A collaboration between Roche and IBM has led to the development of a new algorithm that uses real-world data (RWD) to predict the risk of chronic kidney disease (CKD) in patients with diabetes.

The companies, along with Eli Lilly, the Regenstrief Institute, and the Indiana Bioscience Research Institute, conducted a study comparing algorithms using clinical and RWD to calculate the risk of CKD. The study results were recently published in the journal Nature Medicine, which reported that the new algorithm performed better than published algorithms.

The data from the study suggest that RWD and predictive analytics could be used to help recognise the risk of CKD. With a low diagnosis rate of CKD Stages I–IV in both men and women, there is a need to increase early diagnosis of CKD, which the new algorithm may be able to do.

Figure 1 below shows the low diagnosis rate of CKD for the seven major markets (7MM: US, France, Germany, Italy, Spain, UK, and Japan), especially in the early stages. GlobalData epidemiologists estimate that the low diagnosis rate will continue through 2026 unless there are more effective ways to diagnosis CKD.

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

How well do you really know your competitors?

Access the most comprehensive Company Profiles on the market, powered by GlobalData. Save hours of research. Gain competitive edge.

Company Profile – free sample

Thank you!

Your download email will arrive shortly

Not ready to buy yet? Download a free sample

We are confident about the unique quality of our Company Profiles. However, we want you to make the most beneficial decision for your business, so we offer a free sample that you can download by submitting the below form

By GlobalData
Visit our Privacy Policy for more information about our services, how we may use, process and share your personal data, including information of your rights in respect of your personal data and how you can unsubscribe from future marketing communications. Our services are intended for corporate subscribers and you warrant that the email address submitted is your corporate email address.

The National Institutes of Health has reported diabetes as the leading cause of kidney disease. GlobalData epidemiologists have reported that there are a number of risk factors of CKD that are shared with other diseases such as diabetes, hypertension, and cardiovascular disease. Such risk factors include family history, ethnicity, age, obesity, diabetes, hypertension, anemia, and ischemic heart disease.

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. 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.