Tocilizumab versus sarilumab among adults hospitalised with COVID-19: target trial emulation across England and Scotland

Tocilizumab versus sarilumab among adults hospitalised with COVID-19: target trial emulation across England and Scotland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Tocilizumab versus sarilumab among adults hospitalised with COVID-19: target trial emulation across England and Scotland Laurie Tomlinson, Bang Zheng, Amanj Kurdi, Alain Amstutz, Amelia Green, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7702834/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The interleukin-6 (IL-6) inhibitors tocilizumab and sarilumab have been repurposed for COVID-19 treatment. However, discrepancies still exist across global and national COVID-19 guidelines, with limited data on the comparative effectiveness between these therapeutics especially during the delta/omicron periods. With the approval of NHS England and Public Health Scotland, we compared their effectiveness among adults hospitalised with COVID-19 using electronic health records data through the OpenSAFELY-TPP (England) and EAVE II (Scotland) platforms. Following the target trial emulation framework, 10,487 patients treated between July 2021 and February 2022, when both drugs were frequently prescribed, were included. In England, 1150 (20.1%) of 5710 participants receiving tocilizumab died by day 28 compared with 820 (20.4%) of 4025 participants receiving sarilumab (adjusted hazard ratio [aHR] 1.07, 95% CI 0.96–1.19). In Scotland, 114 (29.4%) of 388 participants receiving tocilizumab died by day 28 compared with 97 (27.0%) of 359 participants receiving sarilumab (aHR 0.92, 95% CI 0.68–1.23). There was no evidence of a difference in time to hospital discharge between the groups, and no credible effect modification by variant of concern, vaccination status, age, sex, ethnicity, body mass index, or comorbidities. Our findings provide supportive evidence for both drugs as alternative therapeutic options in COVID-19 in-patient management. Health sciences/Diseases/Infectious diseases/Viral infection Health sciences/Health care/Therapeutics Health sciences/Signs and symptoms/Respiratory signs and symptoms tocilizumab sarilumab COVID-19 target trial emulation comparative effectiveness Figures Figure 1 Figure 2 Figure 3 Introduction During the COVID-19 pandemic, several potential drug repurposing candidates were investigated for their effect in managing severe cases of COVID-19 infection, including interleukin-6 (IL-6) inhibitors. 1 Traditionally used for the treatment of rheumatoid arthritis, IL-6 inhibitors block IL-6 receptors that play a pivotal role in inflammatory processes 2 and therefore represent a promising treatment option for severe COVID-19 when the inflammatory phase dominates the pathogenesis. 3 Tocilizumab and sarilumab were the most frequently evaluated IL-6 inhibitors in randomised trials. 4 Several meta-analyses have pooled the evidence from randomised trials assessing benefit and harm of IL-6 inhibitors among patients hospitalised with COVID-19. 1,4–7 These concluded that the evidence for tocilizumab in preventing death and improving clinical outcomes is moderate to high, while the evidence for sarilumab has been graded as low. The recently published Randomised, Embedded, Multi-factorial Adaptive Platform Trial for Community-Acquired Pneumonia (REMAP-CAP) trial provided evidence that the two IL-6 inhibitors have equivalent effectiveness at improving survival and reducing duration of organ support. 8 However, all trials were conducted before the delta/omicron SARS-CoV-2 variants of concerns and their sub-lineages became globally prevalent and before large-scale vaccination programmes were in place. In the United Kingdom, both tocilizumab and sarilumab were added to the National Health Service (NHS) and the National Institute for Health and Care Excellence (NICE) Rapid Guideline on managing COVID-19 in January 2021. 9 , 10 Initially, NHS and NICE recommended sarilumab when tocilizumab was unavailable or contraindicated, but in March 2023 they stopped recommending sarilumab entirely due to its off-label status. 11 The World Health Organization (WHO) living guideline, on the other hand, has and continues to recommend both tocilizumab and sarilumab as equal treatment options. 5 Despite the global pandemic being over, COVID-19 still leads to hospitalisations among high-risk patient populations, highlighting the ongoing clinical needs. However, no randomised controlled trial compared sarilumab versus tocilizumab during the delta/omicron period, and no large-scale comparative effectiveness study of the two IL-6 inhibitors on COVID-19 outcomes have been conducted. To inform clinical guidelines and provide evidence for this knowledge gap, we conducted a target trial emulation study to compare the effectiveness of tocilizumab and sarilumab among adults hospitalised due to COVID-19 across England and Scotland between July 2021 and February 2022, when delta/omicron were prevalent and both drugs frequently prescribed. 12 Results We identified 9740 eligible COVID-19 patients in England (Table 1 a) and 747 in Scotland (Table 1 b) treated with an IL-6 inhibitor between July 1, 2021 and February 28, 2022. Of these, 4025 (41.3%) in England and 359 (48.1%) in Scotland received sarilumab. Those prescribed sarilumab in England were more likely to be of White ethnicity (84% sarilumab vs 79% tocilizumab), which was not the case in Scotland. Participants in the tocilizumab group were more recently vaccinated (median time from last vaccination to baseline of 176 days among sarilumab versus 128 days among tocilizumab in England and 167 days versus 140 days in Scotland). There were geographic prescription variations in England, and greater use of sarilumab during the omicron period, i.e., later in the study period across both nations. Table 1 a : Baseline characteristics in OpenSAFELY - England Characteristic Tocilizumab Sarilumab Total Number of patients 5710 (100) 4025 (100) 9740 (100) Age, in years, mean (SD) 58.1 (17.1) 59.4 (15.8) 58.6 (16.6) Age groups 18–39 955 (16.7) 480 (11.9) 1430 (14.7) 40–59 2005 (35.1) 1530 (38) 3540 (36.3) >=60 2750 (48.2) 2015 (50.1) 4765 (48.9) Sex Male 3610 (63.2) 2465 (61.2) 6075 (62.4) Female 2105 (36.9) 1560 (38.8) 3665 (37.6) Ethnicity Black 335 (5.9) 175 (4.3) 510 (5.2) Mixed 120 (2.1) 70 (1.7) 190 (2) Other 170 (3) 100 (2.5) 265 (2.7) Asian 495 (8.7) 265 (6.6) 765 (7.9) White 4525 (79.2) 3375 (83.9) 7895 (81.1) Missing 70 (1.2) 45 (1.1) 115 (1.2) Index of Multiple Deprivation 1 - Most deprived 1575 (27.6) 1110 (27.6) 2685 (27.6) 2 1240 (21.7) 875 (21.7) 2115 (21.7) 3 1075 (18.8) 770 (19.1) 1845 (18.9) 4 915 (16) 635 (15.8) 1550 (15.9) 5 - Least deprived 730 (12.8) 505 (12.5) 1235 (12.7) Missing 175 (3.1) 130 (3.2) 305 (3.1) Region NHS East of England 1080 (18.9) 625 (15.5) 1700 (17.5) London 560 (9.8) 280 (7) 840 (8.6) Midlands 1535 (26.9) 925 (23) 2460 (25.3) North East and Yorkshire 1205 (21.1) 1265 (31.4) 2470 (25.4) North West 255 (4.5) 150 (3.7) 405 (4.2) South East 285 (5) 255 (6.3) 540 (5.5) South West 795 (13.9) 535 (13.3) 1330 (13.7) Rural/Urban Urban major conurbation 1170 (20.5) 835 (20.7) 2005 (20.6) Urban minor conurbation 405 (7.1) 290 (7.2) 695 (7.1) Urban city and town 3020 (52.9) 2115 (52.5) 5135 (52.7) Rural town and fringe 565 (9.9) 395 (9.8) 960 (9.9) Rural village and dispersed 375 (6.6) 260 (6.5) 635 (6.5) Missing 175 (3.1) 130 (3.2) 310 (3.2) Days since hospital admission*, median (IQR) 1 (0–2) 1 (0–2) 1 (0–2) Days since 01.07.2021 (calendar period), median (IQR) 72 (38–143) 132 (110–158) 113 (58–153) Body mass index Underweight/normal 705 (12.3) 465 (11.6) 1170 (12) Overweight 1375 (24.1) 965 (24) 2340 (24) Obese 1280 (22.4) 890 (22.1) 2170 (22.3) Severely obese 1325 (23.2) 1065 (26.5) 2390 (24.5) Missing 1030 (18) 645 (16) 1675 (17.2) Chronic kidney disease stage 3–5 780 (13.7) 560 (13.9) 1335 (13.7) Severe liver disease 65 (1.1) 40 (1) 105 (1.1) Diabetes mellitus 1755 (30.7) 1370 (34) 3130 (32.1) Chronic cardiac disease 980 (17.2) 690 (17.1) 1670 (17.1) Arterial hypertension 2405 (42.1) 1765 (43.9) 4175 (42.9) Chronic respiratory disease 1435 (25.1) 1060 (26.3) 2495 (25.6) Solid cancer 840 (14.7) 540 (13.4) 1380 (14.2) Haematological diseases 290 (5.1) 150 (3.7) 440 (4.5) Immunosuppressive treatment 400 (7) 305 (7.6) 705 (7.2) Immunosuppressive disease 30 (0.5) 25 (0.6) 55 (0.6) Solid organ transplant 115 (2) 65 (1.6) 180 (1.8) Days since positive test**, median (IQR) 1 (0–3) 1 (0–3) 1 (0–3) Days since vaccination, median (IQR) 128 (85–175) 176 (123–198) 150 (93–190) Vaccination status Un-vaccinated 3025 (53) 1940 (48.2) 4960 (50.9) One vaccination 225 (3.9) 90 (2.2) 315 (3.2) Two vaccinations 1970 (34.5) 1600 (39.8) 3570 (36.7) Three or more vaccinations 495 (8.7) 395 (9.8) 890 (9.1) Treated during Omicron (after 06.12.2021) 1210 (21.2) 1030 (25.6) 2240 (23) History of COVID-19 treatment 660 (11.6) 415 (10.3) 1080 (11.1) COVID-19 re-infection (COVID-19 event > = 3 months) 80 (1.4) 55 (1.4) 130 (1.3) Abbreviation: SD (Standard deviation), IQR (interquartile range) * Missing: 130 (2.3%) in tocilizumab, 80 (2%) in sarilumab * Missing: 365 (6.4%) in tocilizumab, 240 (6.0%) in sarilumab Table 1 b : Baseline characteristics in EAVE II - Scotland Characteristic Tocilizumab Sarilumab Total Number of patients 388 (100) 359 (100) 747 (100) Age, in years, mean (SD) 60.9 (15) 60.1 (14.5) 60.5 (14.8) Age groups 18–39 40 (10.3) 38 (10.6) 78 (10.4) 40–59 131 (33.8) 135 (37.6) 266 (35.6) >=60 217 (55.9) 186 (51.8) 403 (53.9) Sex Female 145 (37.4) 136 (37.9) 281 (37.6) Male 243 (62.6) 223 (62.1) 466 (62.4) Ethnicity White 155 (39.9) 137 (38.2) 292 (39.1) Other 3 (0.8) 7 (1.9) 10 (1.3) Unknown 230 (59.3) 215 (59.9) 445 (59.6) Index of Multiple Deprivation 1 - Most deprived 138 (35.6) 163 (45.4) 301 (40.3) 2 98 (25.3) 77 (21.4) 175 (23.4) 3 71 (18.3) 50 (13.9) 121 (16.2) 4 43 (11.1) 34 (9.5) 77 (10.3) 5 - Least deprived 38 (9.8) 35 (9.7) 73 (9.8) Days since hospital admission, median (IQR) 1 (0–2) 1 (0–2) 1 (0–2) Days since 01.07.2021, median (IQR) 75 (54–104) 139 (115–173) 109 (73–154) Body mass index underweight and normal 27 (7) 22 (6.1) 49 (6.6) overweight 64 (16.5) 58 (16.2) 122 (16.3) obese 150 (38.7) 152 (42.3) 302 (40.4) Missing 147 (37.9) 127 (35.4) 274 (36.7) Chronic kidney disease stage 3–5 54 (13.9) 41 (11.4) 95 (12.7) Severe liver disease 8 (2.1) 7 (1.9) 15 (2) Diabetes mellitus 99 (25.5) 79 (22) 178 (23.8) Chronic cardiac disease 109 (28.1) 91 (25.3) 200 (26.8) Arterial hypertension 77 (22.3) 50 (14.9) 127 (18.6) Chronic respiratory disease 104 (26.8) 103 (28.7) 207 (27.7) Haematological diseases 17 (4.4) 14 (3.9) 31 (4.1) Days since vaccination, median (IQR) 140 (100–168) 167 (98–198) 150 (99–184) Vaccination status Un-vaccinated 134 (34.5) 146 (40.7) 280 (37.5) One vaccination 124 (32) 104 (29) 228 (30.5) Two or more vaccinations 130 (33.5) 109 (30.4) 239 (32) Treated during omicron (after 06.12.2021) 47 (12.1) 132 (36.8) 179 (24) Abbreviation: SD (Standard deviation), IQR (interquartile range) We emulated a pragmatic target trial on the comparative effectiveness separately across the two nations. The details of the study design are presented in appendix Table S1 . In England, 820 (20.4%) of 4025 participants in the sarilumab group died by day 28 compared with 1150 (20.1%) of 5710 participants in the tocilizumab group. After controlling for age, sex, NHS region, calendar time, ethnicity, Index of Multiple Deprivation, COVID-19 vaccination status, SARS-CoV-2 re-infection status, BMI, previous use of other COVID-19 treatments, and a set of comorbidities (diabetes, hypertension, chronic heart diseases, chronic respiratory diseases, moderate/severe renal disease, severe liver disease, solid cancer, hematological disease, immunosuppressive disease or treatment, and solid organ transplant), we did not detect a significant difference in mortality between the two groups (adjusted hazard ratio [aHR] 1.07, 95% CI 0.96–1.19; Fig. 1 ). In Scotland, 97 (27.0%) of 359 participants in the sarilumab group died by day 28 compared with 114 (29.4%) of 388 participants in the tocilizumab group (aHR 0.92, 0.68–1.23). By day 90, 930 (23.1%) deaths in the sarilumab group and 1355 (23.7%) deaths in the tocilizumab group occurred in England (aHR 1.01, 0.91–1.13), and 113 (31.5%) deaths in the sarilumab group and 127 (32.7%) deaths in the tocilizumab group occurred in Scotland (aHR 0.92, 0.69–1.21). Within 28 days, 2770 (68.8%) participants in the sarilumab group and 3950 (69.2%) participants in the tocilizumab group were discharged from hospital in England (aHR 0.97, 95% CI 0.91–1.04) and 246 (68.5%) participants in the sarilumab group and 260 (67.0%) participants in the tocilizumab group were discharged in Scotland (aHR 1.07, 95% CI 0.88–1.29). In the sensitivity analysis in the OpenSAFELY database, the results of the propensity-score weighted Cox models were consistent with those in the main analyses (Fig. 2 ). The covariate balance check after weighting the study population based on the propensity score indicated a sufficient balance (standardized mean difference < 0.1) between the two groups, except for one region (appendix figure S1 ). We therefore (i) additionally adjusted for region in the propensity score outcome model and (ii) trimmed the propensity score at the lower 5th percentile and upper 95th percentile, with the latter model yielding a similar point estimate as in the main results. Results of other sensitivity analyses also suggested comparative effectiveness between the two drugs. In the subgroup analyses, we did not observe likely effect modification on the comparative effectiveness by any of the prespecified subgroups (variant of concern, vaccination status, age, sex, ethnicity, body mass index, or comorbidities; Fig. 3 ). Discussion In this large-scale comparative effectiveness study, following the target trial emulation framework and conducted in two separate databases across England and Scotland, we found no significant difference in effectiveness between tocilizumab and sarilumab in terms of mortality or time to hospital discharge among adults hospitalised with severe COVID-19. In addition, we found no credible effect modification by variant of concern, vaccination status, age, sex, ethnicity, body mass index (BMI), or comorbidities (solid cancer, hematological disease, immunosuppressive treatment, diabetes, hypertension, chronic cardiac disease, and chronic respiratory disease). This study has a number of important strengths. Firstly, the study period covers a period of clinical equipoise regarding drug effectiveness and thus maximising comparability to a randomised trial. Both OpenSAFELY and EAVE II are sources of high-quality granular data that have enabled research directly influencing clinical decision-making during the pandemic. 