The lived experience of functional bowel disorders: a machine learning approach

Objective Functional bowel disorders (FBDs) are multi-dimensional diseases varying in demographics, symptomology, lifestyle, mental health, and susceptibility to treatment. The patient lived experience is an integration of these factors, best understood with appropriately multivariate models. Methods In a large patient cohort (n=1175), we developed a machine learning framework to better understand the lived experience of FBDs. Iterating through 59 factors available from routine clinical care, spanning patient demography, diagnosis, symptomatology, life-impact, mental health indices, healthcare access requirements, COVID-19 impact, and treatment effectiveness, machine models were used to quantify the predictive fidelity of one feature from the remainder. Bayesian stochastic block models were used to delineate the network community structure underpinning the lived experience of FBDs. Results Machine models quantified patient personal health rating (R2 0.35), anxiety and depression severity (R2 0.54), employment status (balanced accuracy 96%), frequency of healthcare attendance (R2 0.71), and patient-reported treatment effectiveness variably (R2 range 0.08-0.41). Contrary to the view of many healthcare professionals, the greatest determinants of patient-reported health and quality-of-life were life-impact, mental wellbeing, employment status, and age, rather than diagnostic group and symptom severity. Patients responsive to one treatment were more likely to respond to another, leaving many others refractory to all. Conclusions The assessment of patients with FBDs should be less concerned with diagnostic classification than with the wider life impact of illness, including mental health and employment. The stratification of treatment response (and resistance) has implications for clinical practice and trial design, in need of further research. What is known? What is new here? ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement JKR was supported by the Medical Research Council (MR/X00046X/1). PN is supported by the Wellcome Trust (213038/Z/18/Z) and the UCLH NIHR Biomedical Research Centre. The PERSPECTIVE study was funded by MacGregor Healthcare Ltd. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by local institutional review board and conducted in accordance with the Declaration of Helsinki. The Health Research Authority approved this study prior to commencement. REC reference 21/SW/0086 (IRAS ID 296856). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All code will be made publicly available upon publication at https://github.com/jamesruffle/perspective-ai. Trained model weights are available upon request. Data and code availability is in line with UK government policy on open-source code. Patient data are not available for dissemination under the ethical framework that governs its use.