Department of Urology, Faculty of Medicine, Kyoto University Graduate School of Medicine, 54 Shogoinkawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan.
Department of Health Promotion and Human Behavior, Kyoto University School of Public Health, Kyoto, Japan.
BMC Urol. 2021 May 13;21(1):78. doi: 10.1186/s12894-021-00848-x.
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.
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.
This will be the first study to develop a model to predict the incidence of OAB.
准确的预测模型可以识别普通人群中发生过动性膀胱（OAB）的高危人群，并进行早期预防，从而节省相关医疗费用。然而，目前还没有开发出用于预测 OAB 发生的有效模型。在这项研究中，我们将开发一种用于预测普通人群中 OAB 发病的 5 年模型。
数据将来自长冈队列研究，这是一项纵向的普通人群队列研究。基线特征的测量时间为 2008 年 11 月 28 日至 2010 年 11 月 28 日，随访时间为每 5 年一次。从基线的 9764 名参与者（男性 3208 名，女性 6556 名）中，我们将排除无法参加随访评估的参与者和基线时被定义为患有 OAB 的参与者。采用随访评估中的过动性膀胱症状评分（OABSS）来定义新发生的 OAB。将基线问卷（人口统计学、健康行为、合并症和 OABSS）和血液测试数据作为预测因素纳入其中。我们将利用收缩方法（LASSO 惩罚方法）开发逻辑回归模型。通过区分度和校准度评估模型性能。通过决策曲线分析评估净效益。我们将对模型进行内部验证和时间验证。我们将开发一个基于网络的应用程序，以可视化预测模型并促进其在临床实践中的应用。
这将是第一个开发预测 OAB 发病率模型的研究。
