The SARS-Cov2 pandemic is a major medical and socioeconomic challenge with at least 3 million confirmed cases to date. Data on the clinical characteristics of patients who require hospital admission for COVID19 disease from China, Italy and the US consistently show that patients with cardiovascular comorbidities are over-represented and may have an increased risk of severe COVID19 disease. [1] [2] [3] The reasons underlying the increased incidence of severe COVID19 infection in those with comorbidities such as hypertension, diabetes and other cardiovascular conditions are unknown.
The SARS-Cov2 virus requires the binding of its viral surface spike protein to the ACE2 receptor expressed on epithelial cells in order to be internalised and then undergo replication. 4 Previous studies suggest that the expression of ACE2 may be increased by chronic treatment with ACEi or ARB. 5 As such, it has been hypothesized that treatment with ACEi or ARB could increase the likelihood of SARS-Cov2 binding and entry into epithelial or other cells. 6 Furthermore, it is hypothesised that such a mechanism could account for the increased incidence of severe COVID19 infection among patients with cardiovascular comorbidities, who are frequently treated with ACEi/ARB. 6 Whether or not treatment with ACEi/ARB increases the risk of severe COVID19 disease is a very important question in view of the large numbers of patients potentially on these drugs, especially in western countries with older populations. The issue is controversial because ACEi/ARB may potentially be beneficial in severe lung injury by reducing activation of the renin angiotensin system (RAS). 7-10 Furthermore, increased levels of ACE2 itself have been shown to be protective during severe lung injury. 11, 12 The potential effect of ACEi and ARB during infection with SARS-CoV-2 therefore requires urgent clarification.
We tested for association between treatment with ACEi/ARB and disease severity in a consecutive series of 1200 patients with COVID19 disease admitted to two UK hospitals, King's College Hospital and Princess Royal University Hospital, that have been at the epicentre of the pandemic in London. We used an established and validated informatics pipeline to allow rapid evaluation of this important question during the pandemic.
This article is protected by copyright. All rights reserved. This project operated under London South East Research Ethics Committee approval (reference   18/LO/2048) granted to the King's Electronic Records Research Interface (KERRI); specific work on   COVID19 research was reviewed with expert patient input on a virtual committee with Caldicott Guardian oversight.
The study cohort was defined as all adult inpatients testing positive for SARS-Cov2 by RT-PCR at King's College Hospital and Princess Royal University Hospital from 1st March to 13th April 2020.
Only symptomatic patients who required inpatient admission were included. Presenting symptoms included but were not limited to fever, cough, dyspnoea, myalgia, chest pain or delirium. The primary endpoint was defined as death or admission to a critical care unit for organ-support within 21 days of symptoms onset. Data were collected for a range of clinical and demographic parameters (Table 1) . To ascertain chronic treatment with ACEi, ARB and other relevant medications, we captured information from clinical notes, outpatient clinic letters and inpatient medication orders. If a drug was a regular medication in the community but withheld on admission, we considered this to be on chronic treatment.
The primary endpoint was manually verified by clinician review of the electronic health record.
The data (demographic, emergency department letters, discharge summaries, clinical notes, radiology reports, medication orders, lab results) was retrieved and analysed in near real-time from the structured and unstructured components of the electronic health record (EHR) using a variety of wellvalidated natural language processing (NLP) informatics tools belonging to the CogStack ecosystem, 13 namely DrugPipeline, 14 MedCAT 15 and MedCATTrainer. 16 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. 17 Further supervised training improved detection of annotations and meta-annotations such This article is protected by copyright. All rights reserved.
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 5617 annotations for 265 documents by a domain expert (JTHT) and F1, precision and recall recorded. Additional full case review for correct subsequent diagnosis assignment was performed by 3 clinicians (JTHT, KOG, RZ) for key comorbidities. The performance of DrugPipeline has previously been described. 14 Manual review of 100 detections gave F1=0.91 for exclusion of drug allergies by DrugPipeline.
In order to investigate the association between ACEi/ARB and disease severity measured as critical care admission or death, we performed a series of logistic regressions. In a first step, we explored independently the association for ACEi/ARB (Baseline model). In a second step, we adjusted the model for age and sex (Model 1). Then, we additionally adjusted for hypertension (Model 2) and finally, additionally adjusted for other comorbidities, i.e. diabetes, ischemic heart disease, heart failure and chronic kidney disease (Model 3). We also explored the independent association for hypertension following the same modelling approach. In addition, we assessed the robustness to unmeasured confounders of the fully adjusted estimate of ACEi/ARB effect using the e-value approach, which are defined as the minimum strength of association on the risk-ratio scale that an unmeasured confounder would need to have with both the treatment assignment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates 18 . Sensitivity analyses were performed i) requiring at least two detections of medication for positive exposure; ii) using only structured data on in-hospital medication orders; iii) ignoring our 21 day window for medications; iv) testing sensitivity to unmeasured confounders.
