The three-year evolution of overactive bladder syndrome in community-dwelling female residents aged 40 years and above

Abstract

Objective: In this 3-year longitudinal cohort study, we aimed to evaluate the evolution of overactive bladder in female community residents aged 40 years and above in central Taiwan and identify its risk factors. Materials and methods: Female community residents aged 40 years and above were invited to participate in this study and fill out a yearly Overactive Bladder Symptom Score (OABSS) questionnaire over a 3-year period. A woman was defined to have OAB if the total OABSS was ≧4 and urgency score was ≧2. At the end of the third year, the incidence, remission, persistence, and relapse of OAB in these community residents were calculated. A novel statistical analysis technique, machine learning with data mining, was applied to examine its use in this field.Five machine learning models were used to predict the risk factors associated with persistent OAB and the results were compared with the conventional logistic regression model. Results: In total, 1469 female residents were included in the first year and 1290 (87.8%) women completed the questionnaires for all 3 years. The prevalence of OAB was 20.2% (n = 260). The second- and third-year incidence rates of OAB were 13.5% and 7.1%. The remission rates were 39.6% and 44.3%. Twenty-two percent of the women reported relapse of OAB in the third year. The two-year OAB persistence rate was 43.8%. For the prediction of risk factors for persistent OAB, the multivariable logistic regression model had better predictive accuracy (AUC = 0.664) than the five machine learning models. Age ≧ 60 was associated with persistent OAB (OR 2.8; 95% CI: 1.34–5.89, P = 0.002). Conclusion: The yearly incidence, remission, and persistence rates of OAB were high in female community residents aged 40 years and above in central Taiwan. Older women had a higher risk of persistent OAB symptoms in this 3-year longitudinal cohort study.

Keywords

• Female
• Overactive bladder
• Community study
• Risk factors
• Machine learning