Mayo Clinic's Predictive Tool Identifies Risk of Recurring Kidney Stones

Samara Rosenfeld
FEBRUARY 06, 2019
big data, kidney stones
Image and thumbnail have been modified. Courtesy of Jakupica via Wikimedia.

A new predictive tool made by researchers at Mayo Clinic has shown promise in helping to identify patients who are at risk of recurring kidney stones, according to a study published in Mayo Clinic Proceedings.

John C. Lieske, M.D., from the division of epidemiology at Mayo Clinic, and his research team, used data from the Rochester Epidemiology Project to study a sampling of former chronic kidney stone patients from Olmsted County, Minn. between 1984 and 2017. More than 2,200 patients’ data were used for the study.

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From the data, the team found that common characteristics of patients who had recurring kidney stone events included younger age, male sex, a higher body mass index, history of pregnancy and a family history of stones. The likelihood of stone recurrence increased after each event, and the size and location of the stones were associated with the risk of another episode.

The Risk of Kidney Stones online tool was then developed based on the data and allows doctors to see a more exact average of a patient’s risk of getting kidney stones again, Lieske told Healthcare Analytics News™.

Based on the results of the study, the team found that the average stone recurrence rates per 100 person-years were 3.4 after the first stone episode, 7.1 after the second, 12.1 after the third and 17.6 after the fourth or higher episode. The tool predicted the rates with a confidence interval of 95 percent.

The Risk of Kidney Stones predictive tool is available online and as an app called “Calculate by QxMD” in the App Store and on Google Play and can be used in additional studies to identify risks in patients in a broader population.

In clinical trials, it is important to have patients that are more likely to have a future event. The tool can allow researchers to pick the right patients to participate in their study, Lieske said.

This tool also gives patients more options when it comes to their treatment, Lieske told us.

Patients can decide how aggressive they want to be in preventing future attacks, whether it be starting medications or adjusting their diet, he said. Doctors can now use the tool and tell a patient that they have a 30 percent chance or a 70 percent chance of having another kidney stone episode and can recommend if the patient should start a certain drug regimen to lessen the chances.

And for healthcare executives, the statistics can be used to model the cost-benefit of different interventions, tests and medications.

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