CogStack KCH COVID-19 Analyses
Treatment with ACE-inhibitors is associated with less severe disease with SARS-Covid-19 infection in a multi-site UK acute Hospital Trust
Daniel M Bean1,2+, Zeljko Kraljevic1, Thomas Searle1, Rebecca Bendayan1,4, Andrew Pickles1, Amos Folarin1,2,3,7, Lukasz Roguski2,3,7, Kawsar Noor2,3,7, Anthony Shek8, Kevin O’Gallagher5,6, Rosita Zakeri5,6, Ajay M Shah5,6, James TH Teo5,8+*, Richard JB Dobson1,2,3,4,7+*
*joint author
+corresponding authors: Dan Bean (daniel.bean@kcl.ac.uk); James Teo (jamesteo@nhs.net; +44(0) 20 7828 0800); Richard Dobson (richard.j.dobson@kcl.ac.uk; +44(0) 20 7848 0473)
1. Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, U.K. 2. Health Data Research UK London, University College London, London, U.K. 3. Institute of Health Informatics, University College London, London, U.K. 4. NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, U.K. 5. Kings College Hospital NHS Foundation Trust, London, U.K. 6 School of Cardiovascular Medicine & Sciences, King’s College London British Heart Foundation Centre of Excellence, London SE5 9NU, U.K. 7. NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London. 8. Dept of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London
Abstract:
Background: The SARS-Cov2 virus binds to the ACE2 receptor for cell entry. It has been suggested that ACE-inhibitors, which are commonly used in patients with hypertension or diabetes and which raise ACE2 levels, may increase the risk of severe COVID-19 infection.
Methods: We evaluated this hypothesis in an early cohort of 205 acute inpatients with COVID-19 at King’s College Hospital and Princess Royal University Hospital, London, UK with the primary endpoint being death or transfer to a critical care unit for organ support within 7-days of symptom onset.
Findings: 53 patients out of 205 patients reached the primary endpoint. Contrary to the hypothesis, treatment with ACE-inhibitors was associated with a reduced risk of rapidly deteriorating severe disease. There was a lower rate of death or transfer to a critical care unit within 7 days in patients on an ACE-inhibitor OR 0·29 (CI 0·10-0·75, p<0·01), adjusting for age, gender, comorbidities (hypertension, diabetes mellitus, ischaemic heart disease and heart failure).
Interpretation: Although a small sample size, we do not see evidence for ACE-inhibitors increasing the short-term severity of COVID-19 disease and patients on treatment with ACE-inhibitors should continue these drugs during their COVID-19 illness. A potential beneficial effect needs to be explored as more data becomes available. Our pipeline will provide regular updates of the analysis as numbers increase at https://cogstack.org/cogstack-kch-covid-19-analyses/
Citation
Bean, Dan & Kraljevic, Zeljko & Searle, Thomas & Bendayan, Rebecca & Folarin, Amos & Roguski, Lukasz & Noor, Kawsar & Shek, Anthony & O’Gallagher, Kevin & Zakeri, Rosita & Shah, Ajay & Teo, James & Dobson, Richard. (2020). Treatment with ACE-inhibitors is associated with less severe disease with SARS-Covid-19 infection in a multi-site UK acute Hospital Trust. 10.13140/RG.2.2.34883.14889.
Results:
Our total cohort consists of 205 confirmed positive symptomatic inpatients aged 6320 (SD) years and 52% males (Table 1). Baseline characteristics are 51·2% with hypertension, 30·2% with diabetes and 14·6% with ischaemic heart disease or heart failure. The percentage of patients that have a positive mention of a certain disorder in each of the two groups (Dead or Critical Care, Other) derived via the NLP for medical concept annotations with F1 > 80% and more than 10 annotated mentions are shown in Figure 1 (performance shown in Figure 2). All NLP-detected positive mentions of hypertension, diabetes, ischaemic heart disease or heart failure were manually reviewed at a patient level and false positive rates calculated 1·9%, 3·2%, 31%, 0% respectively.
205 patients positive for COVID-19 at King’s College Hospital NHS Foundation Trust. All variables were complete and shown as N (% of column) except age which is mean (SD). ACEi = Angiotensin converting enzyme inhibitor; ARB = Angiotensin 2 Receptor Blocker.
