Development and validation of models predicting treatment patterns in women with urinary urgency and/or urgency incontinence: A Symptoms of Lower Urinary Tract Dysfunction Research Network observational cohort study

Abstract

AbstractPurposeTo develop a tool to predict a woman's treatment pattern for bothersome urinary urgency (UU) and/or UU incontinence over 1 year after presenting for care at urology or urogynecology clinics.MethodsThe Symptoms of Lower Urinary Tract Dysfunction Research Network observational cohort study enrolled adult women with bothersome UU and/or UU incontinence using the lower urinary tract symptoms (LUTS) Tool who were seeking care for LUTS. Treatments for UU and/or urgency incontinence were ordered from least to most invasive. Ordinal logistic and Cox proportional hazard regression models were fit to predict the most invasive level of treatment during follow‐up and overactive bladder (OAB) medication discontinuation, respectively. Binary logistic regression was performed to predict sling treatment during the study follow‐up. Clinical tools were then created using the models listed above to predict treatment pattern over 12 months.ResultsAmong 349 women, 281 reported UU incontinence, and 68 reported UU at baseline. The highest level of treatment during the study was as follows: 20% no treatment, 24% behavioral treatments, 23% physical therapy, 26% OAB medication, 1% percutaneous tibial nerve stimulation, 3% onabotulinumtoxin A, and 3% sacral neuromodulation. Slings were placed in 10% (n = 36) of participants before baseline and in 11% (n = 40) during study follow‐up. Baseline factors associated with predicting the most invasive level of treatment included baseline level of treatment, hypertension, UU incontinence severity, stress urinary incontinence (SUI) severity, and anticholinergic burden score. Less severe baseline depression and less severe UU incontinence were associated with OAB medication discontinuation. UU and SUI severity were associated with sling placement during the study period. Three tools are available to predict: (1) highest level of treatment; (2) OAB medication discontinuation; and (3) sling placement.ConclusionsOAB treatment prediction tools developed in this study can help providers individualize treatment plans and identify not only patients at risk for treatment discontinuation but also patients who may not be escalated to potentially beneficial OAB treatments, with the goal to improve clinical outcomes for patients suffering from this chronic and often debilitating condition.