics · 2026Article
- Comparative performance of risk prediction models for kidney disease: an external validation using 0.5 million UK Biobank participants.BMC nephrology · 2026Article
- Diabetic kidney disease, biomarkers, and finerenone.Diabetes, obesity & metabolism · 2026Review
- A Nomogram for End-Stage Renal Disease Prediction in Patients with Type 2 Diabetes Mellitus: A Nationwide Cohort Study in Korea.Endocrinology and metabolism (Seoul, Korea) · 2026Article
- Using machine learning algorithms to predict MACE in peritoneal dialysis patients.Scientific reports · 2026Article
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Prognostic models for cardiovascular and kidney outcomes in people with type 2 diabetes: living systematic review and meta-analysis of observational studies.BMJ medicine · 2025Article
- Multi-feature integrated machine learning prediction model for early nephropathy in elderly living with type 2 diabetes mellitus.Frontiers in endocrinology · 2025Article
- Article
- Construction of Risk Prediction Model of Type 2 Diabetic Kidney Disease Based on Deep Learning (Diabetes Metab J 2024;48:771-9).Diabetes & metabolism journal · 2024Article
- Strategies to address diabetic kidney disease burden in Mexico: a narrative review by the Mexican College of Nephrologists.Frontiers in medicine · 2024Review
- Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study.Frontiers in endocrinology · 2023Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDiabetes has become the leading cause of end-stage renal disease (ESRD). Renal risk stratification could assist in earlier identification and targeted prevention. This study aimed to derive risk models to predict ESRD events in type 2 diabetes in primary care. RESEARCH DESIGN AND
methodsThe nationwide derivation cohort included adults with type 2 diabetes from the New Zealand Diabetes Cohort Study initially assessed during 2000-2006 and followed until December 2010, excluding those with pre-existing ESRD. The outcome was fatal or nonfatal ESRD event (peritoneal dialysis or hemodialysis for ESRD, renal transplantation, or death from ESRD). Risk models were developed using Cox proportional hazards models, and their performance was assessed in a separate validation cohort.
resultsThe derivation cohort included 25,736 individuals followed for up to 11 years (180,497 person-years; 86% followed for ≥5 years). At baseline, mean age was 62 years, median diabetes duration 5 years, and median HbA1c 7.2% (55 mmol/mol); 37% had albuminuria; and median estimated glomerular filtration rate (eGFR) was 77 mL/min/1.73 m2. There were 637 ESRD events (2.5%) during follow-up. Models that included sex, ethnicity, age, diabetes duration, albuminuria, serum creatinine, systolic blood pressure, HbA1c, smoking status, and previous cardiovascular disease status performed well with good discrimination and calibration in the derivation cohort and the validation cohort (n=5,877) (C-statistics 0.89-0.92), improving predictive performance compared with previous models.
conclusionsThese 5-year renal risk models performed very well in two large primary care populations with type 2 diabetes. More accurate risk stratification could facilitate earlier intervention than using eGFR and/or albuminuria alone.
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