Clustering of patients with overactive bladder syndrome

Gross, Jeffrey P.; Vetter, Joel; Lai, H. Henry

doi:10.1186/s12894-021-00812-9

Cited by 6 publications

(8 citation statements)

References 28 publications

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“…Several groups have tried to subclassify urinary symptoms or genitourinary pain, but have typically examined only one symptom cluster in isolation (e.g., overactive bladder (40)(41)(42)(43) or genitourinary pain (44)(45)(46)(47)). However, "as often patients present with multiple urinary symptoms that do not perfectly fit the pre-established diagnoses,"(42) the intentional exclusion in these studies of patients with overlapping symptoms or co-existing pelvic organ prolapse may underlie the lack of practical progress in improving current diagnostic schema.…”

Section: Discussionmentioning

confidence: 99%

“…Our findings here are novel and timely with the potential to improve diagnosis and treatment and overcome the significant burden these conditions place on patients and the healthcare system. While multiple groups have proposed various approaches to subclassifying urinary symptoms and genitourinary pain, (40)(41)(42)(43)(44)(45)(46) most require detailed information (patient demographics, physical exam findings, imaging, genetic or biochemical markers, or other diagnostic testing results) unavailable or unfamiliar to most practioners outside of specialized clinical settings. In addition, most of these categorization schema example narrow populations without overlapping symptoms, which has ignored a large subset of real-world patients.…”

Section: Discussionmentioning

confidence: 99%

“…Several groups have tried to subclassify urologic phenotypes, but have typically examined only one symptom cluster in isolation (e.g., overactive bladder 12,22-24 or genitourinary pain [25][26][27][28] ). Substantial work, however, documents that these divisions are artificial; many patients carrying an OAB diagnosis suffer from significant bladder pain (35%) while more than 30% of IC/BPS patients have urge incontinence.…”

Section: Discussionmentioning

confidence: 99%

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Development and Validation of a Machine Learning Algorithm to Classify Lower Urinary Tract Symptoms

Dallas

Chiang

Caron

et al. 2022

Preprint

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Background: Lower urinary tract urinary symptoms (LUTS), such as urinary urgency, frequency, and incontinence, affect the majority of the population at some point in the lifepspan, causing substantial morbidity, yet few receive effective care. Acccurate diagnosis and treatment is usually dictated by the dominant symptom, however, the sizeable symptomatic overlap between disease categories and subjectivity of language used to describe symptoms leads to high rates of misdiagnosis. We hypothesized that more specific and homogeneous LUTS diagnoses are characterized not by specific pathognomic features, but by patterns of existing symptoms indicative of unique causes of convergent symptomatologies. To improve care and diagnostic acuracy, we sought to employ a data-driven approach to LUTS categorization using machine learning to generate diagnostic groupings based on patient-reported clinical data, creating a novel tool for diagnosis for patients with voiding complaints. Methods and Findings: Questionnaire responses in a development cohort of 514 female subjects was used for model development, identifying 4 major categories and 9 specific phenotypes of LUTS using agglomerative hierarchical clustering. The dominant features for each cluster and phenotype were examined independently by two urologic specialists and assigned a clinical identity consistent with recognized causes of voiding dysfunction. Then, a supervised machine learning model was trained to assign unseen patients into these phenotypes. The model was then applied to a validation population of 571 additional subjects to validate the diagnostic machine learning algorithm, demonstrating good reproducibillty of the phenotypes and their symptomatic patterns in an independent cohort. This data-driven, hierarchical clustering approach captured overlapping symptoms inherent in typical patients, recognizing common uncomplicated diagnoses (e.g. overactive bladder) and several novel diagnostic categories (e.g. myofascial pelvic pain). The assigned clusters and phenotypes were consistent with coded primary diagnoses with 70% accuracy; however, physician primary diagnosis may not reflect the complete range of patient symptoms as patients often present with multiple urinary symptoms that do not fit pre-established diagnoses. Although this analysis reflects care-seeking populations from a single center, further studies will need to determine if the model is scalable to the population at large. Conclusions: We describe the generation of a machine learning algorithm relying only on validated patient-reported symptoms for accurate diagnostic classification. Given a growing physician shortage and increasing challenges for patients accessing specialist care, this type of digital technology holds great potential to improve the recognition, diagnosis, and treatment of functional urologic conditions. While future prospective work with larger, multi-institutional cohorts is needed, with refinement, this approach is capable of increasing both the equity and rapidity of access to effective urologic care.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Development and Validation of a Machine Learning Algorithm to Classify Lower Urinary Tract Symptoms

Dallas

Chiang

Caron

et al. 2022

Preprint

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“…Although many other factors have been discussed in the pathophysiology of overactive bladder and UUI, 19 urethral dysfunction is certainly one that has not been satisfactorily addressed in the literature.…”

Section: Urethral Function: Current State Of Sciencementioning

confidence: 99%

Urethral function and failure: A review of current knowledge of urethral closure mechanisms, how they vary, and how they are affected by life events