13 , 14 The study is large and replication of findings from analyses across two UK nations with different data sources adds to the robustness of evidence. We used a standardised protocol following the target trial emulation framework to minimize design-related biases 15 , and findings were robust in multiple sensitivity analyses. Finally, a study within the NHS where healthcare is free at the point of delivery limits bias related to inequality in who can access healthcare compared to other global settings. However, there are a number of limitations. First, despite the granular and large-scale data, the possibility of unmeasured residual confounding cannot be ruled out, although there is no evidence to suggest that type of treatment was influenced by patient characteristics. Second, we were unable to use an identical set of baseline covariates related to treatment indication across the two nations due to lower sample size and the data availability in the EAVE II database, although in the English data, varying the set of confounders - both by including fewer or additional covariates - had only a minor impact on the overall results. This study adds to the existing evidence in a number of important ways. Firstly, we were able to conduct a hypothetical target trial that produced similar evidence to the recently published REMAP-CAP trial, the only randomised trial designed to compare tocilizumab with sarilumab. 8 Utilizing a Bayesian analytical approach, REMAP-CAP determined that tocilizumab and sarilumab met the criteria for equivalence. Recruitment of the last trial participant happened in April 2021. Our study has extended this evidence base by covering a study period after the emergence of the delta and omicron SARS-CoV-2 variants and including largely vaccinated populations. Previous meta-analyses have synthesized evidence from over 40 randomised trials that evaluated one or both IL-6 inhibitors against standard of care or placebo but were unable to establish the comparative effectiveness of tocilizumab and sarilumab. 1 , 4 Two subsequent network meta-analyses sought to address this gap by integrating direct and indirect evidence from randomised trials, showing similar effect estimates as our study, but rated the evidence for the comparison as low. 6 , 7 To date, several clinical guidelines have favoured tocilizumab over sarilumab for the treatment of severe COVID-19 - or have excluded sarilumab entirely from treatment recommendations. 11 However the WHO guidelines 5 endorse the use of both IL-6 inhibitors interchangeably. Our results taken together with results of the REMAP-CAP study suggest changes to guidelines to align with the WHO recommendation which provides treatment options even if there are issues with global supply chains of either drug. In conclusion, among adults hospitalised due to COVID-19, we observed no difference between sarilumab versus tocilizumab with regards to death and hospital discharge, providing real-world evidence to support clinical equivalence of both treatments. Methods Data sources In England, we used primary care records managed by the General Practitioner (GP) software provider The Phoenix Partnership (TPP), linked to Office of National Statistics (ONS) death registration data, the national coronavirus testing records from the Second Generation Surveillance System (SGSS), the national vaccine register (National Immunisation Management System [NIMS]), the NHS Secondary Use Service (SUS) data and the COVID-19 therapeutics dataset through OpenSAFELY. All data is linked, stored and analysed securely using the OpenSAFELY platform, https://www.opensafely.org/ , as part of the ongoing NHS England OpenSAFELY COVID-19 service. OpenSAFELY contains pseudonymised data of approximately 40% of the English population, including coded diagnoses, medications and physiological parameters. No free text data is included. No GP data from patients who have registered a Type-1 Opt out with their GP surgery is included in this study. All code is shared openly for review and re-use under MIT open license ( https://github.com/opensafely/tocilizumab_and_sarilumab ). Detailed pseudonymised patient data is potentially re-identifiable and therefore not shared. In Scotland, we used Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE) II ( https://usher.ed.ac.uk/eave-ii ) , a population health data platform during the COVID-19 pandemic that consolidated linked pseudonymised data across the health system using the Community Health Index number into a near real-time national longitudinal cohort, and included community prescribing (Prescribing Information System [PIS]), hospital prescribing (Hospital Electronic Prescribing and Medicines Administration [HEPMA]), hospital admissions and episodes (Scottish Morbidity Record [SMR01], Rapid Preliminary Inpatient Data [RAPID], Scottish Intensive Care Society Audit Group database [SICSAG]), vaccinations (Turas Vaccine Management Tool [TVMT]), SARS-CoV-2 testing (Electronic Communication of Surveillance Scotland [ECOSS]), and deaths (National Records of Scotland [NRS]). Covering 5.4 million people, approximately 99% of the Scottish population, EAVE II was developed in response to the COVID-19 pandemic. 16 Specification of the target trial We designed this observational comparative effectiveness study to emulate a target trial (i.e., a hypothetical pragmatic trial that would have answered the causal question of interest). The key components of the target trial emulation protocol are summarized in appendix table S1 . We emulated the pragmatic target trial separately across the two nations. The study is reported according to the REporting of studies Conducted using Observational Routinely collected health Data for PharmacoEpidemiological research (RECORD-PE) reporting guideline. 17 Study population The study population included adults (≥ 18 years old) who were hospitalised due to COVID-19 and prescribed either tocilizumab or sarilumab between July 1, 2021 and February 28, 2022. In July 2021, these two IL-6 inhibitors started being frequently prescribed for COVID-19 treatment12 and omicron peaked in January/February 2022.18 In OpenSAFELY, COVID-related admission was directly recorded in the COVID-19 therapeutics dataset, while in EAVE II admissions due to COVID-19 were identified as those occurring within 28 days of a positive Reverse Transcription Polymerase Chain Reaction (RT-PCR) test or those with an International Classification of Diseases, 10th Revision (ICD-10) code for COVID-19 (U07.1 and U07.2) in their admission record (in SMR01 and/or RAPID) as defined in previous studies.19 According to NHS clinical guidelines, to be eligible to receive an IL-6 inhibitor for treatment of COVID-19, COVID-19 patients should have been receiving dexamethasone (or an equivalent corticosteroid) unless contraindicated and requiring respiratory support (or having hypoxaemia with evidence of inflammation).10 Besides drug-specific hypersensitivity and slightly stricter requirements in terms of liver function and platelet count levels for sarilumab, the clinical eligibility criteria were the same across the two IL-6 treatments. People were excluded if they had (1) < 18 or ≥ 110 years of age, (2) missing information on sex or region, or (3) recorded as having received tocilizumab and sarilumab on the same date. Interventions of interest The interventions of interest were the first record of treatment with tocilizumab or sarilumab between July 1, 2021 and February 28, 2022. In OpenSAFELY, this was ascertained from the COVID-19 therapeutics dataset based on the national Blueteq system, while in EAVE II this was ascertained from HEPMA as defined in prior research. 20 Outcomes and follow-up The primary outcome was all-cause mortality within 28 days after treatment initiation, extracted from the ONS mortality database in OpenSAFELY and from the NRS mortality database in EAVE II. Secondary outcomes included 90-day all-cause mortality and time to hospital discharge since treatment initiation. The date of tocilizumab or sarilumab prescription was defined as the baseline date. People were then followed from their baseline date until the earliest of either reaching the outcome, death (when analyzing time to hospital discharge), or end of the follow-up period. Covariates To mimic the randomisation of the target trial, we assumed randomisation conditional on the following baseline covariates: age (restricted cubic splines), sex, NHS region, calendar time (restricted cubic splines), ethnicity (grouped into five broad categories: White, Black or Black British, Asian or Asian British, Mixed, Other), (Scottish) Index of Multiple Deprivation ([(S)IMD], as quintiles derived from the patient’s postcode at lower super output area level), COVID-19 vaccination status (unvaccinated, one vaccination, two vaccinations, or three or more), SARS-CoV-2 re-infection status (positive test or clinical diagnosis code or exposure to COVID-19 drug at least three months prior), BMI, most recent record, grouped into < 25.0, 25.0-<30.0, 30.0- = 35.0 kg/m2), previous use of other COVID-19 treatments (remdesivir, casirivimab/imdevimab and sotrovimab), diabetes, hypertension, chronic heart diseases, chronic respiratory diseases, moderate/severe renal disease, severe liver disease, solid cancer, hematological disease, immunosuppressive disease or treatment, and solid organ transplant. These covariates of interest, potentially prognostic for treatment initiation and outcome, were identified through literature review and discussions with domain experts. Comorbidities were identified through SNOMED CT codes in primary care records and ICD-10 in secondary care records. Ethnicity was identified through SNOMED CT codes and supplemented with information from secondary care records. Individuals with missing BMI, ethnicity, and (S)IMD were included with a missing indicator to maximise power, but alternative assumptions were tested in sensitivity analyses. Absence of recorded codes in terms of comorbidities, vaccination, reinfection, and prior COVID-19 treatment was assumed as not having such an event. Considering the sample size and data availability in the EAVE II database, the following baseline covariates were excluded for the EAVE II trial emulation: SARS-CoV-2 re-infection status, previous use of other COVID-19 treatments, solid cancer, immunosuppressive disease or treatment, and solid organ transplant. Statistical analysis Cox proportional hazards models, with follow-up time as the time scale, were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between treatment and each outcome in turn, adjusted for the above-mentioned covariates. In the OpenSAFELY analysis, the Cox model was stratified by NHS region to account for the geographical heterogeneity in COVID-19 outcomes. For the secondary outcome ‘time to discharge within 28 days’, we assumed death as a competing risk, censored them and assumed the worst case (i.e., no discharge until end of 28-day follow-up) - as recommended and applied in corresponding trial analyses. 21 , 22 We conducted several sensitivity analyses: (1) We explored a reduced set of baseline covariates for the conditional randomisation (age, sex, calendar time, ethnicity, IMD, COVID-19 vaccination status and SARS-CoV-2 re-infection status) and a minimal set (age, sex and calendar time). (2) We extended the conditional randomisation to additionally include rural/urban area, days between last COVID-19 vaccination and treatment initiation, and days between hospital admission and treatment initiation. (3) We used a propensity score weighted Cox model with robust variance estimators to mimic the randomisation instead of using covariate adjustment. The propensity score was derived from a logistic regression modelling the conditional probability of being treated with tocilizumab based on all baseline covariates. We conducted a covariate balance check after weighting using standardised mean differences between the two groups and a threshold of < 0.10 as the indicator for being balanced. (4) To explore the impact of missing data in IMD, BMI and ethnicity, we conducted multiple imputation using chained equations techniques. 