Crohn's disease (CD) is a highly heterogeneous disease, with great variation in patient severity. Using super- vised machine learning techniques to predict severity from common laboratory and clinical measurements, we found that high levels of C-reactive protein and low levels of lymphocytes and albumin are important predictive factors. Building upon this knowledge, we used extreme value theory to create a probabilistic model that combines information about behaviour in the extremes of these lab measurements to produce a single risk score over time. We then clustered these patient risk scores to identify several common clinical trajectories for CD patients. I. INTRODUCTION Crohn's disease (CD), a subtype of inflammatory bowel disease (IBD), is a chronic conditon defined by severe gastrointestinal (GI) inflammation. Its clinical manifestation varies greatly between patients. Some patients experience very mild disease that is easily controlled through anti- inflammatory medications. Others, however, are unable to achieve clinical remission even after multiple courses of anti-inflammatory drugs and immunosuppressants. Biologic therapy (anti-TNF ) and surgical removal of portions of the bowel are the only current treatment options for these latter patients. Approximately half of all CD patients will require GI surgery throughout the course of their life (1), (2), (3). Due to the large variance in presentation, it would be useful to be able to identify common patient trajectories and to use these to identify patients who will go on to develop more serious disease. Patients with high risk of severe disease could be more quickly treated with stronger second-line therapies and could be targeted for more frequent follow- up than they otherwise would. In addition, it has been well- established that there are both genomic and environmental factors that contribute to CD incidence. 163 genetic loci have been identified through genome-wide association studies to be associated with risk of CD (4). However, very little has been found regarding the association of genomic factors with disease severity. Patients with similar phenotypic patterns may share similar underlying genomic disease mechanisms. Patient risk scores over time provide one metric by which to begin such phenotypic clustering. Here, we are defining phenotypic characteristics to include any observable clini- cal features (e.g., blood measurements, age of onset, etc.), excluding genomic factors. Some classification schemes for CD, such as the Montreal classification, subdivide patients
['Kate Niehaus', 'Holm H. Uhlig', 'David A. Clifton']
Phenotypic characterisation of Crohn's disease severity.