Pipitone

Sadeghi

DeLancey

2021

Neurourology and Urodynamics

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Introduction: A critical appraisal of the literature regarding female urethral function and dysfunction is needed in light of recent evidence showing the urethra's role in causing stress and urge urinary incontinence. Methods: An evidence assessment was conducted using selected articles from the literature that contained mechanistic data on factors affecting urethral function and failure.Results: Maximal urethral closure pressure (MUCP) is 40% lower in stress urinary incontinence (SUI) than normal controls. Evidence from five women shows relatively equal contributions to MUCP from striated/smooth muscle, vascular-plexus, connective tissue. MUCP varies twofold in individuals of similar age and declines 15% per decade even in nulliparous women. Age explains 57% of the variance in MUCP. This parallels with striated/smooth muscle loss and reduced nerve density. Factors influencing pressure variation minute-to-minute and decade-to-decade are poorly understood. Connective tissue changes have not been investigated. MUCP in de novo SUI persisting 9-months postpartum is 25% less than in age and parity-matched controls.Longitudinal studies do not show significant changes in urethral function after vaginal birth suggesting that changes in urethral support from birth may unmask pre-existing sphincter weakness and precipitate SUI. Mechanisms of interaction between support injury, pre-existing urethral weakness, and neuropathy are unclear. Conclusion:Urethral failure is the predominant cause of SUI and a contributing factor for UUI; potentially explaining why mixed symptoms predominate in epidemiological studies. Age-related striated muscle loss and differences between women of similar age are prominent features of poor urethral closure. Yet, connective tissue changes, vasculature function, and complex interactions among factors are poorly understood.

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“…Pelvic floor muscle training (PFMT), through voluntary contraction of the muscles to shut the urethra and thereby raise urethral pressure, can inhibit the urinary reflex, control urgency, and prevent the loss of urine during detrusor contraction (13). However, PFMT can be boring and monotonous, which can cause poor compliance and modest improvement of OAB symptoms (14). Therefore, it is of great significance to establish a new safe and effective training method for functional reconstruction of pelvic floor for patients with OAB.…”

Section: Introductionmentioning

confidence: 99%

Efficacy of Yun-type pelvic floor optimal training therapy and PFMT on middle aged women with mild to moderate overactive bladder: a randomized controlled trial

Wang¹,

Shi²,

Wang³

et al. 2022

Ann Transl Med

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Background: The symptoms of overactive bladder (OAB) are highly bothersome to patients. In behavioral therapy, traditional pelvic floor muscle training (PFMT) can be boring and monotonous, which can cause poor compliance. It's important to establish a new safe and effective crotch-pelvic floor functional reconstruction training method [Yun-type pelvic floor optimal training (Yun-type training)] for OAB patients. Methods: This randomized, double-blind, crossover trial recruited a total of 150 women with mild to moderate OAB. Patients were randomly assigned to receive Yun-type training (n=83) or PFMT (n=67). The intervention group was first given Yun-type training for 6 weeks, then switched to PFMT for 6 weeks after 2 weeks of elution. Patients in the control group were first treated with PFMT for 6 weeks, and changed to the use of Yun-type training for 6 weeks after 2 weeks of elution. The primary endpoint was overactive bladder symptom scores (OABSS) after 6 and 14 weeks of Yun-type training and PFMT. Results: Yun-type training could significantly improve OABSS compared with PFMT after 6 and 14 weeks (P<0.001). Yun-type training was associated with an improvement in urine, urgency, urge urinary incontinence (UUI), urogenital distress inventory-6 (UDI-6), patient's perception of bladder condition (PPBC), voiding volume (VV), incontinence impact questionnaire-7 (IIQ-7), female sexual function index (FSFI), sexual desire, arousal, pain, satisfaction, pelvic floor muscle tone, type I muscle strength, type II muscle strength, and sex partner satisfaction as compared with PFMT after 6 and 14 weeks. Moreover, nocturia, maximum flow rate (Q max ), and climax in the Yun-type training group were significantly improved after 6 weeks, and the average flow rate (Q ave ) was improved after 14 weeks in the Yun-type training group. Conclusions: Yun-type training could yield additional benefits on OAB symptoms, sexual function, and sex partner satisfaction as compared with PFMT. The superior sexual function resulting from Yun-type training can be explained by the fact that it can contract deep and superficial muscle layers, resulting in increased vaginal wall pressure and blood flow, which further improves FSFI and sex partner satisfaction. It could lead to a new non-invasive treatment for OAB patients.

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Clustering of patients with overactive bladder syndrome

Cited by 6 publications

References 28 publications

Development and Validation of a Machine Learning Algorithm to Classify Lower Urinary Tract Symptoms

Development and Validation of a Machine Learning Algorithm to Classify Lower Urinary Tract Symptoms

Urethral function and failure: A review of current knowledge of urethral closure mechanisms, how they vary, and how they are affected by life events

Efficacy of Yun-type pelvic floor optimal training therapy and PFMT on middle aged women with mild to moderate overactive bladder: a randomized controlled trial

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