Observational studyDiabetologia2021
Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease.
Observational study in Diabetologia, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06101901 (Studying Patterns in Patient Engagement and Participation in Diabetic Kidney Disease Clinical Trials), which is not on this map. Cited by 81 papers, 3 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.
Studying Patterns in Patient Engagement and Participation in Diabetic Kidney Disease Clinical Trials
Who cites it
81 citing papers in PubMed, 3 syntheses or guidelines pooled it, 158 citations in OpenAlex.
- Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis.Endocrine · 2024Pooled it
- Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.Journal of medical systems · 2024Pooled it
- Comprehensive Factors for Predicting the Complications of DiabetesMellitus: A Systematic Review.Current diabetes reviews · 2024Pooled it
- Baseline Risk and Longitudinal Changes in kidneyintelX.dkd and Its Association With Kidney Outcomes in the CANVAS and CREDENCE Trials.Diabetes care · 2026Trial
- Merging artificial intelligence and retinal vascular geometric parameters: A novel tool for diagnosis and prognosis prediction of diabetic nephropathy.Chinese medical journal · 2026Article
- Real-World Associations of KidneyIntelX Risk Stratification With Guideline-Directed Therapy, Kidney Outcomes, and Metabolic Trajectories in Early Diabetic Kidney Disease.Diabetes, obesity & metabolism · 2026Article
- Bibliometrics and visualization analysis: Big data research on diabetic kidney disease from 2000 to 2025.Medicine · 2026Article
- Multi-Modal, Machine Learning-Driven Framework Integrating Multi-Omics for Personalized Chronic Kidney Disease Management.Journal of clinical medicine · 2026Review
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Cost-effectiveness analysis of a prognostic risk assessment for early-stage 1-3b diabetic kidney disease patients in the United States.Cost effectiveness and resource allocation : C/E · 2026Article
- Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning.iScience · 2026Article
- Machine Learning-Predicted Risk Trajectories for Incident Chronic Kidney Disease and Associations with Post-CKD Outcomes.Journal of medical systems · 2026Article
- Interpretable predictive model for deterioration of kidney function in patients with stage 4 cardiovascular-kidney-metabolic syndrome.BMC nephrology · 2026Article
- Emerging Urinary Biomarkers and Innovative Technologies for the Early Detection and Personalized Management of Chronic Kidney Disease.International journal of molecular sciences · 2026Review
- Development and validation of an early risk prediction model based on inflammatory and metabolically derived markers for early diagnosis of diabetic kidney disease.Scientific reports · 2026Article
- Cost-Effectiveness Analysis of Artificial Intelligence-Driven Risk Stratification in Patients With Diabetic Kidney Disease in the US Veterans Population.Kidney medicine · 2026Article
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Diabetic kidney disease: integrating multi-omics insights, artificial intelligence, and novel therapeutics for precision medicine.Frontiers in genetics · 2026Review
- Development and validation of an explainable neural network model for predicting progression in type 2 diabetic kidney disease.Frontiers in endocrinology · 2026Article
- The dual role of ion channels in diabetic kidney disease: a translational paradigm for biomarkers and target discovery - reviews and prospects.American journal of translational research · 2026Review
21 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 2 institutions in 1 country.
Funding
Abstract
aimPredicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers.
methodsThis is an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net reclassification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of ≥5 ml/min per year, ≥40% sustained decline, or kidney failure within 5 years.
resultsIn 1146 patients, the median age was 63 years, 51% were female, the baseline eGFR was 54 ml min
conclusionsKidneyIntelX improved prediction of kidney outcomes over KDIGO and clinical models in individuals with early stages of DKD.
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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.