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Nat Med. 2019 Jan;25(1):57-59. doi: 10.1038/s41591-018-0239-8. Epub 2019 Jan 7.

Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data.

Author information

1
IBM Switzerland Ltd., Zurich, Switzerland.
2
Roche Diabetes Care GmbH, Mannheim, Germany.
3
Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA.
4
Indiana Biosciences Research Institute, Indianapolis, IN, USA.
5
Regenstrief Institute, Inc., Indianapolis, IN, USA.
6
Roche Diabetes Care GmbH, Mannheim, Germany. wolfgang.petrich@roche.com.

Abstract

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.

PMID:
30617317
DOI:
10.1038/s41591-018-0239-8

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