ArticleKidney international reports2022
Development and External Validation of a Machine Learning Model for Progression of CKD.
Article in Kidney international reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 67 citations in OpenAlex.
- Cardiorenal risk stratification in high-risk type 2 diabetes using a simple clinical score: findings from the ELIXA trial.Frontiers in endocrinology · 2026Trial
- Risk of Progression and Costs of Care for Patients With Chronic Kidney Disease.Kidney medicine · 2026Article
- Predicting High-Risk CKD in Japanese People: Model Development and Validation.Kidney international reports · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Machine Learning-Predicted Risk Trajectories for Incident Chronic Kidney Disease and Associations with Post-CKD Outcomes.Journal of medical systems · 2026Article
- Dynamic Bayesian networks to predict loss of kidney function: a cross-institution use case in a large cohort with or at-risk of CKD.BMC medical informatics and decision making · 2026Article
- Responsible Use of Artificial Intelligence to Improve Kidney Care: A Statement from the American Society of Nephrology.Journal of the American Society of Nephrology : JASN · 2026Review
- Cost-Effectiveness Analysis of Artificial Intelligence-Driven Risk Stratification in Patients With Diabetic Kidney Disease in the US Veterans Population.Kidney medicine · 2026Article
- Validation of the Klinrisk Machine Learning Model for CKD Progression in a Large Representative US Population.Journal of the American Society of Nephrology : JASN · 2026Article
- Risk of Progression and Costs of Care for Patients with Type 2 Diabetes and Chronic Kidney Disease.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025Article
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- Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.BMC medical informatics and decision making · 2025Article
- Risk-directed management of chronic kidney disease.Nature reviews. Nephrology · 2025Review
- The Value of Clinical Decision Support in Healthcare: A Focus on Screening and Early Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence approaches to enable early detection of CKD.Nature reviews. Nephrology · 2025Article
- Risk prediction modeling for cardiorenal clinical outcomes in patients with non-diabetic CKD using US nationwide real-world data.BMC nephrology · 2025Article
- Noninvasive prediction of T-score in IgA nephropathy using machine learning within the Oxford classification system.Frontiers in endocrinology · 2025Article
- Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization.PloS one · 2025Article
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Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Prediction of disease progression at all stages of chronic kidney disease (CKD) may help improve patient outcomes. As such, we aimed to develop and externally validate a random forest model to predict progression of CKD using demographics and laboratory data. Methods: The model was developed in a population-based cohort from Manitoba, Canada, between April 1, 2006, and December 31, 2016, with external validation in Alberta, Canada. A total of 77,196 individuals with an estimated glomerular filtration rate (eGFR) > 10 ml/min per 1.73 m Results: The final model achieved an AUC of 0.88 (95% CI 0.87-0.89) at 2 years and 0.84 (0.83-0.85) at 5 years in internal testing. Discrimination and calibration were preserved in the external validation data set with AUC scores of 0.87 (0.86-0.88) at 2 years and 0.84 (0.84-0.86) at 5 years. The top 30% of individuals predicted as high risk and intermediate risk represent 87% of CKD progression events in 2 years and 77% of progression events in 5 years. Conclusion: A machine learning model that leverages routinely collected laboratory data can predict eGFR decline or kidney failure with accuracy.
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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.