Maintenance therapy with infliximab or vedolizumab in inflammatory bowel disease is not associated with increased SARS-CoV-2 seroprevalence: UK experience in the 2020 pandemic

BackgroundThere has been great concern amongst clinicians and patients that immunomodulatory treatments for IBD may increase risk of SARS-CoV-2 susceptibility or progression to severe disease. MethodsSera from 640 patients attending for maintenance infliximab or vedolizumab infusions between April and June 2020 at the John Radcliffe Hospital (Oxford, UK) and Royal London Hospital (London, UK) were tested using the Abbott SARS-CoV-2 IgG assay. Demographic and clinical data were collated from electronic patient records and research databases. ResultsSeropositivity rates of 3.0% (12/404), 7.2% (13/180), and 12.5% (7/56) were found in the Oxford and London adult IBD cohorts and London paediatric IBD cohorts respectively. Seroprevalence rates in the Oxford adult IBD cohort were lower than that seen in non-patient facing health-care workers within the same hospital (7.2%). Seroprevalence rates of the London paediatric IBD cohort were comparable to a contemporary healthy cohort collected at the same hospital (54/396, 13.6%). ConclusionsSARS-CoV-2 seropositivity rates are not elevated in patients with IBD receiving maintenance infliximab or vedolizumab infusions. There is no rationale based on these data for elective interruption of maintenance therapy, and we recommend continuation of maintenance therapy. These data do not address the efficacy of vaccination in these patients.

Real-time data monitoring for ulcerative colitis: patient perception and qualitative analysis

BACKGROUND/AIMS: TrueColours ulcerative colitis (TCUC) is a comprehensive web-based program that functions through email, providing direct links to questionnaires. Several similar programs are available, however patient perspectives are unexplored. METHODS: A pilot study was conducted to determine feasibility, usability and patient perceptions of real-time data collection (daily symptoms, fortnightly quality of life, 3 monthly outcomes). TCUC was adapted from a web-based program for patients with relapsing-remitting bipolar disorder, using validated UC indices. A semi-structured interview was developed and audio-recorded face-to-face interviews were conducted after 6 months of interaction with TCUC. Transcripts were coded in NVivo11, a qualitative data analysis software package. An inductive approach and thematic analysis was conducted. RESULTS: TCUC was piloted in 66 patients for 6 months. Qualitative analysis currently defies statistical appraisal beyond “data saturation,” even if it has more influence on clinical practice than quantitative data. A total of 28 face-to-face interviews were conducted. Six core themes emerged: awareness, control, decision-making, reassurance, communication and burden of treatment. There was a transcending overarching theme of patient empowerment, which cut across all aspects of the TCUC experience. CONCLUSIONS: Patient perception of the impact of real-time data collection was extremely positive. Patients felt empowered as a product of the self-monitoring format of TCUC, which may be a way of improving self-management of UC whilst also decreasing the burden on the individual and healthcare services.

What's app? Electronic health technology in inflammatory bowel disease

Electronic health (eHealth) data collection is increasingly used in many chronic illnesses, to track pattern of disease. eHealth systems have the potential to revolutionize care. Inflammatory bowel disease (IBD) is a paradigm for such an approach: this is a chronic disease that usually affects young and technologically literate patient population, who are motivated to be involved in their own care. A range of eHealth technologies are available for IBD. This review considers the strengths and weaknesses of 7 platforms that focus on patient-provider interaction. These have been developed in Denmark, United States, the Netherlands, and the United Kingdom, demonstrating an international interest in this form of technology and interaction. Not only do these technologies aim to improve care but they also have the potential to collect large amounts of information. Information includes demographics and patient reported outcomes (symptoms, quality of life), quality of care (steroid use, among other metrics) and outcomes such as hospitalization. These data could inform quality improvement programmes to improve their focus. eHealth technology is also open to machine learning to analyze large data sets, through which personalized algorithms may be developed.