ArticleInternational journal of environmental research and public health2021
Validation of a Novel Predictive Algorithm for Kidney Failure in Patients Suffering from Chronic Kidney Disease: The Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD).
Article in International journal of environmental research and public health, 2021. 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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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Representation of multimorbidity and frailty in the development and validation of kidney failure prognostic prediction models: a systematic review.BMC medicine · 2024Pooled it
- Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.Diagnostics (Basel, Switzerland) · 2026Review
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- The prognostic reasoning system for chronic kidney disease progression (PROGRES-CKD) may help improve waiting list management for outpatient nephrology services in a second-level public hospital in Italy.Journal of nephrology · 2025Article
- Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.Hypertension (Dallas, Tex. : 1979) · 2025Review
- Identification of progression-related genes and construction of prognostic model for chronic kidney disease by machine learning.Frontiers in cell and developmental biology · 2025Article
- Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation.Frontiers in medicine · 2025Article
- Development and validation of a machine learning model to predict time to renal replacement therapy in patients with chronic kidney disease.BMC nephrology · 2024Article
- From electronic health records to clinical management systems: how the digital transformation can support healthcare services.Clinical kidney journal · 2023Review
- Chronic kidney disease-associated pruritus (CKD-aP) is associated with worse quality of life and increased healthcare utilization among dialysis patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023Article
- Data Science in Healthcare: COVID-19 and Beyond.International journal of environmental research and public health · 2022Article
- Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.Frontiers in public health · 2022Article
- The Cardiovascular Literature-Based Risk Algorithm (CALIBRA): Predicting Cardiovascular Events in Patients With Non-Dialysis Dependent Chronic Kidney Disease.Frontiers in nephrology · 2022Article
Corrections and comments
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Authors and funding
16 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current equation-based risk stratification algorithms for kidney failure (KF) may have limited applicability in real world settings, where missing information may impede their computation for a large share of patients, hampering one from taking full advantage of the wealth of information collected in electronic health records. To overcome such limitations, we trained and validated the Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD), a novel algorithm predicting end-stage kidney disease (ESKD). PROGRES-CKD is a naïve Bayes classifier predicting ESKD onset within 6 and 24 months in adult, stage 3-to-5 CKD patients. PROGRES-CKD trained on 17,775 CKD patients treated in the Fresenius Medical Care (FMC) NephroCare network. The algorithm was validated in a second independent FMC cohort (
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