Clinical Kidney Journal, 2022 , vol. 15, no. 1, 79 –9 4 LEADING EUROPEAN NEPHROLOGY doi: 10.1093/ckj/sfab161 Advance Access Publication Date: 3 September 2021 Original Article Clinical Kidney Journal ORIGINAL ARTICLE Association of renin–angiotensin system blockers with COVID-19 diagnosis and prognosis in patients with hypertension: a population-based study Marı́a José Soler 1,*, Aida Ribera2,3,*, Josep R. Marsal2,3, Ana Belen Mendez4, Mireia Andres4,5, Maria Antonia Azancot1, Gerard Oristrell4, Leonardo Méndez-Boo6, Jordana Cohen7, Jose A. Barrabés4,5 and Ignacio Ferreira-González3,4, Vall d’Hebron COVID-19 Working Group 1 Department of Nephrology, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, Nephrology Research Group, Vall d’Hebron Research Institute (VHIR), Barcelona, Spain, 2Department of Cardiology, Cardiovascular Epidemiology Unit, Vall d’Hebron University Hospital Vall d’Hebron Research Institute (VHIR), Barcelona, Spain, 3Centro de Investigación Biomédica en Red de Epidemiologı́a y Salud Pública (CIBERESP), Madrid, Spain, 4Department of Cardiology, Vall d’Hebron University Hospital, Vall d’Hebron Research Institute (VHIR), Barcelona, Spain, 5Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain, 6Departament de Salut, SISAP: Sistema d0 Informació dels Serveis d0 Atenció Primària, Direcció de Sistemes d0 Informació, Institut Català de la Salut, Generalitat de Catalunya, Barcelona, Spain and 7Division of Renal-Electrolyte and Hypertension, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, USA Correspondence to: Aida Ribera; E-mail: aida.ribera@vhir.org and Marı́a José Soler; E-mail: mjsoler01@gmail.com *These authors share first authorship. ABSTRACT Background. The effect of renin–angiotensin system (RAS) blockade either by angiotensin-converting enzyme inhibitors (ACEis) or angiotensin-receptor blockers (ARBs) on coronavirus disease 2019 (COVID-19) susceptibility, mortality and severity is inadequately described. We examined the association between RAS blockade and COVID-19 diagnosis and prognosis in a large population-based cohort of patients with hypertension (HTN). Methods. This is a cohort study using regional health records. We identified all individuals aged 18–95 years from 87 healthcare reference areas of the main health provider in Catalonia (Spain), with a history of HTN from primary care records. Data were linked to COVID-19 test results, hospital, pharmacy and mortality records from 1 March 2020 to 14 August 2020. We defined exposure to RAS blockers as the dispensation of ACEi/ARBs during the 3 months before COVID-19 diagnosis or 1 March 2020. Primary outcomes were: COVID-19 infection and severe progression in hospitalized patients with Received: 10.5.2021; Editorial decision: 27.7.2021 C The Author(s) 2021. Published by Oxford University Press on behalf of ERA. V This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 79 80 | M.J. Soler et al. COVID-19 (the composite of need for invasive respiratory support or death). For both outcomes and for each exposure of interest (RAS blockade, ACEi or ARB) we estimated associations in age-, sex-, healthcare area- and propensity score- matched samples. Results. From a cohort of 1 365 215 inhabitants we identified 305 972 patients with HTN history. Recent use of ACEi/ARBs in patients with HTN was associated with a lower 6-month cumulative incidence of COVID-19 diagnosis f3.78% [95% confidence interval (CI) 3.69–3.86%] versus 4.53% (95% CI 4.40–4.65%); P < 0.001g. In the 12 344 patients with COVID-19 infection, the use of ACEi/ARBs was not associated with a higher risk of hospitalization with need for invasive respiratory support or death [OR ¼ 0.91 (0.71–1.15); P ¼ 0.426]. Conclusions. RAS blockade in patients with HTN is not associated with higher risk of COVID-19 infection or with a worse progression of the disease. Keywords: angiotensin-converting enzyme, angiotensin receptor blockers, COVID-19, hypertension, mortality, renin–angiotensin system blockers INTRODUCTION Most studies that have evaluated the association between RAS blockade and the risk of development and severity of There has been a growing interest and speculation regarding the COVID-19 suggest a neutral effect of this therapy [14–16]. Yet, to effect of the widely used classes of drugs that inhibit the renin–an- assess the association between RAS blockade and the risk of di- giotensin system (RAS) on the novel coronavirus [severe acute re- agnosis of COVID-19, the majority of published studies have a spiratory syndrome coronavirus 2 (SARS-CoV-2)] infection or used case–control design [14–16]. This design could bias to some coronavirus disease 2019 (COVID-19) severity from early 2020 [1]. Angiotensin-converting enzyme (ACE) 2 is a homolog of ACE, with extent risk estimates, especially if COVID-19 cases requiring 40% identity, which was discovered in 2000 [2–4]. Due to its impor- hospitalization are overrepresented [11, 17]. Furthermore, to as- tant role as the receptor of SARS-CoV-2, recent studies have been sess whether ACEi or ARBs are associated with worse outcomes focused on the potential implication of ACE2 in COVID-19 [5–8]. in COVID-19-infected patients, most of the published studies in- Prior data suggested that RAS blockade with ACE inhibitors (ACEis) cluded exclusively hospitalized patients, which constitutes a or angiotensin II receptor blockers (ARBs) may increase ACE2 ex- highly selected population, especially during the massive blow pression or activity, though these data are mixed. Accordingly, dur- of pandemic [18–21]. This is especially true for subpopulations ing the last several months, various observational studies