LAUSR

OBJECTIVE To establish a prediction model of renal calculus for university teachers to help them prevent renal calculus scientifically. This study involves a specific group of university teachers. We collected the physical examination index of 1043 university teachers in the Hubei University of Chinese Medicine in 2018 to build the model. We also used the physical examination data of 968 teachers in 2019 to verify the model. MATERIALS AND METHODS We used Lasso regression to screen the factors and logistic regression analysis to establish the model. RESULTS The models of this study included sex, age, DBP, TC, HDL. C, CEA, UA, ALT, GGT, HB, pH, RBC, RDW, and CLYMPH. Among these, sex, TC, ALT, HB, and LYMPH present high risks in the model. The result is of great significance related to the research of university teachers suffering from renal calculus. The C-index is 0.715, and the AUC is 0.7064. CONCLUSIONS Based on the results of this study, we suggest that physical examination indicators can predict the risk of renal calculus and the individual probability of prevalence in specific groups. According to the risk of each physical examination index, it is possible to effectively prevent the occurrence of renal calculus in certain high-risk groups through lifestyle changes.