Development of a prediction model in female pure or predominant urge urinary incontinence: a retrospective cohort...

Urinary incontinence is a prevalent form of pelvic floor dysfunction, with a non-negligible impact on a patient's quality of life. There are several treatment options, varying from conservative to invasive. The aim of this study is to predict treatment outcomes of pure or predominant urge urinary incontinence (UUI) in women to support shared decision-making and manage patient expectations.

Data on patient characteristics, disease history, and investigations of 512 consecutive women treated for UUI in three hospitals in the Netherlands were retrospectively collected. The predicted outcome was the short-term subjective continence outcome, defined as patient-reported continence 3 months after treatment categorized as cure (no urinary leakage), improvement (any degree of improvement of urinary leakage), and failure (no improvement or worsening of urinary leakage). Multivariable ordinal regression with backward stepwise selection was performed to analyze association between outcome and patient's characteristics. Interactions between patient characteristics and treatment were added to estimate individual treatment benefit. Discriminative ability was assessed with the ordinal c-statistic.

Conservative treatment was applied in 12% of the patients, pharmacological in 62%, and invasive in 26%. Subjective continence outcome was cure, improvement, and failure in 20%, 49%, and 31%, respectively. Number of incontinence episodes per day, voiding frequency during the day, subjective quantity of UI, coexistence of stress urinary incontinence (SUI), night incontinence, and bladder capacity and the interactions between these variables were included in the model. After internal validation, the ordinal c-statistic was 0.699.

Six variables were of value to predict pure or predominant UUI treatment outcome in women. Further development into a comprehensive set of models for the use in various pelvic floor disorders and treatments is recommended to optimize individualized care. This model requires external validation before implementation in clinical practice.