Use cluster analysis to develop an accurate and practical diagnostic method for detrusor underactivity in female patient without bladder outlet obstruction

You have accessJournal of UrologyCME1 Apr 2023MP03-20 USE CLUSTER ANALYSIS TO DEVELOP AN ACCURATE AND PRACTICAL DIAGNOSTIC METHOD FOR DETRUSOR UNDERACTIVITY IN FEMALE PATIENT WITHOUT BLADDER OUTLET OBSTRUCTION Jen-Chieh Chen, Chi-Chieh Lin, Yu-Hua Fan, Shing-Hwa Lu, Alex T. L. Lin, and William J. Huang Jen-Chieh ChenJen-Chieh Chen More articles by this author , Chi-Chieh LinChi-Chieh Lin More articles by this author , Yu-Hua FanYu-Hua Fan More articles by this author , Shing-Hwa LuShing-Hwa Lu More articles by this author , Alex T. L. LinAlex T. L. Lin More articles by this author , and William J. HuangWilliam J. Huang More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003214.20AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The International Continence Society (ICS) definition of the detrusor underactivity (DU) lacks of standardized urodynamic (UDS) parameters and thresholds. Standardized and accepted nomograms for the DU diagnosis have been reported only for men, while there are no definitive standardized UDS criteria for the definition of female DU. We propose to use the cluster analysis to identify DU among female patient without bladder outlet obstruction (BOO) on the daily practice. METHODS: We retrospectively reviewed our video-urodynamic study (VUDS) database from 2015 to 2020. All female patients with “weak stream” subscore of International Prostate Symptom Score equal or greater than 3 points were included. The exclusion criteria were patients who had radiological evidence of BOO and situational inability to void on VUDS. The VUDS results were reviewed by two independent researchers, and DU was diagnosed based on the ICS criteria. A total of 80 patients were enrolled, and we divided them into discovery (from 2018 to 2020) and validation cohort (from 2015 to 2018). Hierarchical cluster analysis and K-means clustering were performed with UDS parameters which included detrusor pressure at maximum flow (PdetQmax), maximal urine flow rate (UFR), and voiding time. RESULTS: Three groups could be identified by cluster analysis in discovery cohort. Group 1 (17 patients) had low PdetQmax (14 cmH2O), high maximal UFR (23.5 ml/s), and short voiding time (10.5 seconds). Group 2 (13 patients) had high PdetQmax (27 cmH2O), medial maximal UFR (14.4 ml/s), and short voiding time (10.6 seconds). Group 3 (8 patients) had low PdetQmax (7 cmH2O), low maximal UFR (11.4 ml/s), and long voiding time (26.1 seconds). We found that all DU (4 patients) and acontratile detrusor (AcD) (4 patients) patients were clustered in Group 3. In validation cohort, we assigned each data point to the nearest centroid (by Euclidean distance after standardization) of the groups that we obtained from the discovery cohort. We found that 7 of 8 DU or AcD patients in validation cohort were assigned to the Group 3. The positive predictive value and the negative predictive value of this approach for diagnosis of DU or AcD were 87.5% and 97.1%. Besides, we also used the validation cohort to repeat the cluster analysis. We discovered that we were able to replicate the division into three similar groups as the discovery cohort, and the centroids were almost the same. CONCLUSIONS: Among woman who had weak stream without BOO on VUDS, we detected three urodynamic characters, similarly in two independent patient cohorts, by using cluster analysis. The DU and AcD could be clustered into one of the groups with high sensitivity and specificity. We propose to use this cluster classification to identify DU on the daily practice. Source of Funding: Nil © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e31 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jen-Chieh Chen More articles by this author Chi-Chieh Lin More articles by this author Yu-Hua Fan More articles by this author Shing-Hwa Lu More articles by this author Alex T. L. Lin More articles by this author William J. Huang More articles by this author Expand All Advertisement PDF downloadLoading ...