Observational studyBMC nephrology2024
Predicting chronic kidney disease progression with artificial intelligence.
Observational study in BMC nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies.World journal of nephrology · 2026Review
- Bridging pediatric to adult care in cystinosis: a scoping review of healthcare transition of care protocols and a roadmap for future practice.BMC nephrology · 2026Article
- Managing maternity: Moving care, not patients, using artificial intelligence (AI), internet-of-things (IOT) and point-of-care testing (POCT) devices.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Reimagining kidney value-based care: leveraging data science for dynamic, clinician-level risk prediction.Current opinion in nephrology and hypertension · 2026Review
- AI-powered insights in pediatric nephrology: current applications and future opportunities.Pediatric nephrology (Berlin, Germany) · 2026Review
- Predicting future kidney function in type 2 diabetes mellitus using machine learning and baseline health information.Scientific reports · 2026Article
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Prognostic Value of Claims Data for Chronic Kidney Disease-Associated Health Risks.Kidney & blood pressure research · 2026Article
- Development and validation of a predictive model for rapid renal function decline in early IgA nephropathy: a multicenter retrospective study.Frontiers in medicine · 2026Article
- Stacking ensemble model for predicting chronic kidney disease in the Uddanam region of India with unknown etiology.Scientific reports · 2025Article
- Emerging Biomarkers and Advanced Diagnostics in Chronic Kidney Disease: Early Detection Through Multi-Omics and AI.Diagnostics (Basel, Switzerland) · 2025Review
- Decoding Kidney Pathophysiology: Omics-Driven Approaches in Precision Medicine.Journal of personalized medicine · 2024Review
- Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of tools that allow estimation of the probability of progression of chronic kidney disease (CKD) to advanced stages has not yet achieved significant practical importance in clinical setting. This study aimed to develop and validate a machine learning-based model for predicting the need for renal replacement therapy (RRT) and disease progression for patients with stage 3-5 CKD.
methodsThis was a retrospective, closed cohort, observational study. Patients with CKD affiliated with a private insurer with five-year follow-up data were selected. Demographic, clinical, and laboratory variables were included, and the models were developed based on machine learning methods. The outcomes were CKD progression, a significant decrease in the estimated glomerular filtration rate (eGFR), and the need for RRT.
resultsThree prediction models were developed-Model 1 (risk at 4.5 years, n = 1446) with a F1 of 0.82, 0.53, and 0.55 for RRT, stage progression, and reduction in the eGFR, respectively,- Model 2 (time- to-event, n = 2143) with a C-index of 0.89, 0.67, and 0.67 for RRT, stage progression, reduction in the eGFR, respectively, and Model 3 (reduced Model 2) with C-index = 0.68, 0.68 and 0.88, for RRT, stage progression, reduction in the eGFR, respectively.
conclusionThe time-to-event model performed well in predicting the three outcomes of CKD progression at five years. This model can be useful for predicting the onset and time of occurrence of the outcomes of interest in the population with established CKD.
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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.