Protocol for development and validation of a prediction model for 5-year risk of incident overactive bladder in the general population: the Nagahama study

• Main Authors: Satoshi Funada, Yan Luo, Takashi Yoshioka, Kazuya Setoh, Yasuharu Tabara, Hiromitsu Negoro, Shusuke Akamatsu, Koji Yoshimura, Fumihiko Matsuda, Toshi A. Furukawa, Orestis Efthimiou, Osamu Ogawa
• Format: Article
• Language: English
• Published: BMC 2021-05-01
• Series: BMC Urology
• Subjects: Urinary bladder; Longitudinal analysis; Cohort study; Risk calculator
• Online Access: https://doi.org/10.1186/s12894-021-00848-x

Abstract Background An accurate prediction model could identify high-risk subjects of incident Overactive bladder (OAB) among the general population and enable early prevention which may save on the related medical costs. However, no efficient model has been developed for predicting incident OAB. In this study, we will develop a model for predicting the onset of OAB at 5-year in the general population setting. Methods Data will be obtained from the Nagahama Cohort Project, a longitudinal, general population cohort study. The baseline characteristics were measured between Nov 28, 2008 and Nov 28, 2010, and follow-up was performed every 5 years. From the total of 9,764 participants (male: 3,208, female: 6,556) at baseline, we will exclude participants who could not attend the follow-up assessment and those who were defined as having OAB at baseline. The outcome will be incident OAB defined using the Overactive Bladder Symptom Score (OABSS) at follow-up assessment. Baseline questionnaires (demographic, health behavior, comorbidities and OABSS) and blood test data will be included as predictors. We will develop a logistic regression model utilizing shrinkage methods (LASSO penalization method). Model performance will be evaluated by discrimination and calibration. Net benefit will be evaluated by decision curve analysis. We will perform an internal validation and a temporal validation of the model. We will develop a web-based application to visualize the prediction model and facilitate its use in clinical practice. Discussion This will be the first study to develop a model to predict the incidence of OAB.