Hidradenitis Suppurativa International Online Community: Patient Characteristics and a Novel Model of Treatment Effectiveness.

Treatment for hidradenitis suppurativa is diverse, yet frequently unsatisfactory. The aims of this study were to create a reproducible artificial intelligence-based patient-reported outcome platform for evaluation of the clinical characteristics and comorbidities of patients with hidradenitis suppurativa, and to use this to grade treatment effectiveness. A retrospective patient- reported outcome study was conducted, based on online questionnaires completed by English-speaking patients registered to the hidradenitis suppurativa StuffThatWorks® online community. Data collected included patient characteristics, comorbidities and treatment satisfaction. These were recoded into scalable labels using a combination of machine learning algorithm, manual coding and validation. A model of treatment effectiveness was generated. The cohort included 1,050 patients of mean ± standard deviation age 34.3 ± 10.3 years. Greater severity of hidradenitis suppurativa was associated with younger age at onset (p < 0.001) and male sex (p < 0.001). The most frequent comorbidities were depression (30%), anxiety (26.4%), and polycystic ovary syndrome (16.6%). Hurley stage I patients rated topical agents, dietary changes, turmeric, and pain relief measures more effective than tetracyclines. For Hurley stage II, adalimumab was rated most effective. For Hurley stage III, adalimumab, other biologic agents, systemic steroids, and surgical treatment were rated more effective than tetracyclines. Patients with hidradenitis suppurativa often have comorbid psychiatric and endocrine diseases. This model of treatment effectiveness provides a direct comparison of standard and complementary options.

Self-reported treatment effectiveness for Crohn's disease using a novel crowdsourcing web-based platform.

BACKGROUND AND AIMS: Internet and social media platforms have become an unprecedented source for sharing self-experience, potentially allowing the collection and integration of health data with patient experience. StuffThatWorks (STW) is an online open platform that applies machine learning and the power of crowdsourcing, where patients with chronic medical conditions can self-report and compare their individual outcomes using a structured online questionnaire. We aimed to conduct a cross-sectional, international, crowdsourcing, artificial-intelligence (AI) web-based study of patients with Crohn's disease (CD) self-reporting their outcomes. METHODS: A proprietary STW Bayesian inference model was built to measure improvement in CD severity (on scale of 1-5) for each treatment and ranked treatments using effectiveness. The effectiveness of first-line biological treatments was analyzed by multiple comparisons and by calculating odds ratios and 95% confidence intervals for each treatment pair. RESULTS: We included 7593 self-reported CD patients for the analysis. Most of the participants were female (75.8%) and from English-speaking countries (95.7%). Overall, anti-TNF drugs were the most reported tried treatment (52.8%). Infliximab (IFX) was ranked as the most effective treatment by the STW effectiveness model followed by bowel surgery (second), adalimumab (ADA, third), ustekinumab (UST, 4rd), and vedolizumab (VDZ, fifth). In paired comparison analyses, IFX was most effective, ADA had similar effectiveness compared to UST and all three were more effective than VDZ. CONCLUSION: We present the first online crowdsourcing AI platform-based study of self-reported treatment effectiveness in CD. Net-based crowdsourcing patient-reported outcome platforms can potentially help both clinicians and patients select the best treatment for their condition.