Predictive Factors for High Post-void Residual Volume in Older Females After OnabotulinumA Treatment for Severe Overactive Bladder Using a Machine Learning Model

Abstract

Introduction Intravesical onabotulinumA injection is actively used for the treatment of overactive bladder (OAB). However, it occasionally results in significant post-void residual urine (PVR) volume, which can lead to complications and can further impair the activities of daily living in older people. Therefore, this study aimed to identify the predictors of a high post-onabotulinumA injection PVR volume in older women with severe OAB. Methods An observational study was conducted on older women who had previously received intravesical onabotulinumA injections to treat OAB between 2020 and 2022. Urodynamic studies and symptom assessments were conducted, and machine learning models, including random forest and support vector machine (SVM) models, were developed using the R code generated by Chat Generative Pre-trained Transformer 4 (ChatGPT, OpenAI, San Francisco, USA). Results Among 128 patients with OAB, 23 (18.0%) had a PVR volume of > 200 mL after receiving onabotulinumA injections. The factors associated with a PVR volume of > 200 mL were investigated using univariate and multivariate analyses. Age, frailty, OAB-wet, daytime frequency, and nocturia were significant predictors. Random forest analysis highlighted daytime frequency, frailty, and voiding efficiency as important factors. An SVM model incorporating daytime frequency, frailty, and voiding efficiency improved PVR volume prediction. Logit(p) estimation yielded an area under the receiver operating characteristic curve of 0.926294. Conclusion The study found daytime frequency, frailty, and voiding inefficiency to be significant factors associated with a PVR volume of > 200 mL, in older women with severe OAB. Utilizing advanced machine learning techniques and following the guidance of ChatGPT, this research emphasizes the relevance of considering multiple intersecting factors for predicting PVR volume. The findings contribute to our understanding of onabotulinumA injection treatment for OAB and support evidence-based decision-making using readily available information.

A Discrete Mathematics Approach for Understanding Risk Factors in Overactive Bladder Treatment

“…This retrospective study enrolled overactive bladder (OAB) patients who underwent 100U intradetrusor injections of onabotulinum toxin A (GlaxoSmithKline, Tokyo, Japan) between 2020 and 2022 [ 10 ]. All patients provided written informed consent after receiving information about potential adverse events associated with these injections [ 10 ]. Each patient received a total of 100U of onabotulinum toxin A injections intradetrusorly into the bladder, with the trigone being excluded.…”

“…Each patient received a total of 100U of onabotulinum toxin A injections intradetrusorly into the bladder, with the trigone being excluded. This study constitutes a post-hoc analysis of a prior study [ 10 ]. It received approval from the regional medical ethics committee (Ethics Review Committee of Yokosuka Urogynecology and Urology Clinic, Protocol No.…”

“…Patients with pelvic organ prolapse at Stage II or lower were considered eligible for the treatment group. At the time of study registration, all patients did not present with neurogenic bladder, intrinsic sphincter deficiency, urinary tract infection, congenital malformation, or vesicovaginal fistula [ 10 ].…”

ChatGPT in urology practice: revolutionizing efficiency and patient care with generative artificial intelligence

“…Okui et al [21] performed a study on the prediction of postvoid residual urine after type A botulinum toxin treatment for severe overactive bladder. Preoperative information was uploaded on ChatGPT to generate predictive models for high postvoid residuals, thus supporting evidence-based decision-making using readily available information [21].…”

“…Okui et al [21] performed a study on the prediction of postvoid residual urine after type A botulinum toxin treatment for severe overactive bladder. Preoperative information was uploaded on ChatGPT to generate predictive models for high postvoid residuals, thus supporting evidence-based decision-making using readily available information [21]. Even if not in a classic clinical scenario, this study provides insight into the feasible application of generative-artificial intelligence in the decision-making process, not by taking the decisional role of the urologist, but instead by assisting the physician, by promptly providing available information.…”