Faecal incontinence in adults

Bharucha, Adil E.; Knowles, Charles H.; Mack, Isabelle; Malcolm, Allison; Oblizajek, Nicholas R.; Rao, Satish S.; Scott, S. M.; Shin, Andrea; Enck, Paul

doi:10.1038/s41572-022-00381-7

Cited by 51 publications

(31 citation statements)

References 304 publications

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“…While FI is estimated to affect 7-15% of community dwelling adults, most patients do not volunteer the symptoms and many providers do not screen for it. (51,52) This algorithm identified severe FI in a small subset of patients, not one of whom carried FI as a coded diagnosis. In this manner, algorithmic classification may drastically improve the recognition of stigmatizing symptoms that patients may be reticent to divulge in person.…”

Section: Discussionmentioning

confidence: 99%

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%

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

Dallas

Chiang

Caron

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Population studies suggest prevalence ranging from 3%-15% in community-dwelling women, 15% in community dwelling older people, 18%-33% in hospitals and 38% in home health to 50%-70% in nursing homes. A clear relationship with advancing age suggests that it will remain a problem within the developing Western population demographic [5].…”

Section: Scopementioning

confidence: 99%

“…Such estimates probably under-reflect the full impact of FI due to underreporting. It is estimated that treatment of urinary incontinence and FI account for at least 2% of total Western healthcare budgets [5,7].…”

Section: Scopementioning

confidence: 99%

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The 2022 European Society of Coloproctology (ESCP) global snapshot audit of faecal incontinence: Study protocol

Dulskas

2023

Colorectal Disease

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AimFaecal incontinence (FI) is a common problem, affecting about 9% of Western populations. However, only a few patients seek consultation and the number of such patients reaching hospital care is unknown. Current treatment pathways are poorly evidence based and are thought to vary greatly between different countries. This audit will study the incidence of patients presenting to coloproctologists with FI, current practice including diagnostics, conservative and surgical techniques across different units in Europe and worldwide. The aim is to explore the incidence of FI in patients attending colorectal surgical clinics, treatment choices and accessibility to diagnostic tools/advanced treatments across the international cohort. Outcomes to be measured include the volume of FI patients consulting clinical practice per surgeon, patient demographics, and diagnostics and intervention details.MethodAn international multicentre snapshot audit will be performed. During the study period all consecutive eligible patients will be included over an 8‐week period between 9 January and 28 February. Data will be entered and saved in a Research Electronic Data Capture secured database. Moreover, two short physician and centre‐level surveys will be filled for current practice evaluation. The results will be published in international journals and will be prepared in accordance with guidelines set by the STROBE (strengthening the reporting of observational studies in epidemiology) statement for observational studies.DiscussionThis multicentre, global prospective audit will be delivered by consultant colorectal and general surgeons as well as trainees. The data obtained will lead to a better understanding of the incidence of FI and treatment and diagnostic possibilities. This snapshot audit will be hypothesis generating and inform areas that need future prospective study.

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“…6 Several risk factors predispose to FI, including sex, age, obstetric trauma, bowel disturbance such as diarrhea and constipation, obesity, smoking, and physical and mental disability. 7 Age is a strong risk factor for FI and prevalence increases markedly above the age of 65 years. 2 Obstetric trauma is the most common etiology of FI in women.…”

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confidence: 99%

Safety and Efficacy of Iltamiocel Cellular Therapy for the Treatment of Fecal Incontinence. Results of a Phase 1/2 Study

et al. 2023

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Objective: To examine the safety and efficacy of iltamiocel, an investigational cellular therapy of autologous muscle-derived cells, as a treatment for fecal incontinence (FI) in adults. Background: Limited therapeutic options are available for patients with FI refractory to conservative treatments. Cell therapy using autologous muscle-derived cells represents a promising, minimally invasive approach for restoring anal sphincter function. Methods: In this multicenter, prospective, non-randomized study, 48 participants were treated with a single iltamiocel dose of 250×10 6 cells. The primary outcome was the incidence of product or procedure-related adverse events (AEs) and serious AEs. Secondary outcomes were changes in the number of FI episodes, Cleveland Clinic Incontinence Score, Fecal Incontinence Quality of Life, and anorectal manometry at 3, 6, and 12 months compared to baseline. Results: No serious AEs and only one product-related AE of inflammation at the injection site were reported. At 12 months, there was a reduction in median FI episodes (−6.0; 95% confidence interval (CI): −10.0, −1.0) and days with episodes (−4.0; 95% CI: −8.0, −1.0). A ≥ 50% reduction in FI episodes was observed in 53.7% of participants, and 24.4% had complete restoration of continence. Symptom severity and quality of life improved with mean Cleveland Clinic Incontinence Score reduction (-2.9; 95% CI: −3.7, −2.1), and Fecal Incontinence Quality of Life increased (2.2; 95% CI:1.4, 2.9). No significant changes were detected in anorectal manometry measurements. A history of episiotomy was significantly associated with treatment response in multivariate analysis. Conclusion:The administration of iltamiocel cellular therapy is safe. Iltamiocel shows promise for significantly improving fecal incontinence symptoms and quality of life.

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Faecal incontinence in adults

Cited by 51 publications

References 304 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

The 2022 European Society of Coloproctology (ESCP) global snapshot audit of faecal incontinence: Study protocol

Safety and Efficacy of Iltamiocel Cellular Therapy for the Treatment of Fecal Incontinence. Results of a Phase 1/2 Study

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