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).
artificial intelligence
chronic kidney disease (CKD)
end-stage kidney disease (ESKD)
kidney replacement therapy (KRT)
machine learning
naïve Bayes classifiers
precision medicine
risk prediction
Journal
International journal of environmental research and public health
ISSN: 1660-4601
Titre abrégé: Int J Environ Res Public Health
Pays: Switzerland
ID NLM: 101238455
Informations de publication
Date de publication:
30 11 2021
30 11 2021
Historique:
received:
28
10
2021
revised:
21
11
2021
accepted:
25
11
2021
entrez:
10
12
2021
pubmed:
11
12
2021
medline:
15
12
2021
Statut:
epublish
Résumé
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 (
Identifiants
pubmed: 34886378
pii: ijerph182312649
doi: 10.3390/ijerph182312649
pmc: PMC8656741
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
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