“The strength of our study compared to previous research on the topic lies not only in the refined methodology, but also in the large amount of data. This allows us to attain a high level of confidence in our findings,” says first author Mariella Gregorich. “Accordingly, the prediction tool proves to be reliable and able to predict a decline in kidney function based on eGFR for up to five years after baseline.” However, the study has also revealed that the individual course depends on other, still unknown factors.

Early recognition and therapy management
Chronic kidney disease (CKD) is one of the most common complications of diabetes and the most common cause of kidney failure that requires dialysis. Since CKD does not have any symptoms in the early stages, it is often recognised only when the decline in kidney function is already very advanced. Through early detection and consistent therapy management, especially in diabetic metabolic and blood pressure control, kidney damage can be delayed or prevented. Currently, kidney function in persons with diabetes is mainly monitored by the regular measurement of eGFR. “Our prediction tool can assist in the continuous monitoring of disease progression and allow the identification of patients with an increased risk of worsening kidney function in the coming years,” says study leader Rainer Oberbauer, highlighting the significant clinical relevance of the prediction tool. A web-adapted version of the model is already under construction and will soon be available for further, independent validation: https://beatdkd.shinyapps.io/shiny/

Full bibliographic information
Published on 24/04/2023 by the Medical University of Vienna
Development and Validation of a Prediction Model for Future Estimated Glomerular Filtration Rate in People With Type 2 Diabetes and Chronic Kidney Disease
Mariella Gregorich, Michael Kammer, Andreas Heinzel, Carsten Böger, Kai-Uwe Eckardt, Hiddo Lambers Heerspink, Bettina Jung, Gert Mayer, Heike Meiselbach, Matthias Schmid, Ulla T. Schultheiss, Georg Heinze, Rainer Oberbauer, for the BEAt-DKD Consortium
Author Affiliations Article Information
doi: Https://doi.org/10.1001/jamanetworkopen.2023.1870