Habit training versus habit training with direct visual biofeedback in adults with chronic constipation: A randomized controlled trial.

AIM: The aim was to determine whether specialist-led habit training using Habit Training with Biofeedback (HTBF) is more effective than specialist-led habit training alone (HT) for chronic constipation and whether outcomes of interventions are improved by stratification to HTBF or HT based on diagnosis (functional defaecation disorder vs. no functional defaecation disorder) by radio-physiological investigations (INVEST). METHOD: This was a parallel three-arm randomized single-blinded controlled trial, permitting two randomized comparisons: HTBF versus HT alone; INVEST- versus no-INVEST-guided intervention. The inclusion criteria were age 18-70 years; attending specialist hospitals in England; self-reported constipation for >6 months; refractory to basic treatment. The main exclusions were secondary constipation and previous experience of the trial interventions. The primary outcome was the mean change in Patient Assessment of Constipation Quality of Life score at 6 months on intention to treat. The secondary outcomes were validated disease-specific and psychological questionnaires and cost-effectiveness (based on EQ-5D-5L). RESULTS: In all, 182 patients were randomized 3:3:2 (target 384): HT n = 68; HTBF n = 68; INVEST-guided treatment n = 46. All interventions had similar reductions (improvement) in the primary outcome at 6 months (approximately -0.8 points of a 4-point scale) with no statistically significant difference between HT and HTBF (-0.03 points; 95% CI -0.33 to 0.27; P = 0.85) or INVEST versus no-INVEST (0.22; -0.11 to 0.55; P = 0.19). Secondary outcomes showed a benefit for all interventions with no evidence of greater cost-effectiveness of HTBF or INVEST compared with HT. CONCLUSION: The results of the study at 6 months were inconclusive. However, with the caveat of under-recruitment and further attrition at 6 months, a simple, cheaper approach to intervention may be as clinically effective and more cost-effective than more complex and invasive approaches.

Constipation Predominant Irritable Bowel Syndrome and Functional Constipation Are Not Discrete Disorders: A Machine Learning Approach.

INTRODUCTION: Chronic constipation is classified into 2 main syndromes, irritable bowel syndrome with constipation (IBS-C) and functional constipation (FC), on the assumption that they differ along multiple clinical characteristics and are plausibly of distinct pathophysiology. Our aim was to test this assumption by applying machine learning to a large prospective cohort of comprehensively phenotyped patients with constipation. METHODS: Demographics, validated symptom and quality of life questionnaires, clinical examination findings, stool transit, and diagnosis were collected in 768 patients with chronic constipation from a tertiary center. We used machine learning to compare the accuracy of diagnostic models for IBS-C and FC based on single differentiating features such as abdominal pain (a "unisymptomatic" model) vs multiple features encompassing a range of symptoms, examination findings and investigations (a "syndromic" model) to assess the grounds for the syndromic segregation of IBS-C and FC in a statistically formalized way. RESULTS: Unisymptomatic models of abdominal pain distinguished between IBS-C and FC cohorts near perfectly (area under the curve 0.97). Syndromic models did not significantly increase diagnostic accuracy (P > 0.15). Furthermore, syndromic models from which abdominal pain was omitted performed at chance-level (area under the curve 0.56). Statistical clustering of clinical characteristics showed no structure relatable to diagnosis, but a syndromic segregation of 18 features differentiating patients by impact of constipation on daily life. DISCUSSION: IBS-C and FC differ only about the presence of abdominal pain, arguably a self-fulfilling difference given that abdominal pain inherently distinguishes the 2 in current diagnostic criteria. This suggests that they are not distinct syndromes but a single syndrome varying along one clinical dimension. An alternative syndromic segregation is identified, which needs evaluation in community-based cohorts. These results have implications for patient recruitment into clinical trials, future disease classifications, and management guidelines.