Clinical Manifestations of Overactive Bladder With Migraine as a Comorbidity: A Prospective Cross-Sectional Study

Black box in overactive bladder: Central sensitization and its relationship with urinary symptom severity and quality of life

AIM

To examine central sensitization (CS), and to investigate the relationship between CS, and urinary symptom severity, and quality of life (QoL) in women with overactive bladder (OAB).

MATERIALS AND METHODS

A total of 144 women with OAB included the study. CS with the Central Sensitization Inventory (CSI), urinary symptom with the Overactive Bladder Questionnaire-Version 8 (OAB-V8), bladder diary and Patients' Perception of Intensity of Urgency Scale (PPIUS) and QoL with the King's Health Questionnaire (KHQ) were assessed.

RESULTS

It was found that 47.9% (n = 69) of women with OAB had CS. It was observed that the CSI score was related to the OAB-V8 score (ρ = 0.327; p < 0.001) and the average number of voids/day (ρ = 0.291; p < 0.001). Additionally, urgency severity was higher in women with OAB with CS than in women with OAB without CS (p = 0.006). There was a relationship between the CSI score and KHQ-incontinence impact (ρ = 0.250; p = 0.012), KHQ-personal relationship (ρ = 0.253; p = 0.002), KHQ-sleep/energy (ρ = 0.180; p = 0.031), KHQ-emotional state (ρ = 0.310; p < 0.001) and KHQ-severity measurement scores (ρ = 0.391; p < 0.001).

CONCLUSION

In this study, it was observed that the majority of women with OAB had CS. It was found that more severe symptoms of CS were associated with worse urinary symptom severity and QoL in these patients. It may be beneficial to evaluate CS in the management of OAB and to consider CS when determining treatment strategies.

How effectively do we apply first-line treatment in overactive bladder?

INTRODUCTION AND HYPOTHESIS

We aimed to reveal the effectiveness of the combination of behavioral therapy (BT), drug therapy, and pelvic floor muscle training (PFMT) in patients with the diagnosis of overactive bladder (OAB) who did not respond to drug therapy.

METHODS

Seventy female patients aged between 18 and 65 years diagnosed with wet-type OAB, who did not respond to drug therapy, were included in our study, which was planned as a prospective randomized controlled trial. The patients were randomly assigned to one of two groups. BT and a combination of anticholinergic + beta3-agonist was implemented in the control group for 12 weeks. BT and PFMT were applied with a combination of anticholinergic + beta3-agonist in the active therapy group for 12 weeks. Post-treatment changes in OAB, ICIQ-SF scores, and frequency and nocturia were compared.

RESULTS

The age and BMI averages of the groups were similar (p>0.01). After the treatment, no significant decrease was observed in OAB scores in the control group (p = 0.06), but a significant decrease was observed in the active therapy group (p<0.01). The mean ICIQ-SF scores and the number of nocturia were found to decrease in both groups after 12 weeks of treatment (p<0.01). There was no significant decrease in frequency in the control group (p = 0.054). It regressed significantly in the active therapy group (p<0.01). After the treatment, 3 of 30 the patients in control group (10%) and 11 of the 32 patients in the active therapy group (34.3%) said that their complaints had regressed and that they were pleased with their current situation. Although after the treatment, 4 patients in the control group were dry (13.3%), 10 patients in the active therapy group were dry (31.25%).

CONCLUSIONS

We demonstrated that drug therapy, BT, and PFMT, which are recommended in the first-line treatment of OAB reduce the need for invasive treatments when they are well explained to the patients and combined.

Applications of artificial intelligence in urological setting: a hopeful path to improved care

Artificial intelligence (AI) has been introduced in urology research and practice. Application of AI leads to better accuracy of disease diagnosis and predictive model for monitoring of responses to medical treatments. This mini-review article aims to summarize current applications and development of AI in urology setting, in particular for diagnosis and treatment of urological diseases. This review will introduce that machine learning algorithm-based models will enhance the prediction accuracy for various bladder diseases including interstitial cystitis, bladder cancer, and reproductive urology.

Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device

Purpose

In this study, a urinary management system was established to collect and analyze urinary time and interval data detected through patient-worn smart bands, and the results of the analysis were shown through a web-based visualization to enable monitoring and appropriate feedback for urological patients.

Methods

We designed a device that can recognize urination time and spacing based on patient-specific posture and consistent posture changes, and we built a urination patient management system based on this device. The order of body movements during urination was consistent in terms of time characteristics; therefore, sequential data were analyzed and urinary activity was recognized using repeated neural networks and long-term short-term memory systems. The results were implemented as a web (HTML5) service program, enabling visual support for clinical diagnostic assistance.

Results

Experiments were conducted to evaluate the performance of the proposed recognition techniques. The effectiveness of smart band monitoring urination was evaluated in 30 men (average age, 28.73 years; range, 26–34 years) without urination problems. The entire experiment lasted a total of 3 days. The final accuracy of the algorithm was calculated based on urological clinical guidelines. This experiment showed a high average accuracy of 95.8%, demonstrating the soundness of the proposed algorithm.

Conclusions

This urinary activity management system showed high accuracy and was applied in a clinical environment to characterize patients’ urinary patterns. As wearable devices are developed and generalized, algorithms capable of detecting certain sequential body motor patterns that reflect certain physiological behaviors can be a new methodology for studying human physiological behaviors. It is also thought that these systems will have a significant impact on diagnostic assistance for clinicians.