23 (5) We assessed the association on COVID-19 related deaths only. These deaths were defined as a death whereby the underlying or contributory cause on the death certificate (ONS mortality database) was COVID-19 (ICD-10 codes U07.1, U07.2). We conducted several subgroup analyses to assess potential effect modifications: Dominant circulating variant of concern (delta variant before December 6, 2021, versus omicron BA.1 variant thereafter), COVID-19 vaccination status (none versus one or more vaccinations), age group (below 60 vs 60 years and above), sex (female versus male), ethnicity (white versus non-white), BMI (below 30 versus 30 or above), and presence versus absence of comorbidities (solid cancer, hematological disease, immunosuppressive treatment, diabetes, hypertension, chronic cardiac disease, and chronic respiratory disease). Effect modifications were tested using likelihood ratio tests, with Bonferroni correction applied to address multiple testing. When presenting the results from the OpenSAFELY database, all counts of 7 or below were redacted, the counts above 7 were rounded to the nearest five to minimise potential disclosure. Data management was performed using Python 3.10, with analysis carried out using Stata 16.1 and R v4.5.0. Code for data management and analysis in OpenSAFELY, the codelists and the pre-specified protocol, are archived online ( https://github.com/opensafely/tocilizumab_and_sarilumab/tree/main ) and all public logs of analysis activity are available online ( https://jobs.opensafely.org/effectiveness-and-safety-of-covid-19-treatments-for-hospitalised-patients/tocilizumab_sarilumab/releases/ ). Declarations Acknowledgments OpenSAFELY: We are very grateful for all the support received from the TPP Technical Operations team throughout this work, and for generous assistance from the information governance and database teams at NHS England and the NHS England Transformation Directorate. Information governance and ethical approval OpenSAFELY: NHS England is the data controller of the NHS England OpenSAFELY COVID-19 Service; TPP is the data processor; all study authors using OpenSAFELY have the approval of NHS England 24 . This implementation of OpenSAFELY is hosted within the TPP environment which is accredited to the ISO 27001 information security standard and is NHS IG Toolkit compliant 25 . Patient data has been pseudonymised for analysis and linkage using industry standard cryptographic hashing techniques; all pseudonymised datasets transmitted for linkage onto OpenSAFELY are encrypted; access to the NHS England OpenSAFELY COVID-19 service is via a virtual private network (VPN) connection; the researchers hold contracts with NHS England and only access the platform to initiate database queries and statistical models; all database activity is logged; only aggregate statistical outputs leave the platform environment following best practice for anonymisation of results such as statistical disclosure control for low cell counts 26 . The service adheres to the obligations of the UK General Data Protection Regulation (UK GDPR) and the Data Protection Act 2018. The service previously operated under notices initially issued in February 2020 by the the Secretary of State under Regulation 3(4) of the Health Service (Control of Patient Information) Regulations 2002 (COPI Regulations), which required organisations to process confidential patient information for COVID-19 purposes; this set aside the requirement for patient consent 27 . As of 1 July 2023, the Secretary of State has requested that NHS England continue to operate the Service under the COVID-19 Directions 2020 28 . In some cases of data sharing, the common law duty of confidence is met using, for example, patient consent or support from the Health Research Authority Confidentiality Advisory Group 29 . Taken together, these provide the legal bases to link patient datasets using the service. GP practices, which provide access to the primary care data, are required to share relevant health information to support the public health response to the pandemic, and have been informed of how the service operates. This study was approved by the Health Research Authority (REC reference 20/LO/0651) and by the LSHTM Ethics Board (reference 21863). EAVE II: Data are held by Public Health Scotland and were made available under the Public Health Scotland COVID-19 data governance framework. As such, individual-level patient data are not publicly available. Access to data was granted following appropriate ethical and governance approvals. Ethical approval for EAVE II was obtained from the National Research Ethics Service Committee, Southeast Scotland 02 (reference number: 12/SS/0201), and the Public Benefit and Privacy Panel for Health and Social Care (reference number: 1920-0279) Funding The OpenSAFELY platform is principally funded by grants from: NHS England [2023-2025]; The Wellcome Trust (222097/Z/20/Z) [2020-2024]; MRC (MR/V015737/1) [2020-2021]. Additional contributions to OpenSAFELY have been funded by grants from: MRC via the National Core Study programme, Longitudinal Health and Wellbeing strand (MC_PC_20030, MC_PC_20059) [2020-2022] and the Data and Connectivity strand (MC_PC_20058) [2021-2022]; NIHR and MRC via the CONVALESCENCE programme (COV-LT-0009, MC_PC_20051) [2021-2024]; NHS England via the Primary Care Medicines Analytics Unit [2021-2024]. This work is independent research jointly funded by the NIHR and UKRI [Characterisation, determinants, mechanisms and consequences of the long-term effects of COVID-19: providing the evidence base for health care services, COV-LT-0009]. This study was also supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101015736. The views expressed in this publication are those of the author(s) and not necessarily those of NIHR, the Department of Health and Social Care, UKRI, NHS England, UK Health Security Agency (UKHSA), or other funders. Funders had no role in the study design, collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. EAVE II was funded by the NIHR Health Technology Assessment Programme [project number 13/34/14], the MRC [MR/R008345/1], and HDR UK through the Data and Connectivity National Core Study. The project has also received support from the Scottish Government and BREATHE – the Health Data Research Hub for Respiratory Health, part of HDR UK. Contributors BZ, LT and AS conceived the idea of this study. BZ, AK and CR were responsible for the data analysis with input from AA, AS and LT. AA and BZ drafted the original version of the manuscript with input from AS, LT, AK and CR. AG, BMK, AM, SB and BG provided administrative and technical support. All authors contributed to the critical review of the manuscript and approved the final manuscript. Declaration of competing interests LT is funded by an NIHR Research Professorship NIHR302405. AA receives his salary from the postdoc mobility grant of the Swiss National Science Foundation (#P500PM_221961). BG has received research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the NHS NIHR, the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he has previously been a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data. AM has represented the RCGP in the health informatics group and the Profession Advisory Group that advises on access to GP Data for Pandemic Planning and Research (GDPPR); the latter was a paid role. AM is a former employee and interim Chief Medical Officer of NHS Digital. AM has consulted for health care vendors, the last time in 2022; the companies consulted in the last 3 years have no relationship to OpenSAFELY. Patient and public involvement and engagement OpenSAFELY: OpenSAFELY has involved patients and the public in various ways: we developed a public website that provides a detailed description of the platform in language suitable for a lay audience (https://opensafely.org); we have participated in two citizen juries exploring public trust in OpenSAFELY; we have co-developed an explainer video (https://www.opensafely.org/about/); we have patient representation who are experts by experience on our OpenSAFELY Oversight Board; we have partnered with Understanding Patient Data to produce lay explainers on the importance of large datasets for research; we have presented at various online public engagement events to key communities (e.g., Healthcare Excellence Through Technology; Faculty of Clinical Informatics annual conference; NHS Assembly; HDRUK symposium); and more. To ensure the patient voice is represented, we are working closely to decide on language choices with appropriate medical research charities (e.g., Association of Medical Research Charities). We will share information and interpretation of our findings through press releases, social media channels, and plain language summaries. Data access and verification OpenSAFELY: Access to the underlying identifiable and potentially re-identifiable pseudonymised electronic health record data is tightly governed by various legislative and regulatory frameworks, and restricted by best practice. The data in the NHS England OpenSAFELY COVID-19 service is drawn from General Practice data across England where TPP is the data processor. TPP developers initiate an automated process to create pseudonymised records in the core OpenSAFELY database, which are copies of key structured data tables in the identifiable records. These pseudonymised records are linked onto key external data resources that have also been pseudonymised via SHA-512 one-way hashing of NHS numbers using a shared salt. University of Oxford, Bennett Institute for Applied Data Science developers and PIs, who hold contracts with NHS England, have access to the OpenSAFELY pseudonymised data tables to develop the OpenSAFELY tools. These tools in turn enable researchers with OpenSAFELY data access agreements to write and execute code for data management and data analysis without direct access to the underlying raw pseudonymised patient data, and to review the outputs of this code. All code for the full data management pipeline — from raw data to completed results for this analysis — and for the OpenSAFELY platform as a whole is available for review at https://github.com/OpenSAFELY. References WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group, Shankar-Hari M, Vale CL, Godolphin PJ, Fisher D, Higgins JPT et al (2021) Association Between Administration of IL-6 Antagonists and Mortality Among Patients Hospitalized for COVID-19: A Meta-analysis. JAMA 326(6):499–518 van der Schoels MM, Breedveld FC, Burmester GR, Dougados M, Emery P et al (2013) Blocking the effects of interleukin-6 in rheumatoid arthritis and other inflammatory rheumatic diseases: systematic literature review and meta-analysis informing a consensus statement. Ann Rheum Dis 72(4):583–589 McElvaney OJ, Curley GF, Rose-John S, McElvaney NG (2021) Interleukin-6: obstacles to targeting a complex cytokine in critical illness. Lancet Respir Med 9(6):643–654 Ghosn LCA, Evrenoglou T, Davidson M, Graña C, Schmucker C, Bollig C, Henschke N, Sguassero Y, Nejstgaard CH, Menon S, Van Nguyen T, Ferrand G, Kapp P, Riveros C, Ávila C, Devane D, Meerpohl JJ, Rada G, Ravaud P (2021) Boutron, Isabelle. Interleukin-6 blocking agents for treating COVID-19: a living systematic review. Cochrane Database Syst Reviews 2021(3):NA–NA Hróbjartsson, Asbjørn; Grasselli, Giacomo; Tovey, David Clinical management of COVID-19 Living guideline, 18 August 2023 [Internet]. [cited 2025 Apr 30]. Available from: https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2023.2 Zeraatkar D, Cusano E, Martínez JPD, Qasim A, Mangala S, Kum E et al (2022) Use of tocilizumab and sarilumab alone or in combination with corticosteroids for covid-19: systematic review and network meta-analysis. BMJ Med 1(1):e000036 Godolphin PJ, Fisher DJ, Berry LR, Derde LPG, Diaz JV, Gordon AC et al (2022) Association between tocilizumab, sarilumab and all-cause mortality at 28 days in hospitalised patients with COVID-19: A network meta-analysis. PLoS ONE 17(7):e0270668 Derde L, Gordon AC, Mouncey PR, Al-Beidh F, Rowan KM, Nichol AD et al (2025) Tocilizumab, sarilumab and anakinra in critically ill patients with COVID-19: a randomised, controlled, open-label, adaptive platform trial. Thorax 80(8):530–539 COVID-19 rapid evidence summary Sarilumab for COVID-19 | Advice | NICE [Internet]. NICE; 2021 [cited 2025 Apr 30]. Available from: https://www.nice.org.uk/advice/es34 Messaging CMO NHS clinical guidance for: Interleukin-6 inhibitors (tocilizumab or sarilumab) for adult patients hospitalised due to COVID-19. This version was issued on 29-Nov-2022 08:29:29 [Internet]. [cited 2025 Apr 30]. Available from: https://www.cas.mhra.gov.uk/ViewandAcknowledgment/ViewAlert.aspx?AlertID=103222 Update information | COVID-19 rapid guideline: managing COVID-19 | Guidance | NICE [Internet]. NICE (2021) [cited 2025 Apr 30]. Available from: https://www.nice.org.uk/guidance/ng191/chapter/update-information Statistics Statistics » COVID-19 Therapeutics (antivirals, neutralising monoclonal antibodies and interleukin 6 inhibitors) [Internet]. [cited 2025 Aug 7]. Available from: https://www.england.nhs.uk/statistics/statistical-work-areas/covid-therapeutics-antivirals-and-neutralising-monoclonal-antibodies/ Research Publications | EAVE II [Internet]. Usher Institute (2024) [cited 2025 Aug 7]. Available from: https://usher.ed.ac.uk/eave-ii/key-outputs/our-publications OpenSAFELY Research Highlights | Bennett Institute for Applied Data Science [Internet] 2023 [cited 2025 Aug 7]. Available from: https://www.bennett.ox.ac.uk/blog/2023/03/opensafely-research-highlights/ Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I (2016) Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol 79:70–75 Simpson CR, Robertson C, Vasileiou E, McMenamin J, Gunson R, Ritchie LD et al (2020) Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II): protocol for an observational study using linked Scottish national data. BMJ Open 10(6):e039097 Langan SM, Schmidt SA, Wing K, Ehrenstein V, Nicholls SG, Filion KB et al (2018) The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology (RECORD-PE). BMJ 363:k3532 Chadeau-Hyam M, Tang D, Eales O, Bodinier B, Wang H, Jonnerby J et al Omicron SARS-CoV-2 epidemic in England during February 2022: A series of cross-sectional community surveys. The Lancet Regional Health – Europe [Internet]. 