Researchers at Mayo Clinic, Rochester, Minnesota have developed an online tool to predict recurrence of kidney stone by tracking the former familiar characteristics. Lisa Vaughan and her team used data from Rochester Epidemiology Project, to randomly sample cases of kidney stone formers, who received clinical care between 1984 to 2017. Common patient features with recurrence included male sex, younger age, family history, higher body mass index, pregnancy, diameter of largest stone and number of stones.
These characteristics were used to develop an online tool for predicting recurrence of kidney stone that can estimate the recurrence by entering information like gender, race and kidney stone history of the subject. John Lieske, M.D., said “each of the risk factors we identified is entered into the model, which then calculates an estimate of the risk of having another kidney stone in…
