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

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.

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Fig. 1: Results of the predictive algorithms.
Fig. 2: Role of sample size and imputation.

Data availability

Restrictions apply to the general availability of the data because of patient agreements and the nature of patient data. Data were used under license for the study presented in this manuscript. The IBM Explorys database data are run by IBM who makes the data available for secondary use (for example, scientific research) on a commercial basis. The INPC database is owned by the participating health institutions of the INPC. Access to the INPC can be provided for research purposes through the Regenstrief Institute Data Core.

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Acknowledgements

The authors thank O. Quarder, C. Ringemann, P. Stephan (Roche Diabetes Care GmbH, Germany), and H. Mikulski (Roche Diabetes Care Spain, S.L.) for their continuing contributions to this work. We are grateful to T. Beck, S. Chittajallu, and S. Weinert (Roche Diabetes Care, Inc., USA) for their consultancy in the early phase of the investigation. The support from U. Günzel as well as H. Rincker and team (Roche Diabetes Care Deutschland, Germany) is highly appreciated. We are indebted to R. Daikeler, K. Kusterer, S. Waibel, and S. Zink (Germany) for their medical advice concerning our initial results. The research described in this manuscript was funded by Roche Diabetes Care GmbH and supplemented with in-kind contributions from Eli Lilly and Company (S.M.), Indiana Biosciences Research Institute (D.R.), and Regenstrief Institute, Inc. (T.S.).

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Authors

Contributions

S.R., A.A., A.B., and F.F.F. generated and validated the Roche/IBM algorithm. T.H. and H.K. performed independent validation and further analysis. S.M., D.R., T.S., and teams enabled data withdrawal and assessment. B.S., L.B., and R.H. provided consultation for the overall research project, which was led by W.P.

Corresponding author

Correspondence to Wolfgang Petrich.

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Competing interests

The authors declare the following potential conflicts of interest: T.H., B.S., W.P., S.R., and A.B. are inventors of a patent application related to the work described in this manuscript. T.H., H.K., B.S., R.H., and W.P. are employees of Roche Diabetes Care GmbH. S.R., A.A., A.B., L.B., and F.F.F. are employees of IBM Switzerland Ltd. S.M. is an employee of Eli Lilly and Company. Independent of his employment at Roche, W.P. is affiliated with Heidelberg University and is a member of the Faculty of Physics and Astronomy. T.S. is affiliated with Indiana University School of Medicine.

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Ravizza, S., Huschto, T., Adamov, A. et al. Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. Nat Med 25, 57–59 (2019). https://doi.org/10.1038/s41591-018-0239-8

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