2022 Oct 1 [cited 2025 Aug 7];21. Available from: https://www.thelancet.com/journals/lanepe/article/PIIS2666-7762(22)00158-2/fulltext Vasileiou E, Simpson CR, Shi T, Kerr S, Agrawal U, Akbari A et al (2021) Interim findings from first-dose mass COVID-19 vaccination roll-out and COVID-19 hospital admissions in Scotland: a national prospective cohort study. Lancet 397(10285):1646–1657 Tibble H, Mueller T, Proud E, Hall E, Kurdi A, Robertson C et al (2023) Uptake of monoclonal antibodies and antiviral therapies for COVID-19 in Scotland. Lancet 401(10371):101–102 Li H, Gleason KJ, Hu Y, Lovell SS, Mukhopadhyay S, Wang L et al (2022) Handling death as an intercurrent event in time to recovery analysis in COVID-19 treatment clinical trials. Contemp Clin Trials 119:106758 Amstutz A, Speich B, Mentré F, Rueegg CS, Belhadi D, Assoumou L et al (2023) Effects of remdesivir in patients hospitalised with COVID-19: a systematic review and individual patient data meta-analysis of randomised controlled trials. Lancet Respiratory Med 11(5):453–464 White IR, Royston P, Wood AM (2011) Multiple imputation using chained equations: Issues and guidance for practice. Stat Med 30(4):377–399 The NHS, England OpenSAFELY COVID-19 service - privacy notice [Internet]. NHS England Digital. [cited 2025 Apr 30]. Available from: https://digital.nhs.uk/coronavirus/coronavirus-covid-19-response-information-governance-hub/the-nhs-england-opensafely-covid-19-service-privacy-notice Data Security and Protection Toolkit [Internet]. [cited 2025 Apr 30]. Available from: https://www.dsptoolkit.nhs.uk/ ISB1523 Anonymisation Standard for Publishing Health and Social Care Data [Internet]. NHS England Digital. [cited 2025 Jul 24]. Available from: https://digital.nhs.uk/data-and-information/information-standards/governance/latest-activity/standards-and-collections/isb1523-anonymisation-standard-for-publishing-health-and-social-care-data [Withdrawn] [withdrawn] Coronavirus (COVID-19) notice under regulation 3(4) of the Health Service (Control of Patient Information) Regulations 2002 – general [Internet]. GOV.UK. 2022 [cited 2025 Jul 24]. Available from: https://www.gov.uk/government/publications/coronavirus-covid-19-notification-of-data-controllers-to-share-information/coronavirus-covid-19-notice-under-regulation-34-of-the-health-service-control-of-patient-information-regulations-2002-general--2 COVID-19 Public Health Directions 2020 [Internet]. NHS England Digital. [cited 2025 Jul 24]. Available from: https://digital.nhs.uk/about-nhs-digital/corporate-information-and-documents/directions-and-data-provision-notices/secretary-of-state-directions/covid-19-public-health-directions-2020 Confidentiality Advisory Group [Internet] Health Research Authority. [cited 2025 Jul 24]. Available from: https://www.hra.nhs.uk/about-us/committees-and-services/confidentiality-advisory-group/ Additional Declarations Yes there is potential Competing Interest. LT is funded by an NIHR Research Professorship NIHR302405. AA receives his salary from the postdoc mobility grant of the Swiss National Science Foundation (#P500PM_221961). BG has received research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the NHS NIHR, the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he has previously been a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data. AM has represented the RCGP in the health informatics group and the Profession Advisory Group that advises on access to GP Data for Pandemic Planning and Research (GDPPR); the latter was a paid role. AM is a former employee and interim Chief Medical Officer of NHS Digital. AM has consulted for health care vendors, the last time in 2022; the companies consulted in the last 3 years have no relationship to OpenSAFELY. Supplementary Files IL6compeffectivenessTTEappendix.docx Supplementary table and figure Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":205238,"visible":true,"origin":"","legend":"\u003cp\u003ePrimary and secondary outcomes\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7702834/v1/335c7cbf92e2d4bffe381375.png"},{"id":92585003,"identity":"f92b69f6-863e-4eb1-934d-32d4c1de15b4","added_by":"auto","created_at":"2025-10-01 10:22:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":235088,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analyses (OpenSAFELY only)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7702834/v1/8132df274d13dde7d77d171b.png"},{"id":92584652,"identity":"d05083f3-33a3-4449-8478-0824e2a77136","added_by":"auto","created_at":"2025-10-01 10:14:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":413244,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot presenting subgroup analyses for the primary outcome (OpenSAFELY only)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7702834/v1/2418a1b95cda69d30100035a.png"},{"id":92585633,"identity":"098e165e-7909-439a-ab2f-673c336a0555","added_by":"auto","created_at":"2025-10-01 10:30:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2165999,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7702834/v1/5337d0e6-5033-4674-897e-4ef074dc9a6e.pdf"},{"id":92584660,"identity":"c2dacf79-18b6-4fdb-9eb7-6a0a78320ceb","added_by":"auto","created_at":"2025-10-01 10:14:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2821856,"visible":true,"origin":"","legend":"Supplementary table and figure","description":"","filename":"IL6compeffectivenessTTEappendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7702834/v1/1cfc546e74b058061880c585.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nLT is funded by an NIHR Research Professorship NIHR302405. AA receives his salary from the postdoc mobility grant of the Swiss National Science Foundation (#P500PM_221961). BG has received research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the NHS NIHR, the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he has previously been a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data. AM has represented the RCGP in the health informatics group and the Profession Advisory Group that advises on access to GP Data for Pandemic Planning and Research (GDPPR); the latter was a paid role. AM is a former employee and interim Chief Medical Officer of NHS Digital. AM has consulted for health care vendors, the last time in 2022; the companies consulted in the last 3 years have no relationship to OpenSAFELY.","formattedTitle":"Tocilizumab versus sarilumab among adults hospitalised with COVID-19: target trial emulation across England and Scotland","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDuring the COVID-19 pandemic, several potential drug repurposing candidates were investigated for their effect in managing severe cases of COVID-19 infection, including interleukin-6 (IL-6) inhibitors.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Traditionally used for the treatment of rheumatoid arthritis, IL-6 inhibitors block IL-6 receptors that play a pivotal role in inflammatory processes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and therefore represent a promising treatment option for severe COVID-19 when the inflammatory phase dominates the pathogenesis.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Tocilizumab and sarilumab were the most frequently evaluated IL-6 inhibitors in randomised trials.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eSeveral meta-analyses have pooled the evidence from randomised trials assessing benefit and harm of IL-6 inhibitors among patients hospitalised with COVID-19.\u003csup\u003e1,4\u0026ndash;7\u003c/sup\u003e These concluded that the evidence for tocilizumab in preventing death and improving clinical outcomes is moderate to high, while the evidence for sarilumab has been graded as low. The recently published Randomised, Embedded, Multi-factorial Adaptive Platform Trial for Community-Acquired Pneumonia (REMAP-CAP) trial provided evidence that the two IL-6 inhibitors have equivalent effectiveness at improving survival and reducing duration of organ support.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e However, all trials were conducted before the delta/omicron SARS-CoV-2 variants of concerns and their sub-lineages became globally prevalent and before large-scale vaccination programmes were in place.\u003c/p\u003e\u003cp\u003eIn the United Kingdom, both tocilizumab and sarilumab were added to the National Health Service (NHS) and the National Institute for Health and Care Excellence (NICE) Rapid Guideline on managing COVID-19 in January 2021.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Initially, NHS and NICE recommended sarilumab when tocilizumab was unavailable or contraindicated, but in March 2023 they stopped recommending sarilumab entirely due to its off-label status.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e The World Health Organization (WHO) living guideline, on the other hand, has and continues to recommend both tocilizumab and sarilumab as equal treatment options.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDespite the global pandemic being over, COVID-19 still leads to hospitalisations among high-risk patient populations, highlighting the ongoing clinical needs. However, no randomised controlled trial compared sarilumab versus tocilizumab during the delta/omicron period, and no large-scale comparative effectiveness study of the two IL-6 inhibitors on COVID-19 outcomes have been conducted. To inform clinical guidelines and provide evidence for this knowledge gap, we conducted a target trial emulation study to compare the effectiveness of tocilizumab and sarilumab among adults hospitalised due to COVID-19 across England and Scotland between July 2021 and February 2022, when delta/omicron were prevalent and both drugs frequently prescribed.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe identified 9740 eligible COVID-19 patients in England (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and 747 in Scotland (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) treated with an IL-6 inhibitor between July 1, 2021 and February 28, 2022. Of these, 4025 (41.3%) in England and 359 (48.1%) in Scotland received sarilumab. Those prescribed sarilumab in England were more likely to be of White ethnicity (84% sarilumab vs 79% tocilizumab), which was not the case in Scotland. Participants in the tocilizumab group were more recently vaccinated (median time from last vaccination to baseline of 176 days among sarilumab versus 128 days among tocilizumab in England and 167 days versus 140 days in Scotland). There were geographic prescription variations in England, and greater use of sarilumab during the omicron period, i.e., later in the study period across both nations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003ea\u003c/b\u003e: Baseline characteristics in OpenSAFELY - England\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTocilizumab\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSarilumab\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of patients\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5710 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4025 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9740 (100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, in years, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.1 (17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.4 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.6 (16.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge groups\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e955 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e480 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1430 (14.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2005 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1530 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3540 (36.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;=60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2750 (48.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2015 (50.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4765 (48.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3610 (63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2465 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6075 (62.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2105 (36.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1560 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3665 (37.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e335 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e175 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e510 (5.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e190 (2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e265 (2.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e495 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e265 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e765 (7.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4525 (79.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3375 (83.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7895 (81.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e115 (1.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndex of Multiple Deprivation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 - Most deprived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1575 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1110 (27.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2685 (27.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1240 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e875 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2115 (21.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1075 (18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e770 (19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1845 (18.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e915 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e635 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1550 (15.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 - Least deprived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e730 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e505 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1235 (12.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e305 (3.