Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data
Published in Nature Medicine, 2019
Using real-world medical data poses challenges due to variability, incompleteness, and lack of standardization. This study demonstrates that a real-world based predictive model for diabetes-associated chronic kidney disease outperforms algorithms developed solely from clinical trial data.
Recommended citation: 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.
Download Paper