Dr Kunihiro Matsushita Lays Out the Benefits of the CKD Patch Approach

December 31, 2020

This approach allows clinicians to keep using existing prediction models and incorporate chronic kidney disease (CKD) data seamlessly to calibrate patients' risks, said Kunihiro Matsushita, MD, an associate professor in the Department of Epidemiology and Division of Cardiology at Johns Hopkins University.

This approach allows clinicians to keep using existing prediction models and incorporate (chronic kidney disease (CKD) data seamlessly to calibrate patients' risks, said Kunihiro Matsushita, MD, an associate professor in the Department of Epidemiology and Division of Cardiology at Johns Hopkins University.

Transcript

What are some of the barriers to using cardiovascular risk prediction tools in patients with kidney disease?

Probably many clinicians and researchers would agree that chronic kidney disease is a risk factor for cardiovascular disease. However, major clinical guidelines do not have a standard approach to incorporate inflammation of chronic kidney disease in their cardiovascular risk prediction system. For example, the American Heart Association and American College of Cardiology acknowledge reduced kidney function as a risk enhancing factor. But their guidelines do not specify how to incorporate and how to calibrate the risk depending on the level of kidney function. On the other hand, the European Society of Cardiology guidelines define an estimated glomerular filtration rate (eGFR) less than 30 as a very high risk condition and eGFR between 30 and 59 as a high risk condition. That approach does not necessarily take into account other factors together with kidney function. So, it may not be an ideal approach.

What are the benefits of the CKD patch approach?

We came up with this idea of chronic kidney disease patch, CKD patch, to allow clinicians to keep using the existing prediction models, such as the pooled cohort equation in the American Heart Association guidelines, and then incorporate chronic kidney disease data seamlessly for calibrating the risk of the patient in front of them. So, that's why it has the name of patch, because it's not a stand alone risk prediction tool. It's specifically designed to be added on top of existing prediction models. For example, the clinicians can calculate the risk of cardiovascular disease for a specific patient with information on traditional risk factors such as age, diabetes, blood pressure. Then the clinicians can get a specific number based on, for example, the pooled cohort equation. But if it happens that the physicians have information on kidney disease, such as eGFR or albuminuria, then, by using our patch, the physicians can enhance the predictive risk based on information on kidney disease.

"To facilitate the uptake of new prediction models, implementation needs to be done strategically." Can you elaborate on this?

I think probably the most important part is to present the risk prediction model together with actions to be taken. I think it's important for researchers to try to have conversations with clinical guidelines groups and if the guidelines also can consider incorporating this prediction model and specific recommendations. I think that will be helpful for clinicians to use risk reduction models, because the information from risk reduction models can actually direct or guide the next clinical actions to be taken.