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion NHS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEast of England\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1080 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e625 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1700 (17.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLondon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e560 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e280 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e840 (8.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMidlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1535 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e925 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2460 (25.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorth East and Yorkshire\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1205 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1265 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2470 (25.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorth West\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e255 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e405 (4.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouth East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e285 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e255 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e540 (5.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouth West\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e795 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e535 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1330 (13.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRural/Urban\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban major conurbation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1170 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e835 (20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2005 (20.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban minor conurbation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e405 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e290 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e695 (7.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban city and town\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3020 (52.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2115 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5135 (52.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural town and fringe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e565 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e395 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e960 (9.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural village and dispersed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e375 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e260 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e635 (6.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e310 (3.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDays since hospital admission*, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDays since 01.07.2021 (calendar period), median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (38\u0026ndash;143)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e132 (110\u0026ndash;158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e113 (58\u0026ndash;153)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody mass index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderweight/normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e705 (12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e465 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1170 (12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1375 (24.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e965 (24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2340 (24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1280 (22.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e890 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2170 (22.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeverely obese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1325 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1065 (26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2390 (24.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1030 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e645 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1675 (17.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic kidney disease stage 3\u0026ndash;5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e780 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e560 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1335 (13.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSevere liver disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e105 (1.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes mellitus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1755 (30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1370 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3130 (32.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic cardiac disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e980 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e690 (17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1670 (17.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArterial hypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2405 (42.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1765 (43.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4175 (42.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic respiratory disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1435 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1060 (26.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2495 (25.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSolid cancer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e840 (14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e540 (13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1380 (14.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHaematological diseases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e290 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150 (3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e440 (4.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eImmunosuppressive treatment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e305 (7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e705 (7.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eImmunosuppressive disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55 (0.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSolid organ transplant\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e180 (1.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDays since positive test**, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0\u0026ndash;3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0\u0026ndash;3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0\u0026ndash;3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDays since vaccination, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128 (85\u0026ndash;175)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e176 (123\u0026ndash;198)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150 (93\u0026ndash;190)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVaccination status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUn-vaccinated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3025 (53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1940 (48.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4960 (50.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne vaccination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e225 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e315 (3.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTwo vaccinations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1970 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1600 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3570 (36.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThree or more vaccinations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e495 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e395 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e890 (9.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreated during Omicron (after 06.12.2021)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1210 (21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1030 (25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2240 (23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistory of COVID-19 treatment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e660 (11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e415 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1080 (11.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCOVID-19 re-infection (COVID-19 event\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3 months)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e130 (1.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: SD (Standard deviation), IQR (interquartile range)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Missing: 130 (2.3%) in tocilizumab, 80 (2%) in sarilumab\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Missing: 365 (6.4%) in tocilizumab, 240 (6.0%) in sarilumab\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eb\u003c/b\u003e: Baseline characteristics in EAVE II - Scotland\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTocilizumab\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSarilumab\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of patients\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e388 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e359 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e747 (100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, in years, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.9 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.1 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.5 (14.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge groups\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78 (10.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 (33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135 (37.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e266 (35.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;=60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e217 (55.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e186 (51.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e403 (53.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145 (37.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e281 (37.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e243 (62.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e223 (62.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e466 (62.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137 (38.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e292 (39.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (1.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230 (59.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e215 (59.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e445 (59.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndex of Multiple Deprivation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 - Most deprived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e163 (45.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e301 (40.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e175 (23.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e121 (16.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77 (10.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 - Least deprived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73 (9.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDays since hospital admission, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDays since 01.07.2021, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (54\u0026ndash;104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139 (115\u0026ndash;173)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e109 (73\u0026ndash;154)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody mass index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eunderweight and normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49 (6.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122 (16.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eobese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150 (38.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e152 (42.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e302 (40.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147 (37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127 (35.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e274 (36.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic kidney disease stage 3\u0026ndash;5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95 (12.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSevere liver disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes mellitus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99 (25.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79 (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e178 (23.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic cardiac disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91 (25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e200 (26.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArterial hypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e127 (18.