Development and validation of a prediction model for failed shockwave lithotripsy of upper urinary tract calculi using computed tomography information: the S

Autor: Takashi, Yoshioka, Tatsuyoshi, Ikenoue, Hideaki, Hashimoto, Hideo, Otsuki, Tadashi, Oeda, Noritaka, Ishito, Ryuta, Watanabe, Takashi, Saika, Motoo, Araki, Shunichi, Fukuhara, Yosuke, Yamamoto
Rok vydání: 2019
Předmět:
Zdroj: World Journal of Urology
ISSN: 1433-8726
Popis: Purpose To develop and validate a new clinical prediction model that accurately predicts the failure of shockwave lithotripsy (SWL) using information obtained from non-contrast-enhanced computed tomography (NCCT). Methods This multicentre retrospective cohort study consecutively enrolled patients diagnosed with upper urinary tract calculi by NCCT at five hospitals in Japan from January 1, 2006 to December 31, 2016. Among the candidate predictors, we selected the six most significant predictors a priori. The main outcome was SWL failure after three sessions. Model calibration was evaluated by the calibration slope and the Hosmer–Lemeshow test. Discrimination was evaluated by the receiver-operating characteristic curves and the area under the curve (AUC). A multivariable logistic regression analysis was performed; based on the estimated β coefficients, predictive scores were generated. Results Of 2695 patients, 2271 were included. Patients were divided into the development cohort (1666 patients) and validation cohort (605 patients) according to geographical factors. We developed a clinical prediction model with scores ranging from 0 to 49 points. We named the prediction model the S3HoCKwave score based on the initials of the predictors (sex, skin-to-stone distance, size, Hounsfield units, colic, and kidney or ureter). As a result of internal validation, the optimism-corrected AUC was 0.72. In the validation cohort, the Hosmer–Lemeshow test did not show statistical significance (P = 0.33), and the AUC was 0.71 (95% confidence interval 0.65–0.76). Conclusions The S3HoCKwave score is easy to understand, has a relatively high predictive value, and allows clinicians to make appropriate treatment selections. Electronic supplementary material The online version of this article (10.1007/s00345-020-03125-y) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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