and with high cardiovascular risk profiles. clinical trials have been designed and conducted to address the ef- To overcome potential selection bias in this context, the ideal fect of RAS blockade on COVID-19 severity [9–13]. approach would be to use a closed community population-based Renin–angiotensin system blockers and COVID-19 | 81 cohort. In this sense, although there are several cohort studies invasive organ/respiratory support or COVID-19 mortality (see that have been carried out in different populations, all of them the combinations of ICD-10 CM procedure codes for the need have selected the final study population in ways that could limit for invasive respiratory support). the generalizability of their findings [12, 16, 20]. In this study, we used a large, unselected community cohort of 305 972 patients Baseline characteristics and comorbidities with a history of hypertension (HTN) to assess whether chronic treatment with RAS inhibitors (RASis) is associated with an in- We assessed baseline characteristics and comorbidities on 1 creased risk of COVID-19 diagnosis or with worse outcomes in March 2020, including: sex, age (in years), rurality (rural, ur- infected patients. ban), socio-economic status [MEDEA (‘Mortalidad en áreas pequen ~ as Espan ~ olas y Desigualdades Socioeconómicas y Ambientales’) deprivation index] and pre-existing comorbid- MATERIALS AND METHODS ities. Rural areas were defined as areas with <10 000 inhabi- tants and a population density <150 inhabitants/km2. The Study design and data sources validated MEDEA deprivation index is calculated by census We performed a cohort study using prospectively collected data tract level in urban areas, categorized in quartiles, where the stored in the Information Systems of the Institut Català de la first and fourth quartiles are the least and most deprived areas, Salut (ICS) in Catalonia, Spain. ICS is the main health provider respectively [24]. Comorbidities were defined as the presence of the Catalan health system, providing primary care to a repre- of a diagnosis code recorded any time before the index date sentative >6.7 million people (>88% of the population in and still active on 1 March for a pre-specified list of conditions. Catalonia), and hospital care to 1.5 million. Lists of ICD-10-CM codes for each of these conditions are pro- The cohort data were obtained from primary-care elec- vided in Supplementary data, Table S2. tronic health records (ECAP). ECAP contains high-quality vali- dated diagnoses [International Classification for Diseases, 10th Drug exposure revision, Clinical Modification (ICD-10-CM)], pharmacy dis- We identified major classes of antihypertensive agents penses and socio-demographic information. ECAP data have (ACEis, ARBs, calcium-channel blockers, diuretics and been previously validated [22] and used for COVID-19 research b-blockers) that were dispensed to patients in the 3 months [23]. Registries of people with a diagnostic code related to HTN prior to the date of COVID infection, or 1 March 2020 in the were linked to the official repository of reverse-transcriptase non-infected [see the list of ATC (Anatomical Therapeutic polymerase chain reaction (RT-PCR) tests for SARS-CoV-2, to Chemical Classification System) codes of included pharmacy hospital admissions, intensive care units (ICUs) and mortality products in Supplementary data, Table S3]. We also identified registries. other drugs including lipid-lowering agents, oral antidiabetic The Vall d’Hebron Ethics Committee approved the study agents, insulin, antiplatelet agents, antiarrhythmic agents, protocol, including a waiver for the informed consent of anticoagulant agents, digitalis, nitrates, inhaled glucocorti- patients taking part in the study, and the data extracted were coids, non-steroidal anti-inflammatory drugs (NSAIDs), im- fully anonymized. The data underlying this article were pro- munosuppressive agents, short-acting b-agonists, long-acting vided by ICS by permission. Data will be shared on request to b-agonists and other agents used for chronic respiratory dis- the corresponding author with permission of ICS. eases. The exposure of interest was defined as the dispensa- tion of ACEis or ARBs at least once during the 3 months before Study participants and follow-up COVID-19 diagnosis or before the start of the first pandemic wave (1 March 2020). Patients with dispensation of both We included all individuals in all healthcare areas with primary agents represented only 0.4% (n ¼ 1532) of the cohort and were and hospital care provided by ICS, aged 18 and <95 years with excluded. a diagnosis of HTN up to 1 March 2020. For the first main study objective, the association of RASis use with risk of COVID-19 diagnosis, participants were followed Statistical analysis from 1 March 2020 to the earliest of a first positive RT-PCR test We report descriptive statistics including means and stan- or clinical diagnosis or death or the end of the study period (14 dard deviations (SDs) for continuous variables, and frequen- August 2020). COVID-19 was identified by a positive RT-PCR test cies and percentages for binary and categorical variables. for SARS-CoV-2 (confirmed cases) and/or a clinical diagnosis Differences in means were assessed using the Student’s t-test (probable cases) recorded in primary care or hospital diagnoses, and differences in percentages were assessed using the or in death certificates. ICD-10-CM codes used are listed in chi-square test. Supplementary data, Table S1. The association between use of RASis and risk of COVID-19 For the second main study objective, the association of diagnosis was assessed using