Real-world data successfully used to predict the risk of chronic kidney disease in patients with diabetes, says GlobalData

Chronic kidney disease (CKD) has a low diagnosis rate which will continue through 2026 unless there are more effective ways to diagnose CKD, according to GlobalData, a leading data and analytics company.

A research collaboration between Roche, IBM, and other partners successfully used real-world data (RWD) to calculate the risk of CKD in patients with diabetes. Their new algorithm using RWD, performed better than published algorithms. With a low diagnosis rate of CKD Stages I–IV in both men and women in the seven major markets (7MM*), there is a need to increase early diagnosis of CKD, which the new algorithm may be able to do.


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Nanthida Nanthavong, Associate Epidemiologist at GlobalData, a leading data and analytics company, comments, “The data from the study suggests that RWD and predictive analytics could be used to help recognize the risk of CKD. With the additional help of RWD to identify the risk of CKD in diabetes patients, early preventive treatment can lead to a lower risk of developing CKD.

“The low diagnosis rate of CKD means that there are a high number of people that are not diagnosed and not receiving treatment for the disease. Without proper treatment, CKD can lead to end-stage kidney disease, which can be fatal without a kidney transplant or dialysis.”

Although there are a few issues with RWD, such as quality, completeness, and uniformity, RWD can serve as a useful complement to clinical trial data to provide additional real-world evidence and to promote effective interventions. The development of a new algorithm between Roche and IBM demonstrates that combining multiple data resources and working with partners can advance prevention and treatment of CKD, and possibly other diseases.

*7MM = US, France, Germany, Italy, Spain, UK, and Japan

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