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

Data Processing: The data (demographic, emergency department letters, discharge summaries, clinical notes, radiology reports, medication orders, lab results) was retrieved and analyzed in near real-time from the structured and unstructured components of the electronic health record (EHR) using a variety of natural language processing (NLP) informatics tools belonging to the CogStack ecosystem, 8 namely DrugPipeline, 9 MedCAT 10 and MedCATTrainer. 11 The CogStack NLP pipeline captures negation, synonyms, and acronyms for medical SNOMED-CT concepts as well as surrounding linguistic context using deep learning and long short-term memory networks. DrugPipeline was used to annotate medications and MedCAT produced unsupervised annotations for all SNOMED-CT concepts under parent terms Clinical Finding, Disorder, Organism, and Event with disambiguation, pre-trained on MIMIC-III. 12 Further supervised training improved detection of annotations and meta-annotations such as experiencer (is the concept annotated experienced by the patient or other), negation (is the concept annotated negated or not) and temporality (is the concept annotated in the past or present ) with MedCATTrainer. Meta-annotations for hypothetical and experiencer were merged into Irrelevant meaning that any concept annotated as either hypothetical or where the experiencer was not the patient was annotated as irrelevant. Performance of the MedCAT NLP pipeline for disorders mentioned in the text was evaluated on 138 documents by 4 annotators (TS, ZK, DB, AS) and F1, precision and recall recorded. Additional full case review for correct subsequent diagnosis assignment was performed by 3 clinicians (JT, KOG, RZ) for key comorbidities: Diabetes Mellitus, Hypertension, Heart Failure and Ischaemic Heart Disease. Performance of DrugPipeline has previously been described. 9 All detected drug mentions were manually reviewed to exclude false positives (e.g. allergy).