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic respiratory disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e207 (27.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHaematological diseases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (4.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDays since vaccination, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (100\u0026ndash;168)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e167 (98\u0026ndash;198)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150 (99\u0026ndash;184)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVaccination status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUn-vaccinated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e146 (40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e280 (37.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne vaccination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124 (32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e104 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e228 (30.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTwo or more vaccinations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130 (33.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e239 (32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreated during omicron (after 06.12.2021)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e132 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e179 (24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: SD (Standard deviation), IQR (interquartile range)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe emulated a pragmatic target trial on the comparative effectiveness separately across the two nations. The details of the study design are presented in appendix Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. In England, 820 (20.4%) of 4025 participants in the sarilumab group died by day 28 compared with 1150 (20.1%) of 5710 participants in the tocilizumab group. After controlling for age, sex, NHS region, calendar time, ethnicity, Index of Multiple Deprivation, COVID-19 vaccination status, SARS-CoV-2 re-infection status, BMI, previous use of other COVID-19 treatments, and a set of comorbidities (diabetes, hypertension, chronic heart diseases, chronic respiratory diseases, moderate/severe renal disease, severe liver disease, solid cancer, hematological disease, immunosuppressive disease or treatment, and solid organ transplant), we did not detect a significant difference in mortality between the two groups (adjusted hazard ratio [aHR] 1.07, 95% CI 0.96\u0026ndash;1.19; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In Scotland, 97 (27.0%) of 359 participants in the sarilumab group died by day 28 compared with 114 (29.4%) of 388 participants in the tocilizumab group (aHR 0.92, 0.68\u0026ndash;1.23). By day 90, 930 (23.1%) deaths in the sarilumab group and 1355 (23.7%) deaths in the tocilizumab group occurred in England (aHR 1.01, 0.91\u0026ndash;1.13), and 113 (31.5%) deaths in the sarilumab group and 127 (32.7%) deaths in the tocilizumab group occurred in Scotland (aHR 0.92, 0.69\u0026ndash;1.21).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWithin 28 days, 2770 (68.8%) participants in the sarilumab group and 3950 (69.2%) participants in the tocilizumab group were discharged from hospital in England (aHR 0.97, 95% CI 0.91\u0026ndash;1.04) and 246 (68.5%) participants in the sarilumab group and 260 (67.0%) participants in the tocilizumab group were discharged in Scotland (aHR 1.07, 95% CI 0.88\u0026ndash;1.29).\u003c/p\u003e\u003cp\u003eIn the sensitivity analysis in the OpenSAFELY database, the results of the propensity-score weighted Cox models were consistent with those in the main analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The covariate balance check after weighting the study population based on the propensity score indicated a sufficient balance (standardized mean difference\u0026thinsp;\u0026lt;\u0026thinsp;0.1) between the two groups, except for one region (appendix figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We therefore (i) additionally adjusted for region in the propensity score outcome model and (ii) trimmed the propensity score at the lower 5th percentile and upper 95th percentile, with the latter model yielding a similar point estimate as in the main results. Results of other sensitivity analyses also suggested comparative effectiveness between the two drugs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the subgroup analyses, we did not observe likely effect modification on the comparative effectiveness by any of the prespecified subgroups (variant of concern, vaccination status, age, sex, ethnicity, body mass index, or comorbidities; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large-scale comparative effectiveness study, following the target trial emulation framework and conducted in two separate databases across England and Scotland, we found no significant difference in effectiveness between tocilizumab and sarilumab in terms of mortality or time to hospital discharge among adults hospitalised with severe COVID-19. In addition, we found no credible effect modification by variant of concern, vaccination status, age, sex, ethnicity, body mass index (BMI), or comorbidities (solid cancer, hematological disease, immunosuppressive treatment, diabetes, hypertension, chronic cardiac disease, and chronic respiratory disease).\u003c/p\u003e\u003cp\u003eThis study has a number of important strengths. Firstly, the study period covers a period of clinical equipoise regarding drug effectiveness and thus maximising comparability to a randomised trial. Both OpenSAFELY and EAVE II are sources of high-quality granular data that have enabled research directly influencing clinical decision-making during the pandemic.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e The study is large and replication of findings from analyses across two UK nations with different data sources adds to the robustness of evidence. We used a standardised protocol following the target trial emulation framework to minimize design-related biases\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and findings were robust in multiple sensitivity analyses. Finally, a study within the NHS where healthcare is free at the point of delivery limits bias related to inequality in who can access healthcare compared to other global settings.\u003c/p\u003e\u003cp\u003eHowever, there are a number of limitations. First, despite the granular and large-scale data, the possibility of unmeasured residual confounding cannot be ruled out, although there is no evidence to suggest that type of treatment was influenced by patient characteristics. Second, we were unable to use an identical set of baseline covariates related to treatment indication across the two nations due to lower sample size and the data availability in the EAVE II database, although in the English data, varying the set of confounders - both by including fewer or additional covariates - had only a minor impact on the overall results.\u003c/p\u003e\u003cp\u003eThis study adds to the existing evidence in a number of important ways. Firstly, we were able to conduct a hypothetical target trial that produced similar evidence to the recently published REMAP-CAP trial, the only randomised trial designed to compare tocilizumab with sarilumab.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Utilizing a Bayesian analytical approach, REMAP-CAP determined that tocilizumab and sarilumab met the criteria for equivalence. Recruitment of the last trial participant happened in April 2021. Our study has extended this evidence base by covering a study period after the emergence of the delta and omicron SARS-CoV-2 variants and including largely vaccinated populations. Previous meta-analyses have synthesized evidence from over 40 randomised trials that evaluated one or both IL-6 inhibitors against standard of care or placebo but were unable to establish the comparative effectiveness of tocilizumab and sarilumab.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Two subsequent network meta-analyses sought to address this gap by integrating direct and indirect evidence from randomised trials, showing similar effect estimates as our study, but rated the evidence for the comparison as low.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo date, several clinical guidelines have favoured tocilizumab over sarilumab for the treatment of severe COVID-19 - or have excluded sarilumab entirely from treatment recommendations.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e However the WHO guidelines\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e endorse the use of both IL-6 inhibitors interchangeably. Our results taken together with results of the REMAP-CAP study suggest changes to guidelines to align with the WHO recommendation which provides treatment options even if there are issues with global supply chains of either drug.\u003c/p\u003e\u003cp\u003eIn conclusion, among adults hospitalised due to COVID-19, we observed no difference between sarilumab versus tocilizumab with regards to death and hospital discharge, providing real-world evidence to support clinical equivalence of both treatments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData sources\u003c/h2\u003e\u003cp\u003eIn England, we used primary care records managed by the General Practitioner (GP) software provider The Phoenix Partnership (TPP), linked to Office of National Statistics (ONS) death registration data, the national coronavirus testing records from the Second Generation Surveillance System (SGSS), the national vaccine register (National Immunisation Management System [NIMS]), the NHS Secondary Use Service (SUS) data and the COVID-19 therapeutics dataset through OpenSAFELY. All data is linked, stored and analysed securely using the OpenSAFELY platform, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.opensafely.org/\u003c/span\u003e\u003cspan address=\"https://www.opensafely.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, as part of the ongoing NHS England OpenSAFELY COVID-19 service. OpenSAFELY contains pseudonymised data of approximately 40% of the English population, including coded diagnoses, medications and physiological parameters. No free text data is included. No GP data from patients who have registered a Type-1 Opt out with their GP surgery is included in this study. All code is shared openly for review and re-use under MIT open license (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/opensafely/tocilizumab_and_sarilumab\u003c/span\u003e\u003cspan address=\"https://github.com/opensafely/tocilizumab_and_sarilumab\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Detailed pseudonymised patient data is potentially re-identifiable and therefore not shared.\u003c/p\u003e\u003cp\u003eIn Scotland, we used Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE) II (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://usher.ed.ac.uk/eave-ii\u003c/span\u003e\u003cspan address=\"https://usher.ed.ac.uk/eave-ii\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, a population health data platform during the COVID-19 pandemic that consolidated linked pseudonymised data across the health system using the Community Health Index number into a near real-time national longitudinal cohort, and included community prescribing (Prescribing Information System [PIS]), hospital prescribing (Hospital Electronic Prescribing and Medicines Administration [HEPMA]), hospital admissions and episodes (Scottish Morbidity Record [SMR01], Rapid Preliminary Inpatient Data [RAPID], Scottish Intensive Care Society Audit Group database [SICSAG]), vaccinations (Turas Vaccine Management Tool [TVMT]), SARS-CoV-2 testing (Electronic Communication of Surveillance Scotland [ECOSS]), and deaths (National Records of Scotland [NRS]). Covering 5.4\u0026nbsp;million people, approximately 99% of the Scottish population, EAVE II was developed in response to the COVID-19 pandemic.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSpecification of the target trial\u003c/h3\u003e\n\u003cp\u003eWe designed this observational comparative effectiveness study to emulate a target trial (i.e., a hypothetical pragmatic trial that would have answered the causal question of interest). The key components of the target trial emulation protocol are summarized in appendix table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. We emulated the pragmatic target trial separately across the two nations. The study is reported according to the REporting of studies Conducted using Observational Routinely collected health Data for PharmacoEpidemiological research (RECORD-PE) reporting guideline.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study population included adults (\u0026ge;\u0026thinsp;18 years old) who were hospitalised due to COVID-19 and prescribed either tocilizumab or sarilumab between July 1, 2021 and February 28, 2022. In July 2021, these two IL-6 inhibitors started being frequently prescribed for COVID-19 treatment12 and omicron peaked in January/February 2022.18 In OpenSAFELY, COVID-related admission was directly recorded in the COVID-19 therapeutics dataset, while in EAVE II admissions due to COVID-19 were identified as those occurring within 28 days of a positive Reverse Transcription Polymerase Chain Reaction (RT-PCR) test or those with an International Classification of Diseases, 10th Revision (ICD-10) code for COVID-19 (U07.1 and U07.2) in their admission record (in SMR01 and/or RAPID) as defined in previous studies.19\u003c/p\u003e\u003cp\u003e According to NHS clinical guidelines, to be eligible to receive an IL-6 inhibitor for treatment of COVID-19, COVID-19 patients should have been receiving dexamethasone (or an equivalent corticosteroid) unless contraindicated and requiring respiratory support (or having hypoxaemia with evidence of inflammation).10 Besides drug-specific hypersensitivity and slightly stricter requirements in terms of liver function and platelet count levels for sarilumab, the clinical eligibility criteria were the same across the two IL-6 treatments. People were excluded if they had (1)\u0026thinsp;\u0026lt;\u0026thinsp;18 or \u0026ge;\u0026thinsp;110 years of age, (2) missing information on sex or region, or (3) recorded as having received tocilizumab and sarilumab on the same date.