mixed effects Cox regression RASis use with risk of worse outcomes among infected analysis, and the association between the use of RASis and out- patients, patients with a diagnosis of COVID-19 were followed comes in infected patients was assessed using mixed effects lo- from the earliest COVID-19 diagnosis (confirmed or probable) gistic models. We used random slope models and a non- until death or the end of the study period (14 August 2020). structured covariance matrix for random effects. In both analy- The merged data allowed us to assess different key events ses, we adjusted for the effect of clustered structure of data throughout the progression of COVID-19: hospitalization, ad- among patients cared for by different health providers located mission to the ICU, need for respiratory support, need for inva- in different geographic locations (i.e. healthcare areas; n ¼ 89) sive haemodynamic support and COVID-19 mortality. Our that might differ in socio-economic status and in the level of ex- primary endpoint for this second objective was need for posure to the virus. We also adjusted all analyses by the 82 | M.J. Soler et al. estimated prevalence of COVID-19 at each healthcare area as an characteristics between exposure groups in matched data was indicator of COVID-19 impact in the area or a surrogate marker assessed using standardized differences (Supplementary data, of the probability of each individual living in each area to be ex- Tables S4–S6). Outcomes between matched cohorts were com- posed to the virus. pared with conditional logistic regression models. We per- To minimize confounding, all comparisons performed were formed further analyses within the matched cohorts to adjust matched by age, sex, healthcare area and the propensity score for those variables with standardized differences > 10%. All for the corresponding exposure. Propensity scores were esti- analyses were performed using the statistical package RStudio. mated using logistic regression and they included the variables age, gender, MEDEA deprivation index, diabetes, cardiovascular disease (CVD) history (i.e. myocardial infarction, angina, stroke, RESULTS heart failure and peripheral vascular disease), asthma/chronic From a reference population of 1 365 215 inhabitants, 18 years obstructive pulmonary disease (COPD) history, dementia, old, living in areas with both primary and hospital care deliv- hypercholesterolaemia, atrial fibrillation, cancer, renal impair- ered by ICS centres (a total of 87 health areas), we identified ment, obesity [diagnosis or body mass index (BMI) >30 kg/m2] 305 972 patients with HTN (Figure 1). Among these, 201 131 and the dispensed medications. Each patient with the exposure (66%) were dispensed RAAS inhibitor (RAASi) therapy during the of interest was 1:1 matched without replacement to the non- 3 months prior to the initial pandemic wave: 127 532 (41.6%) treated patient of the same age, sex and healthcare area with were on an ACEi and 73 599 (24%) were on an ARB. The cumula- the lowest propensity score difference. Balance in patient tive incidence of COVID-19 infection during the first 6 months of HTN patients N = 310 164 Excluded: < 18 y, > 95 y: n = 2723 ARB+ACEI: n = 1469 HTN patients N = 305 972 34.2% / 41.7% / 24.1% 1904 / 0.6% No COVID-19 Clinical diagnostic Positive SARS-CoV-19 test N = 293 628 N = 5889 N = 6455 34.1% / 41.8% / 24.1% 37.4% / 39.2% / 23.4% 39.5% / 37.5% / 23.0% 1193 / 0.4% 141 / 2.4% 570 / 8.8% COVID-19 confirmed or probable N = 12 344 38.5% / 38.3% / 23.2% 711 / 5.8% Non-hospitalized Hospitalized N = 9409 N = 2935 40.3% / 37.6% /22.1% 32.8% / 40.5% / 26.7% 341 / 3.6% 370 / 12.6% Non-severe Severe N = 2601 N = 334 N = number of patients 33.3% / 40.2% / 26.5% 27.6% / 43.4% / 29.0% % RASi / % ACEi / % ARBs Number of deaths / % deaths 294 / 11.3% 76 /22.8% FIGURE 1: Flow chart. The values in boxes represent: percentage of patients on RASis/percentage of patients on ACEi/percentage of patients on ARBs/number of deaths/percentage of patients dying. Renin–angiotensin system blockers and COVID-19 | 83 Table 1. Baseline patient characteristics by RASi, ACEi or ARB use RASi use Yes P-value P-value (ACEi (RAS Global No All ACEi users ARB users versus versus no (n ¼ 305 972) (n ¼ 104 841) (n ¼ 201 131) (n ¼ 127 532) (n ¼ 73 599) ARB) RAS) Age, years, mean 6 SD 68.86 6 13.41 67.1 6 15.01 69.77 6 12.4 68.8 6 12.68 71.46 6 11.71 <0.001 <0.001 Female, n (%) 157 070 (51.3) 56 093 (53.5) 100 977 (50.2) 61 086 (47.9) 39 891 (54.2) <0.001 <0.001 MEDEA deprivation index, n (%) <0.001 <0.001 NA 908 (0.3) 423 (0.4) 485 (0.2) 274 (0.2) 211 (0.3) – – 0 Rural 16 245 (5.3) 5596 (5.3) 10 649 (5.3) 6826 (5.4) 3823 (5.2) – – 1 Rural 28 718 (9.4) 9749 (9.3) 18 969 (9.4) 12 481 (9.8) 6488 (8.8) – – 2 Rural 45 262 (14.8) 15 961 (15.2) 29 301 (14.6) 17 435 (13.7) 11 866 (16.1) – – 1 Urban 41 266 (13.5) 14 295 (13.6) 26 971 (13.4) 17 269 (13.5) 9702 (13.2) – – 2 Urban 43 619 (14.3) 15 157 (14.5) 28 462 (14.2) 17 628 (13.8) 10 834 (14.7) – – 3 Urban 77 321 (25.3) 26 400 (25.2) 50 921 (25.3) 32 047 (25.1) 18 874 (25.6) – – 4 Urban 52 633 (17.2) 17 260 (16.5) 35 373 (17.6) 23 572 (18.5) 11 801 (16) – – Smoking habit, n (%) <0.001 <0.001 No 173 103 (56.6) 62 345 (59.5) 110 758 (55.1) 67 279 (52.8) 43 479 (59.1) – – Yes 43 139 (14.1) 15 915 (15.2) 27 224 (13.5) 19 644 (15.4) 7580 (10.3) – – Former 89 730 (29.3) 26 581 (25.4) 63 149 (31.4) 40 609 (31.8) 22 540 (30.6) – – Obesity, n (%) 145 438 (47.5) 43 744 (41.7) 101 694 (50.6) 62 439 (49) 39 255 (53.3) <0.001 <0.001 Hypercholesterolaemia, n (%) 85 156 (27.8) 26 953 (25.7) 58 203 (28.9) 36 773 (28.8) 21 430 (29.1) 0.179 <0.001 Diabetes, n (%) 86 157 (28.2) 22 914 (21.9) 63 243 (31.4) 38 187 (29.9) 25 056 (34) <0.001 <0.001 Atrial fibrillation, n (%) 28 792 (9.4) 9117 (8.7) 19 675 (9.8) 10 716 (8.4) 8959 (12.2) <0.001 <0.001 Ischaemic heart disease, n (%) 57 601 (18.8) 17 109 (16.3) 40 492 (20.1) 23 639 (18.5) 16 853 (22.9) <0.001 <0.001 Myocardial infarction, n (%) 9794 (3.2) 2785 (2.7) 7009 (3.5) 4362 (3.4) 2647 (3.6) 0.039 <0.001 Angina, n (%) 9102 (3) 2857 (2.7) 6245 (3.1) 3442 (2.7) 2803 (3.8) <0.001 <0.001 Stroke, n (%) 16 370 (5.4) 4968 (4.7) 11 402 (5.7) 6961 (5.5) 4441 (6) <0.001 <0.001 Heart failure, n (%) 16 126 (5.3) 5162 (4.9) 10 964 (5.5) 5755 (4.5) 5209 (7.1) <0.001 <0.001 Peripheral vascular disease, n (%) 11 483 (3.8) 3326 (3.2) 8157 (4.1) 4889 (3.8) 3268 (4.4) <0.001 <0.001 Chronic obstructive pulmonary 86 370 (28.2) 27 332 (26.1) 59 038 (29.4) 34 999 (27.4) 24 039 (32.7) <0.001 <0.001 disease, n (%) Dementia, n (%) 8555 (2.8) 3729 (3.6) 4826 (2.4) 3053 (2.4) 1773 (2.4) 0.843 <0.001 Cancer, n (%) 3117 (1) 1116 (1.1) 2001 (1) 1186 (0.9) 815 (1.1) <0.001 0.072 Chronic kidney disease, n (%) 46 867 (15.3) 14 724 (14) 32 143 (16) 17 760 (13.9) 14 383 (19.5) <0.001 <0.001 Recent concomitant drug therapy Number of concomitant agents, mean 1.95 6 1.87 1.55 6 1.8 2.16 6 1.87 2 6 1.79 2.45 6 1.97 <0.001 <0.001 6 SD 0–1, n (%) 150 551 (49.2) 62 431 (59.5) 88 120 (43.8) 60 361 (47.3) 27 759 (37.7) – – 2–4, n (%) 123 495 (40.4) 34 283 (32.7) 89 212 (44.4) 54 606 (42.8) 34 606 (47) – – >4, n (%) 31 926 (10.4) 8127 (7.8) 23 799 (11.8) 12 565 (9.9) 11 234 (15.3) – – Calcium channel blockers, n (%) 60 984 (19.9) 17 124 (16.3) 43 860 (21.8) 24 744 (19.4) 19 116 (26) <0.001 <0.001 Thiazides, n (%) 30 711 (10) 13 176 (12.6) 17 535 (8.7) 11 701 (9.2) 5834 (7.9) <0.001 <0.001 Loop diuretics, n (%) 29 116 (9.5) 9083 (8.7) 20 033 (10) 10 631 (8.3) 9402 (12.8) <0.001 <0.001 b-blockers, n (%) 62 900 (20.6) 20 452 (19.5) 42 448 (21.1) 23 687 (18.6) 18 761 (25.5) <0.001 <0.001 Lipid-lowering agents, n (%) 112 910 (36.9) 25 649 (24.5) 87 261 (43.4) 52 691 (41.3) 34 570 (47) <0.001 <0.001 Antiplatelet agents, n (%) 57 127 (18.7) 13 056 (12.5) 44 071 (21.9) 26 028 (20.4) 18 043 (24.5) <0.001 <0.001 Vitamin K antagonists, n (%) 15 381 (5) 4098 (3.9) 11 283 (5.6) 6383 (5) 4900 (6.7) <0.001 <0.001 Direct oral anticoagulants, n (%) 11 278 (3.7) 3038 (2.9) 8240 (4.1) 4325 (3.4) 3915 (5.3) <0.001 <0.001 Digital, n (%) 2895 (0.9) 822 (0.8) 2073 (1) 1155 (0.9) 918 (1.2) <0.001 <0.001 Nitrates, n (%) 8713 (2.8) 2649 (2.5) 6064 (3) 3297 (2.6) 2767 (3.8) <0.001 <0.001 Insulin, n (%) 18 528 (6.1) 4085 (3.9) 14 443 (7.2) 7954 (6.2) 6489 (8.8) <0.001 <0.001 Oral antidiabetic agents, n (%) 63 803 (20.9) 12 872 (12.3) 50 931 (25.3) 30 789 (24.1) 20 142 (27.4) <0.001 <0.001 Immunosuppressive agents, n (%) 18 278 (6) 5826 (5.6) 12 452 (6.2) 7202 (5.6) 5250 (7.1) <0.001 <0.001 NSAIDs, n (%) 32 521 (10.6) 10 548 (10.1) 21 973 (10.9) 14 459 (11.3) 7514 (10.2) <0.001 <0.001 Long-action b-adrenergic agents, n (%) 24 126 (7.9) 6545 (6.2) 17 581 (8.7) 9960 (7.8) 7621 (10.4) <0.001 <0.001 Short-action b-adrenergic agents, n (%) 12 078 (3.9) 3438 (3.3) 8640 (4.3) 5278 (4.1) 3362 (4.6) <0.001 <0.001 Steroid agents, n (%) 7088 (2.3) 2115 (2) 4973 (2.5) 2852 (2.2) 2121 (2.9) <0.001 <0.001 COVID-19 infection, n (%) 12 344 (4) 4746 (4.5) 7598 (3.8) 4731 (3.7) 2867 (3.9) 0.036 <0.001 Impact of COVID-19 infection in the corresponding healthcare areas (n ¼ 87) 84 | M.J. Soler et al. Table 1. (continued) RASi use Yes P-value P-value (ACEi (RAS Global No All ACEi users ARB users versus versus no (n ¼ 305 972) (n ¼ 104 841) (n ¼ 201 131) (n ¼ 127 532) (n ¼ 73 599) ARB) RAS) Mean prevalence in the corresponding 4.19 6 1.77 4.19 6 1.79 4.19 6 1.76 4.21 6 1.78 4.14 6 1.73 <0.001 0.859 census area, mean 6 SD Terciles of prevalence, n (%) <3.3 101 633 (33.2) 35 153 (33.5) 66 480 (33.1) 41 233 (32.3) 25 247 (34.3) – – 3.3–4.7 102 272 (33.4) 34 251 (32.7) 68 021 (33.8) 43 281 (33.9) 24 740 (33.6) – – >4.7 102 067 (33.4) 35 437 (33.8) 66 630 (33.1) 43 018 (33.7) 23 612 (32.1) – – NA, not available. FIGURE 2: Association of recent use of RAASi (A), ACEi (B) or ARB (C) users versus non-users with the risk of COVID-19 infection, stratified by different variables. Estimates are presented for the age-, sex- and propensity score-matched samples. N. Treatments, number of concomitant pharmacological treatments; COVID-19 im- pact, estimated prevalence of COVID-19 at the corresponding healthcare area. Renin–angiotensin system blockers and COVID-19 | 85 FIGURE 2: (continued) the pandemic in this population was 4.03% [95% confidence in- RASis users were prescribed more other drugs on average terval (CI) 3.96–4.10]. (mean total number of other drugs 2.16 6 1.87 versus 1.55 6 1.8, P < 0.001), except for NSAIDs. Patients in both groups lived in RASis therapy and the risk of COVID-19 diagnosis healthcare areas with a fairly similar COVID-19 impact , al- though with a trend of higher cumulative incidence in the group Table 1 shows the baseline differences between RASi users ver- of patients on RASis. Differences were smaller when comparing sus non-users and, within the RASis group, between ACEi and ACEi with ARB users, although the latter tended to have a ARB users. Compared with non-users, RASi users were older, higher prevalence of baseline risk factors, comorbidities and mostly male, with higher deprivation indexes, higher preva- lence of obesity, HTN and hypercholesterolaemia, and lower concomitant therapies. The cumulative incidence of COVID-19 prevalence of diabetes. Other comorbidities were in general also diagnosis at 6 months (Table 2) was lower in RASi users com- more