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The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Our total cohort consisted of 1200 confirmed positive symptomatic inpatients aged 63+20 (SD) years with 52% being male ( Table 1) We next compared the outcome of patients on chronic treatment with ACEi/ARB versus those not on these agents. The group on ACEi/ARB were significantly older but had a similar male/female split and a similar ethnicity profile to those not on ACEi/ARB ( Table 1) . The BMI was similar between groups. There was a greater proportion of patients with cardiovascular comorbidities (hypertension, diabetes, heart failure, ischaemic heart disease) and chronic kidney disease in the group on ACEi/ARB than those not taking these drugs, as would be expected. Therefore, the patients on ACEi/ARB had a higher prevalence of factors associated with worse outcome of COVID19 disease in prior studies. [1] [2] [3] The
ACEi/ARB group also had higher rates of treatment with beta blockers and statins than those not on ACEi/ARB, consistent with their higher rates of cardiovascular morbidities. Figure 2 shows Kaplan-Meier curves for the primary end-point in patients on ACEi/ARB and those not on these drugs. This article is protected by copyright. All rights reserved.
To assess the independent effect of ACEi/ARB on primary outcome, we first performed an unadjusted logistic regression analysis. This indicated that the likelihood of a severe outcome was similar in individuals on ACEi/ARB as compared to those not on these drugs, with an Odds Ratio (OR) of 0·83 (CI 0·64-1·07) -Baseline Model, Table 2 . However, after adjustments for age and sex (Model 1 in Table 2 ), the likelihood of severe disease was significantly lower in those on ACEi/ARBs (OR 0.70 [0.53-0.91], p<0.01). Additional adjustment for hypertension (Model 2 in Table 2 ) and for the other major comorbidities, diabetes, chronic kidney disease, and ischaemic heart disease/heart failure (Model 3 in Table 2 We also examined the independent association between hypertension and disease severity. The results showed that individuals with hypertension had a similar likelihood of suffering a severe outcome as those that without hypertension, either in unadjusted models (OR 1·25 [CI 0·98-1·59]; p=0·069) or in models adjusted for age and gender (OR 1·03 [CI 0·80-1·32]; p=0·83).
Sensitivity analyses were performed using criteria for ACEi exposure that were either more strict (requiring multiple mentions in the clinical notes or using only in-hospital medication orders as evidence) or less strict (including any mention of ACEi treatment even outside a 21 day window from onset of symptoms). In all cases, the estimates of the impact of ACEi treatment were consistent with those in Table 2 . In analysis requiring at least two mentions of chronic treatment with ACEi/ARB, we found that this was significant in the uncorrected baseline model (a lower OR). We estimated an e-value of 1.82 which suggests that the estimate, though clearly significant, could yet be vulnerable to possible
confounders not yet included.
This study in a large consecutive cohort of 1200 patients in the UK suggests that chronic treatment with ACEi and ARB is not associated with an increase in severe outcome of COVID19 disease, defined as death or admission to a critical care unit. The hypothetical relationship between This article is protected by copyright. All rights reserved.
Accepted Article treatment with ACEi/ARB and severe COVID19 disease has been intensely debated. 6, 9, 8 There are theoretical mechanisms whereby chronic treatment with ACEi/ARB might increase propensity to SARS-CoV2 infection as well as other mechanisms whereby treatment with these agents might be beneficial. It is a particularly important question because chronic treatment with ACEi/ARB is of proven benefit in conditions such as hypertension, diabetes, chronic kidney disease and heart failure and an unwarranted cessation of therapy in patients with these conditions as a result of the SARS-CoV2 pandemic could have serious long-term detrimental effects.
