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

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.