common in patients on RASis, although stroke history pared with non-users [3.8% (95% CI 3.7–3.8%) versus 4.53% (95% and dementia were less frequent in these patients. Similarly, CI 4.4–4.7%); P < 0.001]. Within the RASis group, cumulative 86 | M.J. Soler et al. FIGURE 2: (continued) incidence was slightly higher in patients on ARBs [3.9% (95% CI and S6). In these matched cohorts, cumulative incidence of 3.8–4.0%)] than in those on ACEis [3.7% (95% CI 3.6–3.8%); COVID-19 diagnosis (Table 2) was also lower in those patients P ¼ 0.036]. on ACEis and in those patients on ARBs comparing their respec- Baseline covariates between RASi users versus non-users tive non-user pairs: 1.7% (95% CI 1.6–1.8%) versus 1.8% (95% CI were well-balanced in 93 662 pairs of age-, sex-, area- and pro- 1.7–1.9%) (P  0.011) in the ACEi cohort and 2.2% (95% CI 2.0–2.4%) pensity score-matched patients (Supplementary data, Table S4). versus 2.6% (95% CI 2.4–2.7%) (P ¼ 0.023) in the ARBs cohort. In these matched cohorts, cumulative incidence of COVID-19 di- Figure 2 shows the subgroup analyses for each therapy stratified agnosis (Table 2) was lower in RASi users versus non-users by age, sex, diabetes, HTN, CVD, obesity, number of concomitant [3.9% (95% CI 3.8–4.0%) versus 4.4% (95% CI 4.3–4.5%); P ¼ 0.016]. treatments and the COVID-19 impact in the corresponding health- In the matched pairs of ACEi users versus non-users (n ¼ 60 964 care areas. Association of RASis with a lower cumulative incidence pairs) and ARB users versus non-users (n ¼ 32 698 pairs), covari- of COVID-19 diagnosis was consistent across the different ates were also well balanced (Supplementary data, Tables S5 subgroups. Renin–angiotensin system blockers and COVID-19 | 87 Table 2. Cumulative incidence of COVID-19 diagnosis and inci- for the risk of severe COVID-19 disease showed consistent dence of main outcomes in the COVID-19 infected, in the whole results across the different subgroups (Figure 3). study sample and in the age-, sex-, area- and propensity score- matched samples DISCUSSION All sample % Matched sample % In this large population-based, unselected cohort of 305 972 incidence incidence patients with HTN, recent use of ACEis or ARBs was associated (95% CI) (95% CI) with a lower 6-month cumulative incidence of COVID-19 diag- Incidence of COVID-19 infection nosis. In addition, in the 12 344 patients with COVID-19 infec- Non-users 4.5 (4.4–4.6) 2.1 (2.0–2.2) tion, the use of ACEis or ARBs was not associated with a higher ACEi users 3.7 (3.6–3.8) 1.7 (1.6–1.8) risk of hospitalization, need for invasive respiratory support or ARB users 3.9 (3.8–4.0) 2.2 (2.0–2.4) death. COVID-19-infected patients SARS-CoV-2 and other coronaviruses can infect host cells by Hospitalization means of interaction with membrane-bound ACE2 on alveolar Non-users 20.2 (19.1–21.3) 20.5 (18.7–22.3) epithelial cells [25]. ACE2 is an enzyme part of the RAS that ACEi users 25.2 (22.7–27.7) 21.9 (19.6–24.2) shares around 40% homology with the classical ACE [2, 3]. Prior ARB users 27.4 (24.3–20.5) 23.8 (20.7–26.9) experimental and clinical studies demonstrated that treatment Severe episode with RASis may increase ACE2 in different compartments in the Non-users 1.9 (1.5–2.3) 1.9 (1.3–2.5) ACEi users 3.1 (2.6–3.6) 2.0 (1.2–2.8) body such as circulating ACE2 and ACE2 expression in the heart ARB users 3.4 (2.7–4.1) 3.0 (1.8–4.2) and renal vasculature [26–28]. For this reason, Fang et al. [29] Death suggested that treatment with ACEis or ARBs could increase the Non-users 7.2 (6.5–7.9) 6.5 (5.4–7.6) risk and severity of COVID-19 after exposure to SARS-CoV-2. ACEi users 4.4 (3.8–5.0) 4.4 (3.3–5.5) This speculation motivated researchers to investigate this hy- ARB users 5.5 (4.7–6.3) 7.0 (5.1–8.9) pothesis, while medical societies urged against withdrawal of RAS blockade in patients with or at risk of cardiac or renal dis- ease in the COVID-19 pandemic era [30]. The primary function of ACE2 is to counterbalance the actions of ACE. Thus, whereas ACE degrades angiotensin I to an- RAASis and prognosis in patients infected with giotensin II, ACE2 degrades angiotensin II into angiotensin-(1– COVID-19 7), exerting counterregulation of the RAS [31]. Circulating ACE2 Table 3 shows the baseline differences in the 12 344 COVID-19- activity is increased in patients with increased cardiovascular infected patients between RASi users (n ¼ 7598) and non-users risk characteristics such as male sex, heart failure, myocardial (n ¼ 4746) and, within the RASi group, between ACEi (n ¼ 4731) infarction and diabetes [28, 32–34]. Some authors postulate that and ARB (n ¼ 2867) users. In general, differences observed in the this increase in circulating ACE2 may be in part due to a com- whole study population were also reproducible in infected pensatory mechanism, where in conditions with high cardio- patients, although without statistically significant differences vascular risk the excess of angiotensin II accumulation may in angina, cancer and concomitant treatment with digitalis, lead to a circulating rise of ACE2 to degrade angiotensin II to an- loop diuretics, vitamin K, nitrates and steroids. As in the whole giotensin-(1–7) [28, 35]. population group, differences between ACEi and ARBs users The potential harm related to RAS blockade in patients with were smaller. Table 2 shows the cumulative incidence of main COVID-19 infection has now been widely studied. In the last outcomes according to RASis therapy. Hospitalization rates, 6 months, 15 observational studies have been focused in the need for intensive care and the need for invasive respiratory effect of prior RAS blockade on the severity