Evaluation of Anorectal Function in Perianal Crohn’s Disease: A Pilot Study

Background: Perianal Crohn’s disease is a disabling condition, with little known about anorectal function in healed/inactive perianal Crohn’s disease; Aim: To evaluate anorectal function in a cohort of patients with treated/healed perianal Crohn’s disease; Methods: Prospective cohort study, including high-resolution anorectal manometry, balloon expulsion test, and 3D-endoanal ultrasound in all patients; Results: Of the 16 patients studied (mean age ± SD, 42 ± 13 years), 12 (75%) were men. A laceration of the internal anal sphincter and/or anal scarring was seen in nine (56%) patients; there was no laceration of the external anal sphincter. Five (56%) of these nine patients had never experienced faecal incontinence. All had normal anal resting and squeeze pressures. Manometry suggested dyssynergia in 11 (69%) patients, with only one (6%) fulfilling the criteria for obstructed defecation. Hyposensitivity for at least one sensory parameter was seen in 11 (69%) patients and hypersensitivity in five (31%) patients; Conclusions: This study detected sphincter abnormalities in more than half of patients, many of whom were asymptomatic. Alterations in rectal sensation were frequently seen, more commonly with rectal hyposensitivity. Trial registration: ClinicalTrials.gov (NCT03819257).

Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry

BACKGROUND AND AIMS: Evaluation for dyssynergia is the most common reason that gastroenterologists refer patients for anorectal manometry, because dyssynergia is amenable to biofeedback by physical therapists. High-definition anorectal manometry (3D-HDAM) is a promising technology to evaluate anorectal physiology, but adoption remains limited by its sheer complexity. We developed a 3D-HDAM deep learning algorithm to evaluate for dyssynergia. METHODS: Spatial-temporal data were extracted from consecutive 3D-HDAM studies performed between 2018-2020 at a tertiary institution. The technical procedure and gold standard definition of dyssynergia were based on the London consensus, adapted to the needs of 3D-HDAM technology. Three machine learning models were generated: (1) traditional machine learning informed by conventional anorectal function metrics, (2) deep learning, and (3) a hybrid approach. Diagnostic accuracy was evaluated using bootstrap sampling to calculate area-under-the-curve (AUC). To evaluate overfitting, models were validated by adding 502 simulated defecation maneuvers with diagnostic ambiguity. RESULTS: 302 3D-HDAM studies representing 1,208 simulated defecation maneuvers were included (average age 55.2 years; 80.5% women). The deep learning model had comparable diagnostic accuracy (AUC=0.91 [95% confidence interval 0.89-0.93]) to traditional (AUC=0.93[0.92-0.95]) and hybrid (AUC=0.96[0.94-0.97]) predictive models in training cohorts. However, the deep learning model handled ambiguous tests more cautiously than other models; the deep learning model was more likely to designate an ambiguous test as inconclusive (odds ratio=4.21[2.78-6.38]) versus traditional/hybrid approaches. CONCLUSIONS: By considering complex spatial-temporal information beyond conventional anorectal function metrics, deep learning on 3D-HDAM technology may enable gastroenterologists to reliably identify and manage dyssynergia in broader practice.

Prevalence and Clinical Characteristics of Dyssynergic Defecation and Slow Transit Constipation in Patients with Chronic Constipation

Patients with chronic constipation who do not respond to initial treatments often need further evaluation for dyssynergic defecation (DD) and slow transit constipation (STC). The aims of this study are to characterize the prevalence of DD and STC in patients referred to a motility center with chronic constipation and correlate diagnoses of DD and STC to patient demographics, medical history, and symptoms. High-resolution ARM (HR-ARM), balloon expulsion testing (BET) and whole gut transit scintigraphy (WGTS) of consecutive patients with chronic constipation were reviewed. Patients completed questionnaires describing their medical history and symptoms at the time of testing. A total of 230 patients completed HR-ARM, BET, and WGTS. Fifty (22%) patients had DD, and 127 (55%) patients had STC. Thirty patients (13%) had both DD and STC. There were no symptoms that were suggestive of STC vs. DD; however, patients with STC and DD reported more severe constipation than patients with normal transit and anorectal function. Patients with chronic constipation often need evaluation for both DD and STC to better understand their pathophysiology of symptoms and help direct treatment.