Authors' objectives: Identification of biomarkers that predict severe Crohn’s disease is an urgent unmet research need, but existing research is piecemeal and haphazard. To identify biomarkers that are potentially able to predict the development of subsequent severe Crohn’s disease.

Authors' results and conclusions: In total, 29,950 abstracts yielded just 71 individual studies, reporting 56 non-overlapping cohorts. Five clinical biomarkers (Montreal behaviour, age, disease duration, disease location and smoking), two serological biomarkers (anti-Saccharomyces cerevisiae antibodies and anti-flagellin antibodies) and one genetic biomarker (nucleotide-binding oligomerisation domain-containing protein 2) displayed statistically significant prognostic potential. Overall, the strongest association with subsequent severe disease was identified for Montreal B2 and B3 categories (odds ratio 4.09 and 6.25, respectively). Research for individual biomarkers to predict severe Crohn’s disease is scant, heterogeneous and at a high risk of bias. Despite a large amount of potential research, we encountered relatively few biomarkers with data sufficient for meta-analysis, identifying only eight biomarkers with potential predictive capability.

Authors' methods: This was a prognostic systematic review with meta-analysis reserved for those potential predictors with sufficient existing research (defined as five or more primary studies). PubMed and EMBASE searched from inception to 1 January 2016, updated to 1 January 2018. Eligible studies were studies that compared biomarkers in patients who did or did not subsequently develop severe Crohn’s disease. We excluded biomarkers that had insufficient research evidence. A clinician and two statisticians independently extracted data relating to predictors, severe disease definitions, event numbers and outcomes, including odds/hazard ratios. We assessed risk of bias. We searched for associations with subsequent severe disease rather than precise estimates of strength. A random-effects meta-analysis was performed separately for odds ratios. Definitions of severe disease varied widely, and some studies confounded diagnosis and prognosis. Risk of bias was rated as ‘high’ in 92% of studies overall. Some biomarkers that are used regularly in daily practice, for example C-reactive protein, were studied too infrequently for meta-analysis.

Authors' identified further research: We will use existing data sets to develop and then validate a predictive model based on the potential predictors identified by this systematic review. Contingent on the outcome of that research, a prospective external validation may prove clinically desirable.

Project Status: Completed

URL for project: https://www.journalslibrary.nihr.ac.uk/programmes/hta/1421007

Year Published: 2021

URL for published report: https://www.journalslibrary.nihr.ac.uk/hta/hta25450

URL for additional information: English

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: England, United Kingdom

DOI: 10.3310/hta25450

Organisation Name: NIHR Health Technology Assessment programme

Contact Address: NIHR Journals Library, National Institute for Health and Care Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK

Contact Name: journals.library@nihr.ac.uk

Contact Email: journals.library@nihr.ac.uk

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