The general clinical characteristics and the rates of severe outcome of the patients in our study were broadly similar to those that have been described in recent large series from Italy and the USA. [1] [2] [3] We found that patients who were on chronic treatment with ACEi/ARB had many demographic and comorbidity features that have been associated in previous studies with worse outcome in COVID-19 disease, such as an older age and a higher prevalence of hypertension, diabetes, heart failure and other morbidities 1-3 . Treatment with ACEi/ARB was nevertheless not associated with an increase in rates of severe outcomes, with or without adjustment for age, sex and comorbidities. A number of very recent studies have now also reported on the relationship between ACEi/ARB and outcome of COVID-19 disease in hospitalised patients. In a single centre study from Wuhan in which only 115 of 1178 patients (<10%) were taking ACEi/ARB, the authors did not find any relationship between these drugs and outcome; 19 the data are however limited by the low numbers on ACEi/ARB and potential confounding by other factors. A second report from China was a retrospective multi-centre study including 1128 patients but again had only 188 patients (16.6%) on treatment with ACEi/ARB. 20 This study suggested that treatment with ACEi/ARB was associated with a lower rate of severe outcome with COVID19 infection. Mehra et al. 21 published a series of 8910 patients with COVID19 from 169 hospitals in which data was extracted through the Surgical Outcomes Collaborative, and also reported that treatment with ACEi was associated with a significantly lower OR for death. Although at first sight this study has the advantage of scale, the population reported was much younger (average age ~50 years) and had unexpectedly low prevalence of hypertension (25%) and diabetes (18%) than in most other large reports. 2,3 Furthermore, the proportion of patients on treatment with ACEi/ARB was very low (less than 9%), 21 raising significant concerns about the translatability of the findings. Mancia et al. 22 reported a This article is protected by copyright. All rights reserved. Accepted Article case control series from Italy in which 617 patients had severe COVID19 disease among 6272 SARS-CoV2 positive cases, and the rates of ACEi/ARB usage were higher. These authors found no association between ACEi/ARB and the likelihood of infection or fatal disease. However, no data on ethnicity were included in this study. 22 At the time of submission, our study was the first to be conducted on an ethnically mixed population in the western world and to include significant proportions of both White and minority ethnic (Black, Asian) patients. The rates of usage of ACEi/ARB in our study (33.2%) are in line with those expected in well-treated patients with comorbidities and are therefore, in principle, more applicable to patients in Europe and the Americas. Ethnicity is a very pertinent issue in this regard due to the recognised ethnicity-related differences in response to drugs affecting the RAS. 23, 24 Of relevance, the ethnicity profiles of the patients on ACEi/ARB in our study were similar to those not taking these drugs. A recent study from New York also reports on a multi-ethnic population among whom 1002 patients developed severe COVID19 illness. 25 These authors found no evidence of an increased risk of severe COVID19 in patients taking ACEi or ARB. Finally, an analysis of patients with heart failure reports no association between ACEi/ARB treatment and the concentrations of plasma ACE2. 26 Although the relevance of plasma ACE2 to susceptibility to SARS-CoV2 (which binds to cell surface ACE2 4 ) is unclear, this study also fails to provide evidence in support of the theoretical risks of ACEi/ARB with respect to COVID19.
In the current study, when we adjusted for age, sex and comorbidities in logistic regression analyses, the OR for a severe outcome was significantly lower in patients on ACEi/ARB than those not on these agents. This suggestion of a favourable association of treatment with ACEi/ARB and less severe outcome in COVID19 disease would be consistent with the hypothesised beneficial effects of inhibition of RAS activation in patients with severe lung injury or Acute Respiratory Distress Syndrome (ARDS). 7, 8 However due to possibility of unmeasured confounding factors, the confirmation of a potential therapeutic benefit of treatment with ACEi/ARB in COVID19 disease would require further studies and randomised control trials.
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 mitigated against this by manually validating annotations in a This article is protected by copyright. All rights reserved.
subset of records and also verifying drug treatments against inpatient electronic prescription data.
Moreover, we performed sensitivity analyses to test the impact of different criteria to define the ACEi/ARB exposed cohort on our results, and found that the OR remained <1.0 and significant for ACEi/ARB exposure in all adjusted analyses. We therefore consider our analysis pipeline to be robust to specific details of the pipeline that are not clinically relevant. However we did find that the estimated odds ratio may be sensitive to unmeasured confounding, which suggests caution in the interpretation of any protective effect and the need for replication in a larger sample remains.
Our study has some potential limitations. Although the patients and data were prospectively collected, the analyses were retrospective. The study was conducted on two hospital sites in a single geographical, albeit ethnically mixed, locus in the UK over a relatively short follow-up period.
However, the duration of follow-up is sufficient to accurately detect early severe outcomes based on the data from multiple studies during the current pandemic. We used the covariates identified as important in the previous large case series on COVID19 1-3 , including age, sex and common comorbidities, to adjust our analyses. However, it is possible that other unmeasured confounders could have influenced the results. For example, the patients on chronic ACEi/ARB treatment were also more frequently treated with statins than those not on these drugs, which could suggest that their medical conditions were generally better managed. However, the ACEi/ARB group was also older and had higher rates of hypertension, diabetes and multiple morbidities, making it unlikely that these patients were physiologically healthier. Our study was performed in patients with COVID19 who required hospitalisation; the effect of chronic treatment with ACEi/ARB on less severe infection with SARS-CoV2 in the non-hospital setting requires further study. Whether the current results are applicable to other global populations, such as in Africa, will also require additional study. This article is protected by copyright. All rights reserved. Figure 1. The percentage of patients that have a positive mention of a disorder in each of the two groups (Dead or Critical Care, Other). Dead or Critical Care -patients that had died or were admitted to the Critical Care Unit; and Other -patients that were alive and had not been admitted to the Critical Care by day 21. All diseases were extracted from free-text using Cogstack and MedCAT. Only medical concept annotations with F1 > 80%, more than 10 annotated samples and present in at least 10% of either group are shown. Disease names that start "Any: " are aggregate concepts for multiple specific conditions that are used in our analysis.