of COVID-19 [36, 37]. support were higher in RASis users versus non-users. In con- Consistently, no association between ACEi or ARB use and ill- trast, the death rate was lower in RASi users [4.8% (95% CI 4.4– ness severity has been found, in studies performed across sev- 5.3%) versus 7.2% (95% CI 6.5–8.0%); P < 0.001]. The same pattern eral continents [36]. However, most of these studies were was observed in the composite endpoint of death or need for in- performed exclusively in hospitalized patients, who are not vasive respiratory support in RASi users [7.3% (95% CI 6.7–7.90)] necessarily representative of the whole spectrum of COVID-19 versus in non-users [8.7% (95% CI 7.9–9.5%); P ¼ 0.006]. Within patients, or used case–control designs, which are prone to sev- the RASi group, higher rate of hospitalization, death and in the eral sources of bias [12, 14–16, 21, 38]. Among the latter, the composite endpoint death or need for invasive respiratory sup- most notable studies were performed by Mancia et al., de Abajo port was observed in patients on ARBs compared with patients et al. and Fosbøl et al. [14–16]. Mancia et al. conducted a case– on ACEis. control study involving patients with confirmed COVID-19 in Table 4 shows the crude and adjusted effects of RAS (ACEi the Lombardy region of Italy. In this study, 6272 people with and ARB) use on main outcomes. In the age-, sex-, area- and confirmed SARS-CoV-2 infection were compared with 30 759 propensity score-matched analyses, baseline differences be- controls matched by age, sex and region. In a logistic-regression tween patients on RASis (ACEi users n ¼ 1295, ARB users n ¼ 728, multivariate analysis, neither ACEis nor ARBs were associated any RASi n ¼ 2023) were well balanced (Supplementary data, with an increased risk of SARS-CoV-2 infection or severe Tables S7 and S8). In the matched cohorts, there were no differ- COVID-19 [14]. Similarly, de Abajo et al. collected data from ences between RASi users and non-users regarding hospitaliza- 11 139 hospitalized patients and 11 390 age- and sex-matched tion, death, need for invasive respiratory support and the population controls from the Madrid region, and estimated that composite of death or need for invasive respiratory support users of RASis had an adjusted OR for COVID-19 requiring ad- (Table 4). Interestingly, the death rate was lower in ACEi users mission to hospital of 0.94 (95% CI 0.77–1.15) [15]. Finally, in the as compared with non-ACEi users [4.40% (95% CI 3.28–5.52%) nested case–control aspect of Fosbøl et al.’s study, the use of versus 6.25% (95% CI 4.94–7.57%); P ¼ 0.044]. Subgroup analyses ACEis or ARBs compared with other antihypertensive 88 | M.J. Soler et al. Table 3. Baseline characteristics of patients with COVID-19 diagnosis by RASi, ACEi or ARB use RASi use Yes P-value P-value (RAS Global ACEi ARB (ACEi versus no (n ¼ 12 344) No (n ¼ 4746) All (n ¼ 7598) (n ¼ 4731) (n ¼ 2867) versus ARB) RAS) Age, years, mean 6 SD 68.22 6 15.31 67.83 6 16.95 68.47 6 14.19 67.42 6 14.67 70.21 6 13.16 <0.001 0.028 Female, n (%) 6829 (55.3) 2786 (58.7) 4043 (53.2) 2439 (51.6) 1604 (55.9) <0.001 <0.001 MEDEA deprivation index, n (%) 0.026 0.108 NA 47 (0.4) 16 (0.3) 31 (0.4) 20 (0.4) 11 (0.4) – – 0 Rural 513 (4.2) 210 (4.4) 303 (4) 174 (3.7) 129 (4.5) – – 1 Rural 1181 (9.6) 432 (9.1) 749 (9.9) 496 (10.5) 253 (8.8) – – 2 Rural 1600 (13) 625 (13.2) 975 (12.8) 613 (13) 362 (12.6) – – 1 Urban 1097 (8.9) 451 (9.5) 646 (8.5) 403 (8.5) 243 (8.5) – – 2 Urban 2184 (17.7) 857 (18.1) 1327 (17.5) 823 (17.4) 504 (17.6) – – 3 Urban 3421 (27.7) 1318 (27.8) 2103 (27.7) 1260 (26.6) 843 (29.4) – – 4 Urban 2301 (18.6) 837 (17.6) 1464 (19.3) 942 (19.9) 522 (18.2) – – Smoking habit, n (%) <0.001 <0.001 No 7184 (58.2) 2908 (61.3) 4276 (56.3) 2601 (55) 1675 (58.4) – – Yes 1492 (12.1) 595 (12.5) 897 (11.8) 605 (12.8) 292 (10.2) – – Former 3668 (29.7) 1243 (26.2) 2425 (31.9) 1525 (32.2) 900 (31.4) – – Obesity, n (%) 5949 (48.2) 2081 (43.8) 3868 (50.9) 2334 (49.3) 1534 (53.5) <0.001 <0.001 Hypercholesterolaemia, n (%) 3490 (28.3) 1265 (26.7) 2225 (29.3) 1354 (28.6) 871 (30.4) 0.108 0.002 Diabetes, n (%) 3498 (28.3) 1147 (24.2) 2351 (30.9) 1400 (29.6) 951 (33.2) 0.001 <0.001 Atrial fibrillation, n (%) 1365 (11.1) 550 (11.6) 815 (10.7) 454 (9.6) 361 (12.6) <0.001 0.145 Ischaemic heart disease, n (%) 2702 (21.9) 977 (20.6) 1725 (22.7) 994 (21) 731 (25.5) <0.001 0.006 Myocardial infarction, n (%) 435 (3.5) 144 (3) 291 (3.8) 172 (3.6) 119 (4.2) 0.284 0.022 Angina, n (%) 411 (3.3) 141 (3) 270 (3.6) 146 (3.1) 124 (4.3) 0.006 0.088 Stroke, n (%) 932 (7.6) 356 (7.5) 576 (7.6) 347 (7.3) 229 (8) 0.319 0.898 Heart failure, n (%) 879 (7.1) 359 (7.6) 520 (6.8) 266 (5.6) 254 (8.9) <0.001 0.139 Peripheral vascular disease, n (%) 488 (4) 165 (3.5) 323 (4.3) 192 (4.1) 131 (4.6) 0.312 0.036 Chronic obstructive pulmonary 4132 (33.5) 1491 (31.4) 2641 (34.8) 1575 (33.3) 1066 (37.2) <0.001 <0.001 disease, n (%) Dementia, n (%) 841 (6.8) 424 (8.9) 417 (5.5) 274 (5.8) 143 (5) 0.15 <0.001 Cancer, n (%) 141 (1.1) 54 (1.1) 87 (1.1) 45 (1) 42 (1.5) 0.054 1 Chronic kidney disease, n (%) 2084 (16.9) 875 (18.4) 1209 (15.9) 645 (13.6) 564 (19.7) <0.001 <0.001 Concomitant drug therapy during prior 3 months Number of concomitant agents, mean 6 SD 2.12 6 1.99 1.76 6 1.94 2.34 6 1.98 2.18 6 1.91 2.61 6 2.08 <0.001 <0.001 0–1, n (%) 5767 (46.7) 2642 (55.7) 3125 (41.1) 2094 (44.3) 1031 (36) – – 2–4, n (%) 5006 (40.6) 1631 (34.4) 3375 (44.4) 2057 (43.5) 1318 (46) – – >4, n (%) 1571 (12.7) 473 (10) 1098 (14.5) 580 (12.3) 518 (18.1) – – Calcium channel blockers, n (%) 2482 (20.1) 739 (15.6) 1743 (22.9) 954 (20.2) 789 (27.5) <0.001 <0.001 