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

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


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Area Under Curve
  • Data Analysis*
  • Diabetes Mellitus / diagnosis*
  • Humans
  • Prognosis
  • Renal Insufficiency, Chronic / complications*
  • Renal Insufficiency, Chronic / diagnosis*
  • Sample Size