Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
01 2019
01 2019
Historique:
received:
11
12
2017
accepted:
04
10
2018
entrez:
9
1
2019
pubmed:
9
1
2019
medline:
11
5
2019
Statut:
ppublish
Résumé
Diagnostic procedures, therapeutic recommendations, and medical risk stratifications are based on dedicated, strictly controlled clinical trials. However, a plethora of real-world medical data exists, whereupon the increase in data volume comes at the expense of completeness, uniformity, and control. Here, a case-by-case comparison shows that the predictive power of our real world data-based model for diabetes-related chronic kidney disease outperforms published algorithms, which were derived from clinical study data.
Identifiants
pubmed: 30617317
doi: 10.1038/s41591-018-0239-8
pii: 10.1038/s41591-018-0239-8
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
57-59Références
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