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

5 Citations (Scopus)

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

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Controlled Clinical Trials
Medical problems
Chronic Renal Insufficiency
Therapeutics
Clinical Studies

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Ravizza, S., Huschto, T., Adamov, A., Böhm, L., Büsser, A., Flöther, F. F., ... 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

Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. / Ravizza, Stefan; Huschto, Tony; Adamov, Anja; Böhm, Lars; Büsser, Alexander; Flöther, Frederik F.; Hinzmann, Rolf; König, Helena; McAhren, Scott M.; Robertson, Daniel H.; Schleyer, Titus; Schneidinger, Bernd; Petrich, Wolfgang.

In: Nature Medicine, Vol. 25, No. 1, 01.01.2019, p. 57-59.

Research output: Contribution to journalArticle

Ravizza, S, Huschto, T, Adamov, A, Böhm, L, Büsser, A, Flöther, FF, Hinzmann, R, König, H, McAhren, SM, Robertson, DH, 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, vol. 25, no. 1, pp. 57-59. https://doi.org/10.1038/s41591-018-0239-8
Ravizza, Stefan ; Huschto, Tony ; Adamov, Anja ; Böhm, Lars ; Büsser, Alexander ; Flöther, Frederik F. ; Hinzmann, Rolf ; König, Helena ; McAhren, Scott M. ; Robertson, Daniel H. ; Schleyer, Titus ; Schneidinger, Bernd ; Petrich, Wolfgang. / Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. In: Nature Medicine. 2019 ; Vol. 25, No. 1. pp. 57-59.
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