Background: Identification of biomarkers that predict severe Crohn's disease is an urgent unmet research need, but existing research is piecemeal and haphazard.
Objective: To identify biomarkers that are potentially able to predict the development of subsequent severe Crohn's disease.
Design: 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).
Data sources: PubMed and EMBASE searched from inception to 1 January 2016, updated to 1 January 2018.
Review methods: 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.
Results: 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).
Limitations: 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.
Conclusions: 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.
Future work: 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.
Study registration: This study is registered as PROSPERO CRD42016029363.
Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 45. See the NIHR Journals Library website for further project information.
Keywords: BIOLOGICAL MARKERS; BIOMARKERS; CROHN’S DISEASE; DIAGNOSTIC ACCURACY; META-ANALYSIS; PREDICTION; PROGNOSIS; REVIEW, SYSTEMATIC.
Crohn’s disease causes inflammation of the intestines. Traditional treatment uses drugs, such as steroids, at a gradually increasing dose as symptoms worsen. Newer ‘biological’ drugs may stop disease, but are not used as an early treatment because they are expensive and have serious side effects. Using biologicals early means knowing which patients will develop severe disease in the future. A ‘prognostic biomarker’ is a measurement made on a patient that predicts a future outcome. A lot of research has attempted to identify biomarkers that predict severe Crohn’s disease, but research is haphazard and of variable quality. We therefore carried out a ‘systematic review’, which identifies research in a comprehensive and unbiased fashion. We found nearly 30,000 research papers, 71 of which were acceptable quality and described 56 groups of Crohn’s disease patients. We then used a statistical method called ‘meta-analysis’ to combine results from multiple studies. This allowed us to identify the most promising biomarkers to predict future severe disease. We found five clinical biomarkers (e.g. age and smoking), two blood biomarkers and one genetic biomarker that seemed reasonably able to predict future severe Crohn’s disease. However, we also found that most research was poorly performed and frequently confused diagnosis (current disease) with prognosis (future disease). Some commonly used biomarkers were not sufficiently investigated. We were surprised to identify so few prognostic biomarkers in the face of a seemingly vast amount of research. Future research should be better conducted and not confuse diagnosis with prognosis. We will use statistical methods to combine the promising biomarkers that we identified into a ‘prognostic model’, which is a mathematical formula that provides the likelihood of developing severe disease in the future. We will then test how well this works by using patient data from existing Crohn’s disease databases.