Thiazides, n (%) 1229 (10) 560 (11.8) 669 (8.8) 436 (9.2) 233 (8.1) 0.114 <0.001 Loop diuretics, n (%) 1723 (14) 659 (13.9) 1064 (14) 560 (11.8) 504 (17.6) <0.001 0.875 b-blockers, n (%) 2487 (20.1) 907 (19.1) 1580 (20.8) 875 (18.5) 705 (24.6) <0.001 0.025 Lipid-lowering agents, n (%) 3901 (31.6) 974 (20.5) 2927 (38.5) 1716 (36.3) 1211 (42.2) <0.001 <0.001 Antiplatelet agents, n (%) 2451 (19.9) 715 (15.1) 1736 (22.8) 1012 (21.4) 724 (25.3) <0.001 <0.001 Vitamin K antagonists, n (%) 602 (4.9) 212 (4.5) 390 (5.1) 220 (4.7) 170 (5.9) 0.017 0.103 Direct oral anticoagulants, n (%) 599 (4.9) 215 (4.5) 384 (5.1) 195 (4.1) 189 (6.6) <0.001 0.202 Digital, n (%) 124 (1) 52 (1.1) 72 (0.9) 41 (0.9) 31 (1.1) 0.416 0.478 Nitrates, n (%) 428 (3.5) 147 (3.1) 281 (3.7) 146 (3.1) 135 (4.7) <0.001 0.085 Insulin, n (%) 862 (7) 257 (5.4) 605 (8) 338 (7.1) 267 (9.3) <0.001 <0.001 Oral antidiabetic agents, n (%) 2330 (18.9) 562 (11.8) 1768 (23.3) 1057 (22.3) 711 (24.8) 0.015 <0.001 Immunosuppressive agents, n (%) 1140 (9.2) 404 (8.5) 736 (9.7) 433 (9.2) 303 (10.6) 0.047 0.031 NSAIDs, n (%) 1331 (10.8) 440 (9.3) 891 (11.7) 613 (13) 278 (9.7) <0.001 <0.001 Long-action b-adrenergic agents, n (%) 1291 (10.5) 421 (8.9) 870 (11.5) 496 (10.5) 374 (13) <0.001 <0.001 Short-action b-adrenergic agents, n (%) 801 (6.5) 264 (5.6) 537 (7.1) 347 (7.3) 190 (6.6) 0.263 0.001 Steroid agents, n (%) 444 (3.6) 159 (3.4) 285 (3.8) 155 (3.3) 130 (4.5) 0.006 0.265 Impact of COVID-19 infection in the corresponding healthcare areas (n ¼ 87) Mean prevalence in the corresponding area, 4.94 6 1.82 4.97 6 1.8 4.93 6 1.84 4.98 6 1.87 4.84 6 1.78 0.001 0.267 mean 6 SD Renin–angiotensin system blockers and COVID-19 | 89 Table 3. (continued) RASi use Yes P-value P-value (RAS Global ACEi ARB (ACEi versus no (n ¼ 12 344) No (n ¼ 4746) All (n ¼ 7598) (n ¼ 4731) (n ¼ 2867) versus ARB) RAS) Terciles of prevalence, n (%) <3.3 2233 (18.1) 840 (17.7) 1393 (18.3) 833 (17.6) 560 (19.5) 0.008 0.051 3.3–4.7 4172 (33.8) 1557 (32.8) 2615 (34.4) 1600 (33.8) 1015 (35.4) – – >4.7 5939 (48.1) 2349 (49.5) 3590 (47.2) 2298 (48.6) 1292 (45.1) – – Table 4. Crude and adjusted effect of RAS, ACEi and ARB use on main outcomes in COVID-19-infected patients RAS users versus ACEi users versus ARB users versus non-users [HR (95% CI)] P-value non-users [HR (95% CI)] P-value non-users [HR (95% CI)] P-value Hospitalization Crude 1.39 (1.27–1.51) <0.001 1.33 (1.20–1.46) <0.001 1.49 (1.33–1.66) <0.001 Adjusted 1.22 (1.12–1.34) <0.001 1.22 (1.10–1.35) <0.001 1.20 (1.07–1.34) 0.002 Matched 1.14 (0.98–1.33) 0.097 1.18 (0.97–1.44) 0.100 1.08 (0.84–1.39) 0.559 Death Crude 0.65 (0.56–0.76) <0.001 0.60 (0.50–0.71) <0.001 0.75 (0.62–0.91) 0.004 Adjusted 0.69 (0.59–0.82) <0.001 0.66 (0.54–0.80) <0.001 0.75 (0.61–0.93) 0.008 Matched 0.79 (0.60–1.04) 0.095 0.67 (0.46–0.96) 0.030 1.00 (0.66–1.53) 1 Severe episode Crude 1.66 (1.31–2.13) <0.001 1.60 (1.23–2.09) <0.001 1.77 (1.33–2.37) <0.001 Adjusted 1.36 (1.07–1.75) 0.015 1.30 (0.99–1.71) 0.058 1.41 (1.05–1.91) 0.024 Matched 1.26 (0.83–1.93) 0.282 0.96 (0.56–1.65) 0.891 2 (0.97–4.12) 0.061 Death or severe episode Crude 0.83 (0.73–0.95) 0.005 0.77 (0.66–0.90) 0.001 0.93 (0.78–1.10) 0.383 Adjusted 0.80 (0.69–0.92) 0.001 0.77 (0.66–0.91) 0.002 0.85 (0.71–1.02) 0.077 Matched 0.91 (0.71–1.15) 0.426 0.76 (0.56–1.05) 0.095 1.16 (0.80–1.68) 0.446 HR, hazard ratio. medications, was not significantly associated with COVID-19 di- population was highly selected, since only 322 044 out of 732 410 agnosis in 494 170 patients with HTN (data from Danish national potential participants were ultimately included [20]. administrative registries) [16]. Our study performed in an unselected cohort of 305 972 There are several cohort studies that have been conducted patients with HTN has demonstrated, using a properly bal- in community population cohorts. Reynolds et al. conducted a anced 93 662 age-, sex-, area- and propensity score-matched study based on data from the electronic health records of 12 594 pairs, that prior treatment with RASis in this high cardiovas- patients in the New York University Langone Health system cular risk cohort is associated with a lower frequency of who were tested for COVID-19. A total of 5894 patients had a COVID-19 diagnosis. Whether this association is related to positive test, among whom 1002 had severe illness (defined as the effect of RAS blockade, or the treatment with RASis is admission to the ICU, mechanical ventilation or death). The merely a marker of other unknown factors should be eluci- authors observed no positive association of any of the analysed dated in a randomized clinical trial. In any case, this finding drug classes, including ACEis and ARBs, with either a positive is in concordance with a recently published meta-analysis SARS-CoV-2 test result or severe illness [12]. In the retrospective that included three studies with a total of 8766 patients with cohort aspect of Fosbøl et al.’s study 4480 patients who were ex- COVID-19, showing that ACEis or ARBs were not associated amined in a hospital and had a diagnosis code for COVID-19 with a higher likelihood of positive SARS-CoV-2 test results were included [16]. Finally, the recently published Dublin et al. in symptomatic patients [36]. [20] study addresses two objectives, namely the association be- In agreement with the majority of previous observational tween RASis and the risk of COVID-19 diagnosis and the risk of studies, in our large cohort in hypertensive patients from hospitalization in the infected patients. Neither of these studies Catalonia (Spain), we have been able to replicate the same observed significant associations between RASi use