ArticleDiabetes, obesity & metabolism2020
Machine-learning-based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials data.
Article in Diabetes, obesity & metabolism, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 6 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed, 6 syntheses or guidelines pooled it, 80 citations in OpenAlex.
- Risk factors for diabetic kidney disease in type 2 diabetes mellitus in Asia: a 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
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis.BMC medical informatics and decision making · 2022Pooled it
- A study on risk factors for the progression from T2DM to end-stage renal disease based on Mendelian randomization and logistic regression analysis.Renal failure · 2026Article
- Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning.iScience · 2026Article
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Interpretable bioimpedance-based modeling of body composition and metabolic indices related to physical function in maintenance hemodialysis patients.Frontiers in nutrition · 2026Article
- Machine learning model for predicting hypotension following continuous renal replacement therapy initiation in end-stage kidney disease patients: a SHAP-interpretable approach.Frontiers in medicine · 2026Article
- Predicting 1-Year Renal Outcomes in Patients with Diabetic Kidney Disease in CKD Stages 3 to 4: A Multimodal Machine Learning Approach Fusing Clinical Composites and Pathology Images.Research (Washington, D.C.) · 2026Article
- Development and validation of a predictive model for diabetic kidney disease risk in patients with T2DM: a hospital data platform study.Frontiers in endocrinology · 2026Article
- Enhancing end-stage renal disease outcome prediction: a multisourced data-driven approach.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions.Journal of diabetes science and technology · 2025Review
- Predictive ability of visit-to-visit glucose variability on diabetes complications.BMC medical informatics and decision making · 2025Article
- Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage.PeerJ. Computer science · 2025Article
- Noninvasive prediction of T-score in IgA nephropathy using machine learning within the Oxford classification system.Frontiers in endocrinology · 2025Article
- A multi-modal fusion model with enhanced feature representation for chronic kidney disease progression prediction.Briefings in bioinformatics · 2024Article
- A Klotho-Based Machine Learning Model for Prediction of both Kidney and Cardiovascular Outcomes in Chronic Kidney Disease.Kidney diseases (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
Abstract
aimTo predict end-stage renal disease (ESRD) in patients with type 2 diabetes by using machine-learning models with multiple baseline demographic and clinical characteristics. MATERIALS AND
methodsIn total, 11 789 patients with type 2 diabetes and nephropathy from three clinical trials, RENAAL (n = 1513), IDNT (n = 1715) and ALTITUDE (n = 8561), were used in this study. Eighteen baseline demographic and clinical characteristics were used as predictors to train machine-learning models to predict ESRD (doubling of serum creatinine and/or ESRD). We used the area under the receiver operator curve (AUC) to assess the prediction performance of models and compared this with traditional Cox proportional hazard regression and kidney failure risk equation models.
resultsThe feed forward neural network model predicted ESRD with an AUC of 0.82 (0.76-0.87), 0.81 (0.75-0.86) and 0.84 (0.79-0.90) in the RENAAL, IDNT and ALTITUDE trials, respectively. The feed forward neural network model selected urinary albumin to creatinine ratio, serum albumin, uric acid and serum creatinine as important predictors and obtained a state-of-the-art performance for predicting long-term ESRD.
conclusionsDespite large inter-patient variability, non-linear machine-learning models can be used to predict long-term ESRD in patients with type 2 diabetes and nephropathy using baseline demographic and clinical characteristics. The proposed method has the potential to create accurate and multiple outcome prediction automated models to identify high-risk patients who could benefit from therapy in clinical practice.
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What OpenQuestion holds
Registered trials
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.