Predictive markers for infections after extracorporeal shockwave lithotripsy in patients with kidney stone based on a large prospective cohort.

Abstract

Infections in patients with kidney stones after extracorporeal shockwave lithotripsy (SWL) is a common clinical issue. However, the associated factors are unclear. Therefore, we aim to develop and validate a predictive model for infections after SWL in patients with kidney stone. Between June 2020 and May 2022, consecutive kidney stone patients were enrolled. Of them, 553 patients comprised the development cohort. One hundred sixty-five patients comprised the validation cohort. The data were prospectively collected. The stepwise selection was applied using the likelihood ratio test with Akaike's information criterion as the stopping rule; A predictive model was constructed through multivariate logistic regression. The performance was evaluated regarding discrimination, calibration, and clinical usefulness. Predictors of infections after SWL in treating kidney stones included older age (OR = 1.026, p = 0.041), female (OR = 2.066, p = 0.039), higher BMI (OR = 1.072, p = 0.039), lower stone density (OR = 0.995, p < 0.001), and higher grade of hydronephrosis (OR = 5.148, p < 0.001). For the validation cohort, the model showed good discrimination with an area under the receiver operating characteristic curve of 0.839 (95% CI 0.736, 0.941) and good calibration. Decision curve analysis demonstrated that the model was also clinically useful. This study indicated that age, gender, BMI, stone density, and hydronephrosis grade were significant predictors of infections after SWL in treating kidney stones. It provided evidence in optimizing prevention and perioperative treatment strategies to reduce the risk of infection after SWL.