Treatment with ACE-inhibitors is associated with less severe disease with SARS-Covid-19 infection in a multi-site UK acute Hospital Trust

Daniel Bean; Zeljko Kraljevic; Thomas Searle; Rebecca Bendayan; Andrew Pickles; Amos Folarin; Lukasz Roguski; Kawsar Noor; Anthony Shek; Kevin o'gallagher; Rosita Zakeri; Ajay Shah; James Teo; Richard JB Dobson

This study used an NLP approach to perform very rapid analysis of high volume, unstructured real world clinical data. This however introduces the possibility of missing circumlocutory mentions of disease, symptoms or medications. We have mitigated against this by manually validating annotations in a subset of records and also verified ACEi and ARB annotations against inpatient electronic prescription data. Moreover, we have performed sensitivity analyses to test the impact of different criteria to define the ACEi exposed cohort on our results, finding that although not significant the OR remained <1.0 for ACEi exposure in all analyses. The lack of significance in the more strict analyses is likely due to the loss of power as some detections of ACEi medication are excluded. For the less strict analysis, the lack of significance may be due to noise introduced (e.g. prescription halted before the study period). The NLP output in the less strict analysis is also not manually reviewed and is highly likely to contain some irrelevant mentions e.g. previous allergic reaction. While the findings of robustness to bias due to unmeasured confounding increased our confidence, the need for replication in a larger sample remains.