ArticleRenal failure2022
Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease.
Article in Renal failure, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 4 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 4 syntheses or guidelines pooled it, 101 citations in OpenAlex.
- Prediction models for progression from diabetic kidney disease to end-stage renal disease: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Representation of multimorbidity and frailty in the development and validation of kidney failure prognostic prediction models: a systematic review.BMC medicine · 2024Pooled it
- Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis.Endocrine · 2024Pooled it
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle-aged and older US people with prediabetes or diabetes.Clinical cardiology · 2023Trial
- Article
- Merging artificial intelligence and retinal vascular geometric parameters: A novel tool for diagnosis and prognosis prediction of diabetic nephropathy.Chinese medical journal · 2026Article
- Alteration in adenosine monophosphate deaminase activity and ferric reducing capacity in diabetic renal failure patients.Journal, genetic engineering & biotechnology · 2026Article
- Predicting acute and chronic diabetes complications among adults newly diagnosed with diabetes: development and validation of dynamic risk prediction models.Nature communications · 2026Article
- Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning.iScience · 2026Article
- Development and validation of a nomogram for predicting advanced chronic kidney disease.iScience · 2026Article
- Multicenter study of the diagnostic value of erythrocyte morphology assessment by the EH-2090 for differentiation of glomerular and non-glomerular hematuria.BMC nephrology · 2026Article
- Endocrine disorders linked to chronic kidney disease: Mechanisms and clinical implications.World journal of nephrology · 2026Review
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- A nomogram prediction model incorporating noninvasive lens AGEs and conventional biochemical indicators for assessing and predicting diabetic kidney disease.Scientific reports · 2026Article
- TGF-β1 Gene Polymorphisms in Saudi Patients With Type 2 Diabetes With or Without Diabetic Nephropathy.International journal of endocrinology · 2026Article
- Potential Relationship Between Macrophage Inflammatory Protein-1β and Diabetic Kidney Disease: A Multi-Model Study.International journal of general medicine · 2026Article
- Integrated multi-omics analysis unveils microbiota-metabolite-host interactions and novel biomarkers for early diabetic kidney disease diagnosis.Frontiers in immunology · 2026Article
- Development and external validation of a machine learning model for predicting in-hospital mortality in ICU patients with diabetic kidney disease: a study utilizing the MIMIC database and a Chinese cohort.Frontiers in endocrinology · 2026Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsDiabetic kidney disease (DKD) is the most common cause of end-stage renal disease (ESRD) and is associated with increased morbidity and mortality in patients with diabetes. Identification of risk factors involved in the progression of DKD to ESRD is expected to result in early detection and appropriate intervention and improve prognosis. Therefore, this study aimed to establish a risk prediction model for ESRD resulting from DKD in patients with type 2 diabetes mellitus (T2DM).
methodsBetween January 2008 and July 2019, a total of 390 Chinese patients with T2DM and DKD confirmed by percutaneous renal biopsy were enrolled and followed up for at least 1 year. Four machine learning algorithms (gradient boosting machine, support vector machine, logistic regression, and random forest (RF)) were used to identify the critical clinical and pathological features and to build a risk prediction model for ESRD.
resultsThere were 158 renal outcome events (ESRD) (40.51%) during the 3-year median follow up. The RF algorithm showed the best performance at predicting progression to ESRD, showing the highest AUC (0.90) and ACC (82.65%). The RF algorithm identified five major factors: Cystatin-C, serum albumin (sAlb), hemoglobin (Hb), 24-hour urine urinary total protein, and estimated glomerular filtration rate. A nomogram according to the aforementioned five predictive factors was constructed to predict the incidence of ESRD.
conclusionMachine learning algorithms can efficiently predict the incident ESRD in DKD participants. Compared with the previous models, the importance of sAlb and Hb were highlighted in the current model.Highlights
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.