Picco, Michael F.

AbstractIntroductionUlcerative colitis (UC) associated with primary sclerosing cholangitis (PSC) is a rare phenotype. We aimed to assess patients with UC-PSC or UC alone and describe differences in clinical and phenotypic characteristics, antitumor necrosis factor (TNF) therapy, and long-term clinical outcomes.MethodsThis retrospective multicenter cohort study included patients who received a diagnosis of UC from 1962 through 2015. We evaluated clinical factors associated with UC-PSC vs UC alone and assessed associations by using multivariable logistic regression models.ResultsAmong 522 patients with UC, 56 (10.7%) had PSC. Compared with UC alone, patients with UC-PSC were younger (younger than 20 years) at diagnosis (odds ratios [OR], 2.35; adjusted P = 0.02) and had milder UC severity (adjusted P = 0.05), despite having pancolonic involvement (OR, 7.01; adjusted P < 0.001). In the biologics era (calendar year 2005 to 2015), patients with UC-PSC less commonly received anti-TNF therapy compared with patients with UC (OR, 0.38; adjusted P = 0.009), but their response rates were similar. Fewer patients with UC-PSC received corticosteroids (OR, 0.24; adjusted P = 0.005) or rectal 5-aminosalicyte acid (OR, 0.26; adjusted P < 0.001). Other differences were identified that were not statistically significant in a multivariable model: patients with UC-PSC more commonly were male, had lower rates of smoking, and had higher rates of colorectal cancer and colectomy.DiscussionThis study identified a unique phenotype of UC with concurrent PSC, which had different clinical behavior compared with UC only. These phenotypic characteristics can help identify high-risk patients with UC before PSC is diagnosed and guide different management and monitoring strategies.

INTRODUCTION: Mucosal healing is a widely accepted target for ulcerative colitis (UC) treatment. Linked color imaging (LCI) is a novel endoscopic enhancement system that utilizes Light Emitting Diode (LED) sources to spectrally enhance subtle erythema and mucosal surface patterns which may improve endoscopic classification of IBD associated inflammation. We aimed to assess the accuracy of LCI in classifying UC-associated mucosal healing compared to a validated histological reference standard (Geboes score). METHODS: 22 patients with ulcerative colitis were prospectively enrolled and evaluated by LCI colonoscopy using the Fujifilm’s digital video Processor VP-7000 and LED light source BL-7000 (Fujifilm, Tokyo Japan). Interobserver agreement was reported as kappa values. Geboes score was reported as score range of 0 to 5.4. A Geboes score of ≥3.1 (presence of surface intraepithelial neutrophils or < 5% crypts) was used to define histologic activity and a score of < 2B (no increase in neutrophils in the lamina propria) was used to define histologic remission. Geboes score has been previously validated to predict clinical recurrence. We calculated the overall accuracy, positive predictive value (PPV) and negative predictive value (NPV) of the LCI classification identifying mucosal healing against the Geboes score using the Fisher’s exact test. RESULTS: Total of 62 endoscopic images were obtained for different parts of the colon, 43 of which were matched to a corresponding biopsy. Endoscopic images were scored by 2 endoscopists into LCI-A (no redness), LCI-B (redness with visible vessels) and LCI-C (redness without visible vessels). A final LCI score was given for images with discrepant scores based on a consensus vote from both endoscopists. The interobserver agreement between the two endoscopists was 81%, kappa of 0.71 (95% confidence interval [CI] 0.57- 0.86). Among regions scored ≥3.1 on Geboes score, 9.5%, 28.6% and 61.9 % were classified as LCI-A, LCI-B and LCI-C, respectively ( P < 0.0001). Among images scored < 2B on Geboes score, 81.8%, 13.6% and 4.6% were classified as LCI-A, LCI-B and LCI-C, respectively ( P < 0.0001). Analyzing data against histology with LCI-A representing mucosal healing and LCI-B and C representing active mucosal disease, the overall accuracy becomes 86%, with NPV of 82.6% and PPV of 90% for identifying UC patients with histological and endoscopic evidence of mucosal healing. CONCLUSION: LCI technology has a potential role in assessing mucosal healing in UC.

AbstractBackgroundIt is important to identify patients with inflammatory bowel disease [IBD] refractory to anti-tumour necrosis factor [TNF] therapy, to avoid potential adverse effects and to adopt different treatment strategies. We aimed to identify and validate clinical and genetic factors to predict anti-TNF response in patients with IBD.Materials and MethodsMayo Clinic and Washington University IBD genetic association study cohorts were used as discovery and replicate datasets, respectively. Clinical factors included sex, age at diagnosis, disease duration and phenotype, disease location, bowel resection, tobacco use, family history of IBD, extraintestinal manifestations, and response to anti-TNF therapy.ResultsOf 474 patients with IBD treated with anti-TNF therapy, 41 [8.7%] were refractory to therapy and 433 [91.3%] had response. Multivariate analysis showed history of immunomodulator use (odds ratio 10.2, p = 8.73E-4) and bowel resection (odds ratio 3.24, p = 4.38E-4) were associated with refractory response to anti-TNF agents. Among genetic loci, two [rs116724455 in TNFSF4/18, rs2228416 in PLIN2] were successfully replicated and another four [rs762787, rs9572250, rs144256942, rs523781] with suggestive evidence were found. An exploratory risk model predictability [area under the curve] increased from 0.72 [clinical predictors] to 0.89 after adding genetic predictors. Through identified clinical and genetic predictors, we constructed a preliminary anti-TNF refractory score to differentiate anti-TNF non-responders (mean [standard deviation] score, 5.49 [0.99]) from responders (2.65 [0.39]; p = 4.33E-23).ConclusionsNovel and validated genetic loci, including variants in TNFSF, were found associated with anti-TNF response in patients with IBD. Future validation of the exploratory risk model in a large prospective cohort is warranted.