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

Abstract

Introduction Discrete mathematics, a branch of mathematics that includes graph theory, combinatorics, and logic, focuses on discrete mathematical structures. Its application in the medical field, particularly in analyzing patterns in patient data and optimizing treatment methods, is invaluable. This study, focusing on post-void residual (PVR) urine following overactive bladder (OAB) treatment, utilized discrete mathematics techniques to analyze PVR and its associated risk factors. Methods A retrospective study was conducted on 128 OAB patients who received intradetrusor onabotulinum toxin A injections between 2020 and 2022. Network graphs based on graph theory were used to analyze correlations between clinical variables, and clustering analysis was performed with PVR as the primary variable. Results The network graph analysis revealed that frailty, daytime frequency, and nocturia episodes were closely related to PVR. Clustering analysis with PVR as the primary variable divided the patients into three groups, suggesting that the group with particularly high frailty (Cluster 1) is at high risk for PVR. Moreover, significant differences in clinical indicators such as age, voiding efficiency, Overactive Bladder Symptom Score, and International Consultation on Incontinence Questionnaire-Short Form were observed in the remaining two clusters (Cluster 0 and 2). Conclusion This study demonstrates the effectiveness of discrete mathematics methods in identifying risk factors for PVR after OAB treatment and in distinguishing clinical subgroups based on patient characteristics. This approach could contribute to the formulation of individualized treatment strategies and the improvement of patient care quality. Further development and clinical application of this methodology are expected in future research.

Innovative decision making tools using discrete mathematics for stress urinary incontinence treatment

“…In this study, we adapted graph theory from discrete mathematics to explore the complex interactions between various variables related to SUI and treatments (TVT, VEL, PFMT). To our knowledge, this is the first time discrete mathematics has been applied to clinical medical treatment choices 5 .…”

“…The variables were represented as nodes, and the edges between them were weighted according to the calculated correlation coefficients. The shorter the edge, the higher the correlation coefficient, which visually represents the strength of the relationship between variables 5 .…”

“…Graph theory is used in a wide range of fields, from modern navigation systems such as Google Maps to air traffic control and robotic operations, contributing to efficient decision making by computing optimal routes and solutions 3 , 4 . However, the application of graph theory in clinical medicine, particularly in treatment selection, is largely unexplored, with our work being one of the few in this field 5 . This scarcity of research highlights the significant potential and need for further investigation in this area 5 , 6 .…”

“…However, the application of graph theory in clinical medicine, particularly in treatment selection, is largely unexplored, with our work being one of the few in this field 5 . This scarcity of research highlights the significant potential and need for further investigation in this area 5 , 6 .…”

Navigating Treatment Choices for Stress and Urgency Urinary Incontinence Using Graph Theory in Discrete Mathematics

“…This graph theory is used in Google Maps and air traffic control maps, but its application in clinical medicine has been minimal [2]. Our research focused on the utilization of discrete mathematics in defining the treatment [3]. In this study, we used network diagrams and heuristic functions, which are fundamental to car navigation, for treatment selection.…”