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eInterventions of interest\u003c/h2\u003e\u003cp\u003eThe interventions of interest were the first record of treatment with tocilizumab or sarilumab between July 1, 2021 and February 28, 2022. In OpenSAFELY, this was ascertained from the COVID-19 therapeutics dataset based on the national Blueteq system, while in EAVE II this was ascertained from HEPMA as defined in prior research.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOutcomes and follow-up\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was all-cause mortality within 28 days after treatment initiation, extracted from the ONS mortality database in OpenSAFELY and from the NRS mortality database in EAVE II. Secondary outcomes included 90-day all-cause mortality and time to hospital discharge since treatment initiation. The date of tocilizumab or sarilumab prescription was defined as the baseline date. People were then followed from their baseline date until the earliest of either reaching the outcome, death (when analyzing time to hospital discharge), or end of the follow-up period.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eTo mimic the randomisation of the target trial, we assumed randomisation conditional on the following baseline covariates: age (restricted cubic splines), sex, NHS region, calendar time (restricted cubic splines), ethnicity (grouped into five broad categories: White, Black or Black British, Asian or Asian British, Mixed, Other), (Scottish) Index of Multiple Deprivation ([(S)IMD], as quintiles derived from the patient\u0026rsquo;s postcode at lower super output area level), COVID-19 vaccination status (unvaccinated, one vaccination, two vaccinations, or three or more), SARS-CoV-2 re-infection status (positive test or clinical diagnosis code or exposure to COVID-19 drug at least three months prior), BMI, most recent record, grouped into \u0026lt;\u0026thinsp;25.0, 25.0-\u0026lt;30.0, 30.0-\u0026lt;35, and \u0026gt;\u0026thinsp;=\u0026thinsp;35.0 kg/m2), previous use of other COVID-19 treatments (remdesivir, casirivimab/imdevimab and sotrovimab), diabetes, hypertension, chronic heart diseases, chronic respiratory diseases, moderate/severe renal disease, severe liver disease, solid cancer, hematological disease, immunosuppressive disease or treatment, and solid organ transplant. These covariates of interest, potentially prognostic for treatment initiation and outcome, were identified through literature review and discussions with domain experts. Comorbidities were identified through SNOMED CT codes in primary care records and ICD-10 in secondary care records. Ethnicity was identified through SNOMED CT codes and supplemented with information from secondary care records. Individuals with missing BMI, ethnicity, and (S)IMD were included with a missing indicator to maximise power, but alternative assumptions were tested in sensitivity analyses. Absence of recorded codes in terms of comorbidities, vaccination, reinfection, and prior COVID-19 treatment was assumed as not having such an event. Considering the sample size and data availability in the EAVE II database, the following baseline covariates were excluded for the EAVE II trial emulation: SARS-CoV-2 re-infection status, previous use of other COVID-19 treatments, solid cancer, immunosuppressive disease or treatment, and solid organ transplant.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eCox proportional hazards models, with follow-up time as the time scale, were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between treatment and each outcome in turn, adjusted for the above-mentioned covariates. In the OpenSAFELY analysis, the Cox model was stratified by NHS region to account for the geographical heterogeneity in COVID-19 outcomes. For the secondary outcome \u0026lsquo;time to discharge within 28 days\u0026rsquo;, we assumed death as a competing risk, censored them and assumed the worst case (i.e., no discharge until end of 28-day follow-up) - as recommended and applied in corresponding trial analyses.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWe conducted several sensitivity analyses: (1) We explored a reduced set of baseline covariates for the conditional randomisation (age, sex, calendar time, ethnicity, IMD, COVID-19 vaccination status and SARS-CoV-2 re-infection status) and a minimal set (age, sex and calendar time). (2) We extended the conditional randomisation to additionally include rural/urban area, days between last COVID-19 vaccination and treatment initiation, and days between hospital admission and treatment initiation. (3) We used a propensity score weighted Cox model with robust variance estimators to mimic the randomisation instead of using covariate adjustment. The propensity score was derived from a logistic regression modelling the conditional probability of being treated with tocilizumab based on all baseline covariates. We conducted a covariate balance check after weighting using standardised mean differences between the two groups and a threshold of \u0026lt;\u0026thinsp;0.10 as the indicator for being balanced. (4) To explore the impact of missing data in IMD, BMI and ethnicity, we conducted multiple imputation using chained equations techniques.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e (5) We assessed the association on COVID-19 related deaths only. These deaths were defined as a death whereby the underlying or contributory cause on the death certificate (ONS mortality database) was COVID-19 (ICD-10 codes U07.1, U07.2).\u003c/p\u003e\u003cp\u003eWe conducted several subgroup analyses to assess potential effect modifications: Dominant circulating variant of concern (delta variant before December 6, 2021, versus omicron BA.1 variant thereafter), COVID-19 vaccination status (none versus one or more vaccinations), age group (below 60 vs 60 years and above), sex (female versus male), ethnicity (white versus non-white), BMI (below 30 versus 30 or above), and presence versus absence of comorbidities (solid cancer, hematological disease, immunosuppressive treatment, diabetes, hypertension, chronic cardiac disease, and chronic respiratory disease). Effect modifications were tested using likelihood ratio tests, with Bonferroni correction applied to address multiple testing.\u003c/p\u003e\u003cp\u003eWhen presenting the results from the OpenSAFELY database, all counts of 7 or below were redacted, the counts above 7 were rounded to the nearest five to minimise potential disclosure. Data management was performed using Python 3.10, with analysis carried out using Stata 16.1 and R v4.5.0. Code for data management and analysis in OpenSAFELY, the codelists and the pre-specified protocol, are archived online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/opensafely/tocilizumab_and_sarilumab/tree/main\u003c/span\u003e\u003cspan address=\"https://github.com/opensafely/tocilizumab_and_sarilumab/tree/main\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and all public logs of analysis activity are available online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jobs.opensafely.org/effectiveness-and-safety-of-covid-19-treatments-for-hospitalised-patients/tocilizumab_sarilumab/releases/\u003c/span\u003e\u003cspan address=\"https://jobs.opensafely.org/effectiveness-and-safety-of-covid-19-treatments-for-hospitalised-patients/tocilizumab_sarilumab/releases/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eOpenSAFELY: We are very grateful for all the support received from the TPP Technical Operations team throughout this work, and for generous assistance from the information governance and database teams at NHS England and the NHS England Transformation Directorate.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eInformation governance and ethical approval\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eOpenSAFELY: NHS England is the data controller of the NHS England OpenSAFELY COVID-19 Service; TPP is the data processor; all study authors using OpenSAFELY have the approval of NHS England\u003ca href=\"https://www.zotero.org/google-docs/?NvXhXC\"\u003e\u003csup\u003e24\u003c/sup\u003e\u003c/a\u003e. This implementation of OpenSAFELY is hosted within the TPP environment which is accredited to the ISO 27001 information security standard and is NHS IG Toolkit compliant\u003ca href=\"https://www.zotero.org/google-docs/?JytG38\"\u003e\u003csup\u003e25\u003c/sup\u003e\u003c/a\u003e. Patient data has been pseudonymised for analysis and linkage using industry standard cryptographic hashing techniques; all pseudonymised datasets transmitted for linkage onto OpenSAFELY are encrypted; access to the NHS England OpenSAFELY COVID-19 service is via a virtual private network (VPN) connection; the researchers hold contracts with NHS England and only access the platform to initiate database queries and statistical models; all database activity is logged; only aggregate statistical outputs leave the platform environment following best practice for anonymisation of results such as statistical disclosure control for low cell counts\u003ca href=\"https://www.zotero.org/google-docs/?esbKUJ\"\u003e\u003csup\u003e26\u003c/sup\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThe service adheres to the obligations of the UK General Data Protection Regulation (UK GDPR) and the Data Protection Act 2018. The service previously operated under notices initially issued in February 2020 by the the Secretary of State under Regulation 3(4) of the Health Service (Control of Patient Information) Regulations 2002 (COPI Regulations), which required organisations to process confidential patient information for COVID-19 purposes; this set aside the requirement for patient consent\u003ca href=\"https://www.zotero.org/google-docs/?evpQ12\"\u003e\u003csup\u003e27\u003c/sup\u003e\u003c/a\u003e. As of 1 July 2023, the Secretary of State has requested that NHS England continue to operate the Service under the COVID-19 Directions 2020\u003ca href=\"https://www.zotero.org/google-docs/?uEzEeq\"\u003e\u003csup\u003e28\u003c/sup\u003e\u003c/a\u003e. In some cases of data sharing, the common law duty of confidence is met using, for example, patient consent or support from the Health Research Authority Confidentiality Advisory Group\u003ca href=\"https://www.zotero.org/google-docs/?0LlxPN\"\u003e\u003csup\u003e29\u003c/sup\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eTaken together, these provide the legal bases to link patient datasets using the service. GP practices, which provide access to the primary care data, are required to share relevant health information to support the public health response to the pandemic, and have been informed of how the service operates. This study was approved by the Health Research Authority (REC reference 20/LO/0651) and by the LSHTM Ethics Board (reference 21863).\u003c/p\u003e\n\u003cp\u003eEAVE II: Data are held by Public Health Scotland and were made available under the Public Health Scotland COVID-19 data governance framework. As such, individual-level patient data are not publicly available. Access to data was granted following appropriate ethical and governance approvals. Ethical approval for EAVE II was obtained from the National Research Ethics Service Committee, Southeast Scotland 02 (reference number: 12/SS/0201), and the Public Benefit and Privacy Panel for Health and Social Care (reference number: 1920-0279)\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe OpenSAFELY platform is principally funded by grants from: NHS England [2023-2025]; The Wellcome Trust (222097/Z/20/Z) [2020-2024]; MRC (MR/V015737/1) [2020-2021]. Additional contributions to OpenSAFELY have been funded by grants from: MRC via the National Core Study programme, Longitudinal Health and Wellbeing strand (MC_PC_20030, MC_PC_20059) [2020-2022] and the Data and Connectivity strand (MC_PC_20058) [2021-2022]; NIHR and MRC via the CONVALESCENCE programme (COV-LT-0009, MC_PC_20051) [2021-2024]; NHS England via the Primary Care Medicines Analytics Unit [2021-2024]. This work is independent research jointly funded by the NIHR and UKRI [Characterisation, determinants, mechanisms and consequences of the long-term effects of COVID-19: providing the evidence base for health care services, COV-LT-0009]. This study was also supported by the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under grant agreement No 101015736. The views expressed in this publication are those of the author(s) and not necessarily those of NIHR, the Department of Health and Social Care, UKRI, NHS England, UK Health Security Agency (UKHSA), or other funders. Funders had no role in the study design, collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEAVE II was funded by the NIHR Health Technology Assessment Programme [project number 13/34/14], the MRC [MR/R008345/1], and HDR UK through the Data and Connectivity National Core Study. The project has also received support from the Scottish Government and BREATHE \u0026ndash; the Health Data Research Hub for Respiratory Health, part of HDR UK.