Can dysplasia surveillance be better targeted in ulcerative colitis by using faecal calprotectin?

Background: In the inflammatory bowel diseases, chronic inflammation predisposes to dysplasia and colorectal carcinoma, leading to the need of surveillance colonoscopies. The most-used marker of colonic inflammation is faecal calprotectin. Its correlation with endoscopic and histological findings is well-documented. In this study, we evaluated the role of sequential faecal calprotectin measurements in predicting colorectal dysplasia, to identify patients with increased risk of dysplasia or colonic malignancy in ulcerative colitis.Methods: We collected the faecal calprotectin measurements and colorectal histology reports of patients with ulcerative colitis treated in Helsinki University Hospital (Helsinki, Finland) between 2007 and 2017, with a focus on IBD-associated neoplasia, inflammatory activity, and sporadic adenomas. Using the time-weighted AUC of faecal calprotectin as a marker of inflammatory burden, we tested the performance of faecal calprotectin to predict the risk for colorectal neoplasia.Results: In total, 982 patients with ulcerative colitis were included. Of them, 845 had pancolitis and 127 concomitant primary sclerosing cholangitis. Forty-one patients (4%) had IBD-associated colorectal dysplasia and seven (0.7%) developed adenocarcinoma. In patients with constantly elevated faecal calprotectin level (>500 µg/g), colorectal neoplasia was more frequent compared to those with low (<200 µg/g) calprotectin (13% and 4%, p < 0.05). Histological inflammatory activity was also related to more frequent dysplastic changes.Conclusions: Colon dysplasia and adenocarcinoma are more common among ulcerative colitis patients with constantly elevated faecal calprotectin than in patients in remission. The role of inflammatory activity in inducing neoplastic changes in colon is further supported by histology, as histological inflammatory activity correlates with dysplasia.

Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study.

INTRODUCTION: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. OBJECTIVE: The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. METHODS: Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. RESULTS: Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15-60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. CONCLUSIONS: Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost-benefit of integrating these analyses at this stage of signal management requires further research.