Of the 205 patients, 53 patients died or required critical care support within 7 days of symptoms and 152 patients did not. The inclusion criteria of only patients needing admission is likely why this critical outcome figure is relatively high (25·9%) compared to fatality rate in population studies but is comparable to hospital case series. 14% (5/37) patients with exposure to an ACE-inhibitor died or required critical care support compared to 29% (48/168) for patients without such exposure.
Findings from unadjusted logistic regression models indicated that individuals on ACEi had lower likelihood of severe disease (OR 0·42 (CI 0·14-1·00), p=0·058). These associations were only partially attenuated when adjustments for gender and age were included (Model 1 in Table 2). Furthermore, these associations remained significant and were only partially attenuated when the model was additionally adjusted for hypertension (Model 2 in Table 2) and further for other comorbidities diabetes and ischaemic heart disease or heart failure (Model 3 in Table 2). Odds ratios and p-values for all variables in each model are shown in Supplementary Table 1. Males were found to have a higher likelihood of severe disease in Model 3 (OR 2.00 (CI 1.00-4.00), p=0.037)
Odds ratios and p-values calculated from logistic regressions applying Firth’s correction. ACEi = Angiotensin converting enzyme inhibitor. OR = Odds ratio.
We also examined the independent association between hypertension and disease severity. Our results showed that individuals diagnosed with hypertension had a similar likelihood of developing severe disease as those that were not diagnosed with hypertension, either in unadjusted models (OR 1·60 (CI 0·88-3·10); p=0·12) or models adjusted for age and gender (OR 1·80 (CI 0·83-3·80); p=0·14).
We did not run the regression analysis on the ARB group as there are only 9 patients in our cohort. We intend to carry out this analysis as our cohort grows.
Sensitivity analyses showed similar results when compared with the results from exact logistic regression analyses with univariate adjustment. We also compared our results to those using the penalised regression model and criteria for ACEi exposure that were either more strict (requiring multiple mentions or using only medications ordered in hospital) and less strict (including any detection of ACEi outside our 7 day window). In all cases we found that estimates of the impact of ACEi exposure were consistently in the same direction as those in Table 2 but were not significant.
Discussion:
This study suggests that ACE-inhibitors do not increase the severity of COVID-19 disease as hypothesised but may reduce severity. This holds true even after adjusting for conditions where ACEi may be used (hypertension, diabetes mellitus, ischaemic heart disease and heart failure). No meaningful comment can be made about ARB effect given the low prevalence of their use in this cohort, although ARB have a different mechanism of action compared to ACEi.
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.
One limitation with this study is the relatively small sample taken from a single UK centre over a short follow-up. Although we have used statistical procedures to provide robust results with our current sample, as numbers increase further updates to the analysis will be required to better understand our findings and confirm the directionality of these associations. Our group will provide regular updates during the pandemic to the analysis at the link https://cogstack.org/cogstack-kch-covid-19-analyses/ including longer follow-up and pooled analysis with other organisations. Whether these results also apply to infection severity in the non-hospital setting or to different global populations requires further study.
A tentative favourable association of ACE-inhibitors with less severe early outcomes is suggested by this study. A putative mechanism could be reduced RAS activation in patients on ACE-inhibitors, which is considered protective in Acute Respiratory Distress Syndrome, ARDS. Furthermore, elevation of ACE2 also reduces RAS activation and is protective in acute lung injury, including in ARDS of SARS1 infection.
In summary, based on these early results and the absence of any evidence suggesting harm, patients on treatment with ACE-inhibitors should continue these drugs during their COVID-19 illness as per current guidelines. Active research is merited on whether ACE inhibition or enhancement of ACE2 may have a therapeutic role in severe COVID-19 disease.
Appendices:
Dead or Critical Care – patients that have died or that are in the Intensive Treatment Unit; and Other – patients that are neither dead nor in ITU at day 7. All diseases were extracted from free-text using Cogstack and MedCAT. Only medical concept annotations with F1 > 80% and more than 10 annotated samples are shown.
Precision (P), Recall (R) and F1 (harmonic mean of precision and recall). Only medical concept annotations with F1 > 80% and more than 10 annotated samples are shown.
Odds ratios and p-values calculated from logistic regressions applying Firth’s correction. ACEi = Angiotensin converting enzyme inhibitor. OR = Odds ratio.