Abstract
Up to 25% of inflammatory bowel disease (IBD) presents during childhood, often with severe and extensive disease, leading to significant morbidity including delayed growth and nutritional impairment. The classical approach to management has centred on differentiation into Crohn's disease (CD) or ulcerative colitis (UC), with subsequent treatment based on symptoms, results and complications. However, IBD is a heterogeneous condition with substantial variation in phenotype, disease course and outcome, so whilst effective treatment exists one size does not fit all. The ability to predict disease course at diagnosis, alongside tailoring medications based on response gives the potential for a more 'personalised approach'. The move to a pre-emptive strategy to prevent IBD-related complications, whilst simultaneously minimising side effects and long-term toxicity from therapy, particularly in those with relatively indolent disease, has the potential to revolutionise care. In very early-onset IBD, personalised approaches to diagnosis and management have become the standard of treatment enabling clinicians to significantly alter the outcomes of the few children with monogenic disease. However, the promise of discoveries in genomics, microbiome and transcriptomics in paediatric IBD has not yet translated to clinical application for the vast majority of patients. Despite this, the opportunity presents itself to apply data gathered at diagnosis and follow-up to predict which patients are likely to progress to complicated disease, which will respond well and which will require additional therapy. Using complex mathematics and innovative, cutting-edge machine learning (ML) techniques gives the potential to use this data to develop personalised clinical care algorithms to treat patients more effectively, reduce toxicity and improve outcome. In this review, we will consider current management of paediatric IBD, discuss how precision medicine is making inroads into clinical practice already, examine the contemporary studies applying data to stratify patients and explore how future management may be revolutionised by personalisation with clinical, genomic and other multi-omic data.