and COVID- results and demonstrated that there was no association be- 19 diagnosis or severity. tween ACEis or ARBs and negative outcomes such as hospitali- All of these studies, although well conducted and properly zation, total mortality, need for invasive organ/respiratory analysed, may have limited external validity because of the se- support and the composite of total mortality or need for inva- lection of the study population. For instance, although Dublin et sive organ/respiratory support in COVID-19-infected patients. al.’s study had a population-based setting, the final study Furthermore, the BRACE CORONA study, the first randomized 90 | M.J. Soler et al. FIGURE 3: Association of recent use of RASis (A), ACEis (B) or ARBs (C) versus non-users with the risk of severe COVID-19 disease, stratified by different variables. Estimates are presented for the sex-, age- and propensity score-matched samples. N. Treatments, number of concomitant pharmacological treatments; COVID-19 im- pact, estimated prevalence of COVID-19 at the corresponding healthcare area. clinical trial with results available demonstrated that among categories, (iv) area under the curve of a modified Sequential patients hospitalized with COVID-19 infection and receiving Organ Failure Assessment score. Similar to the previous study, chronic ACEis/ARBs, suspending ACEis/ARBs was not benefi- there was no significant difference in any of the secondary cial/deleterious in terms of mortality and hospitalization days endpoints examined, including all-cause death, length of hos- [13]. In addition, in the REPLACE COVID study, a multicentre pitalization, ICU admission or duration of mechanical ventila- trial involving 20 sites in seven countries, there was a total en- tion [10]. On the basis of these findings, no evidence exists to rolment of 152 patients who were admitted to hospital with recommend RAS blockade withdrawal for COVID-19 concerns. COVID-19. COVID-19 patients were randomly assigned to con- Furthermore, in patients at risk for CVD such as our studied tinuation or discontinuation of RAASis. The primary compos- population with HTN, diabetes or CVD, RAS discontinuation ite endpoint, which was a global rank score based on (i) days may play a deleterious role in cardio- and renoprotection. to death during hospitalization, (ii) days on mechanical venti- Limitations of our work include the retrospective and obser- lation or extracorporeal membrane oxigenation , (iii) days re- vational study design that lack the causal interpretation pro- quiring kidney replacement therapy, inotropes or pressors; vided by interventional studies and randomized clinical trials. and, for patients who did not fit into the previous three Another limitation of our study is that the number of people Renin–angiotensin system blockers and COVID-19 | 91 FIGURE 3: (continued) tested for COVID-19 varied over time during the study period. At our single centre. A total of 1413 patients were hospitalized with the beginning, a larger amount of people with possible COVID- a diagnosis of COVID-19. Of these, 435 (31%) patients were on 19 were not tested, especially those with no or mild symptoms RAS blockade at the time of hospitalization. Of these, 154 (35%) and who were not hospitalized. Thus, the proportion of people patients withdrew RAS therapy during the first 3 days of hospi- infected is very likely underestimated and the proportions of talization. Thus, based on this indirect analysis, we can infer patients with severe illness among the infected are likely over- that the most common decision at the time of the study was estimated. At the time of the study, there was no clear recom- to maintain RAS therapy. In any case, we do not believe that the mendation from scientific societies regarding what to do with interruption of this therapy during hospitalization in part of RAS therapy in hospitalized COVID-19 patients and the decision the cohort could affect the results, in light of the results of the to suspend or maintain RAS therapy was left to the physician’s BRACE CORONA study [13]. discretion. Although we do not have specific data in the entire In conclusion, this study provides evidence that the use of cohort, we were able to retrospectively analyse the data from RAS blockade in patients with HTN does not increase the risk of 92 | M.J. Soler et al. FIGURE 3: (continued) COVID-19 diagnosis or of disease severity among patients with FUNDING COVID-19. The study was partially funded by ‘CIBER de Epidemiologı́a y Salud Pública (CIBERESP)’. SUPPLEMENTARY DATA Supplementary data are available at ckj online. CONFLICT OF INTEREST STATEMENT ACKNOWLEDGEMENTS M.J.S. reports personal fees from NovoNordisk (Honoraria, We are grateful to Manuel Medina-Peralta from SISAP in the Advisory), Janssen (honoraria), Boehringer (honoraria, advisory, ICS for supervision of data extraction and Sònia Abilleira grant and consultancy), Eli Lilly (Honoraria), AstraZeneca (hon- from ICS for supervision of data extraction and for review- oraria and advisory), Esteve (honoraria), Fresenius Medical ing the manuscript. Care (honoraria), Mundipharma (honoraria and advisory) and Renin–angiotensin system blockers and COVID-19 | 93 Vifor (honoraria and advisory). M.J.S. is also a Member of the 16. Fosbøl EL, Butt JH, Østergaard L et al. Association of CKJ Editorial Board. angiotensin-converting enzyme inhibitor or angiotensin re- ceptor blocker use with COVID-19 diagnosis and mortality. JAMA 2020; 324: 168–177 REFERENCES 17. Schulz KF, Grimes DA. Case-control studies: research in reverse. Lancet 2002; 359: 431–434 1. 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