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

Stefan Ravizza, Tony Huschto, Anja Adamov, Lars Böhm, Alexander Büsser, Frederik F. Flöther, Rolf Hinzmann, Helena König, Scott M. McAhren, Daniel H. Robertson, Titus Schleyer, Bernd Schneidinger, Wolfgang Petrich

Research output: Contribution to journalArticle

19 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)57-59
Number of pages3
JournalNature Medicine
Volume25
Issue number1
DOIs
StatePublished - Jan 1 2019

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Fingerprint Dive into the research topics of 'Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data'. Together they form a unique fingerprint.

  • Cite this

    Ravizza, S., Huschto, T., Adamov, A., Böhm, L., Büsser, A., Flöther, F. F., Hinzmann, R., König, H., McAhren, S. M., Robertson, D. H., Schleyer, T., Schneidinger, B., & Petrich, W. (2019). Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. Nature Medicine, 25(1), 57-59. https://doi.org/10.1038/s41591-018-0239-8