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eBZ, LT and AS conceived the idea of this study. BZ, AK and CR were responsible for the data analysis with input from AA, AS and LT. AA and BZ drafted the original version of the manuscript with input from AS, LT, AK and CR. AG, BMK, AM, SB and BG provided administrative and technical support. All authors contributed to the critical review of the manuscript and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eLT is funded by an NIHR Research Professorship NIHR302405. AA receives his salary from the postdoc mobility grant of the Swiss National Science Foundation (#P500PM_221961). BG has received research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the NHS NIHR, the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he has previously been a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data. AM has represented the RCGP in the health informatics group and the Profession Advisory Group that advises on access to GP Data for Pandemic Planning and Research (GDPPR); the latter was a paid role. AM is a former employee and interim Chief Medical Officer of NHS Digital. AM has consulted for health care vendors, the last time in 2022; the companies consulted in the last 3 years have no relationship to OpenSAFELY.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and public involvement and engagement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpenSAFELY: OpenSAFELY has involved patients and the public in various ways: we developed a public website that provides a detailed description of the platform in language suitable for a lay audience (https://opensafely.org); we have participated in two citizen juries exploring public trust in OpenSAFELY; we have co-developed an explainer video (https://www.opensafely.org/about/); we have patient representation who are experts by experience on our OpenSAFELY Oversight Board; we have partnered with Understanding Patient Data to produce lay explainers on the importance of large datasets for research; we have presented at various online public engagement events to key communities (e.g., Healthcare Excellence Through Technology; Faculty of Clinical Informatics annual conference; NHS Assembly; HDRUK symposium); and more. To ensure the patient voice is represented, we are working closely to decide on language choices with appropriate medical research charities (e.g., Association of Medical Research Charities). We will share information and interpretation of our findings through press releases, social media channels, and plain language summaries.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData access and verification\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eOpenSAFELY: Access to the underlying identifiable and potentially re-identifiable pseudonymised electronic health record data is tightly governed by various legislative and regulatory frameworks, and restricted by best practice. The data in the NHS England OpenSAFELY COVID-19 service is drawn from General Practice data across England where TPP is the data processor.\u003c/p\u003e\n\u003cp\u003eTPP developers initiate an automated process to create pseudonymised records in the core OpenSAFELY database, which are copies of key structured data tables in the identifiable records. These pseudonymised records are linked onto key external data resources that have also been pseudonymised via SHA-512 one-way hashing of NHS numbers using a shared salt. University of Oxford, Bennett Institute for Applied Data Science developers and PIs, who hold contracts with NHS England, have access to the OpenSAFELY pseudonymised data tables to develop the OpenSAFELY tools.\u003cbr\u003e\u0026nbsp;These tools in turn enable researchers with OpenSAFELY data access agreements to write and execute code for data management and data analysis without direct access to the underlying raw pseudonymised patient data, and to review the outputs of this code. All code for the full data management pipeline \u0026mdash; from raw data to completed results for this analysis \u0026mdash; and for the OpenSAFELY platform as a whole is available for review at https://github.com/OpenSAFELY.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group, Shankar-Hari M, Vale CL, Godolphin PJ, Fisher D, Higgins JPT et al (2021) Association Between Administration of IL-6 Antagonists and Mortality Among Patients Hospitalized for COVID-19: A Meta-analysis. JAMA 326(6):499\u0026ndash;518\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan der Schoels MM, Breedveld FC, Burmester GR, Dougados M, Emery P et al (2013) Blocking the effects of interleukin-6 in rheumatoid arthritis and other inflammatory rheumatic diseases: systematic literature review and meta-analysis informing a consensus statement. Ann Rheum Dis 72(4):583\u0026ndash;589\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcElvaney OJ, Curley GF, Rose-John S, McElvaney NG (2021) Interleukin-6: obstacles to targeting a complex cytokine in critical illness. Lancet Respir Med 9(6):643\u0026ndash;654\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhosn LCA, Evrenoglou T, Davidson M, Gra\u0026ntilde;a C, Schmucker C, Bollig C, Henschke N, Sguassero Y, Nejstgaard CH, Menon S, Van Nguyen T, Ferrand G, Kapp P, Riveros C, \u0026Aacute;vila C, Devane D, Meerpohl JJ, Rada G, Ravaud P (2021) Boutron, Isabelle. Interleukin-6 blocking agents for treating COVID-19: a living systematic review. Cochrane Database Syst Reviews 2021(3):NA\u0026ndash;NA Hr\u0026oacute;bjartsson, Asbj\u0026oslash;rn; Grasselli, Giacomo; Tovey, David\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClinical management of COVID-19 Living guideline, 18 August 2023 [Internet]. [cited 2025 Apr 30]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2023.2\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2023.2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeraatkar D, Cusano E, Mart\u0026iacute;nez JPD, Qasim A, Mangala S, Kum E et al (2022) Use of tocilizumab and sarilumab alone or in combination with corticosteroids for covid-19: systematic review and network meta-analysis. BMJ Med 1(1):e000036\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGodolphin PJ, Fisher DJ, Berry LR, Derde LPG, Diaz JV, Gordon AC et al (2022) Association between tocilizumab, sarilumab and all-cause mortality at 28 days in hospitalised patients with COVID-19: A network meta-analysis. PLoS ONE 17(7):e0270668\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDerde L, Gordon AC, Mouncey PR, Al-Beidh F, Rowan KM, Nichol AD et al (2025) Tocilizumab, sarilumab and anakinra in critically ill patients with COVID-19: a randomised, controlled, open-label, adaptive platform trial. Thorax 80(8):530\u0026ndash;539\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCOVID-19 rapid evidence summary Sarilumab for COVID-19 | Advice | NICE [Internet]. NICE; 2021 [cited 2025 Apr 30]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nice.org.uk/advice/es34\u003c/span\u003e\u003cspan address=\"https://www.nice.org.uk/advice/es34\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMessaging CMO NHS clinical guidance for: Interleukin-6 inhibitors (tocilizumab or sarilumab) for adult patients hospitalised due to COVID-19. This version was issued on 29-Nov-2022 08:29:29 [Internet]. [cited 2025 Apr 30]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cas.mhra.gov.uk/ViewandAcknowledgment/ViewAlert.aspx?AlertID=103222\u003c/span\u003e\u003cspan address=\"https://www.cas.mhra.gov.uk/ViewandAcknowledgment/ViewAlert.aspx?AlertID=103222\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUpdate information | COVID-19 rapid guideline: managing COVID-19 | Guidance | NICE [Internet]. NICE (2021) [cited 2025 Apr 30]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nice.org.uk/guidance/ng191/chapter/update-information\u003c/span\u003e\u003cspan address=\"https://www.nice.org.uk/guidance/ng191/chapter/update-information\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStatistics Statistics \u0026raquo; COVID-19 Therapeutics (antivirals, neutralising monoclonal antibodies and interleukin 6 inhibitors) [Internet]. [cited 2025 Aug 7]. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://usher.ed.ac.uk/eave-ii/key-outputs/our-publications\u003c/span\u003e\u003cspan address=\"https://usher.ed.ac.uk/eave-ii/key-outputs/our-publications\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOpenSAFELY Research Highlights | Bennett Institute for Applied Data Science [Internet] 2023 [cited 2025 Aug 7]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bennett.ox.ac.uk/blog/2023/03/opensafely-research-highlights/\u003c/span\u003e\u003cspan address=\"https://www.bennett.ox.ac.uk/blog/2023/03/opensafely-research-highlights/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHern\u0026aacute;n MA, Sauer BC, Hern\u0026aacute;ndez-D\u0026iacute;az S, Platt R, Shrier I (2016) Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol 79:70\u0026ndash;75\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimpson CR, Robertson C, Vasileiou E, McMenamin J, Gunson R, Ritchie LD et al (2020) Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II): protocol for an observational study using linked Scottish national data. BMJ Open 10(6):e039097\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLangan SM, Schmidt SA, Wing K, Ehrenstein V, Nicholls SG, Filion KB et al (2018) The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology (RECORD-PE). BMJ 363:k3532\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChadeau-Hyam M, Tang D, Eales O, Bodinier B, Wang H, Jonnerby J et al Omicron SARS-CoV-2 epidemic in England during February 2022: A series of cross-sectional community surveys. The Lancet Regional Health \u0026ndash; Europe [Internet]. 2022 Oct 1 [cited 2025 Aug 7];21. 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Lancet 401(10371):101\u0026ndash;102\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Gleason KJ, Hu Y, Lovell SS, Mukhopadhyay S, Wang L et al (2022) Handling death as an intercurrent event in time to recovery analysis in COVID-19 treatment clinical trials. Contemp Clin Trials 119:106758\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmstutz A, Speich B, Mentr\u0026eacute; F, Rueegg CS, Belhadi D, Assoumou L et al (2023) Effects of remdesivir in patients hospitalised with COVID-19: a systematic review and individual patient data meta-analysis of randomised controlled trials. Lancet Respiratory Med 11(5):453\u0026ndash;464\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhite IR, Royston P, Wood AM (2011) Multiple imputation using chained equations: Issues and guidance for practice. Stat Med 30(4):377\u0026ndash;399\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe NHS, England OpenSAFELY COVID-19 service - privacy notice [Internet]. NHS England Digital. [cited 2025 Apr 30]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://digital.nhs.uk/coronavirus/coronavirus-covid-19-response-information-governance-hub/the-nhs-england-opensafely-covid-19-service-privacy-notice\u003c/span\u003e\u003cspan address=\"https://digital.nhs.uk/coronavirus/coronavirus-covid-19-response-information-governance-hub/the-nhs-england-opensafely-covid-19-service-privacy-notice\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eData Security and Protection Toolkit [Internet]. [cited 2025 Apr 30]. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hra.nhs.uk/about-us/committees-and-services/confidentiality-advisory-group/\u003c/span\u003e\u003cspan address=\"https://www.hra.nhs.uk/about-us/committees-and-services/confidentiality-advisory-group/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"tocilizumab, sarilumab, COVID-19, target trial emulation, comparative effectiveness","lastPublishedDoi":"10.21203/rs.3.rs-7702834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7702834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe interleukin-6 (IL-6) inhibitors tocilizumab and sarilumab have been repurposed for COVID-19 treatment. However, discrepancies still exist across global and national COVID-19 guidelines, with limited data on the comparative effectiveness between these therapeutics especially during the delta/omicron periods. With the approval of NHS England and Public Health Scotland, we compared their effectiveness among adults hospitalised with COVID-19 using electronic health records data through the OpenSAFELY-TPP (England) and EAVE II (Scotland) platforms. Following the target trial emulation framework, 10,487 patients treated between July 2021 and February 2022, when both drugs were frequently prescribed, were included. In England, 1150 (20.1%) of 5710 participants receiving tocilizumab died by day 28 compared with 820 (20.4%) of 4025 participants receiving sarilumab (adjusted hazard ratio [aHR] 1.07, 95% CI 0.96\u0026ndash;1.19). In Scotland, 114 (29.4%) of 388 participants receiving tocilizumab died by day 28 compared with 97 (27.0%) of 359 participants receiving sarilumab (aHR 0.92, 95% CI 0.68\u0026ndash;1.23). There was no evidence of a difference in time to hospital discharge between the groups, and no credible effect modification by variant of concern, vaccination status, age, sex, ethnicity, body mass index, or comorbidities. Our findings provide supportive evidence for both drugs as alternative therapeutic options in COVID-19 in-patient management.\u003c/p\u003e","manuscriptTitle":"Tocilizumab versus sarilumab among adults hospitalised with COVID-19: target trial emulation across England and Scotland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 10:14:02","doi":"10.21203/rs.3.rs-7702834/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"20676b8e-1fe9-427b-92cc-0431a8bb078b","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55582422,"name":"Health sciences/Diseases/Infectious diseases/Viral infection"},{"id":55582423,"name":"Health sciences/Health care/Therapeutics"},{"id":55582424,"name":"Health sciences/Signs and symptoms/Respiratory signs and symptoms"}],"tags":[],"updatedAt":"2026-04-27T09:30:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 10:14:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7702834","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7702834","identity":"rs-7702834","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}