Evolution of the Use of Corticosteroids for the Treatment of Hospitalised COVID-19 Patients in Spain between March and November 2020 SEMI-COVID National Registry

Journal of Clinical Medicine Article Evolution of the Use of Corticosteroids for the Treatment of Hospitalised COVID-19 Patients in Spain between March and November 2020: SEMI-COVID National Registry David Balaz 1,2,*, Philip Erick Wikman-Jorgensen 1,2,*, Vicente Giner Galvañ 1,2,3,*, Manuel Rubio-Rivas 4, Borja de Miguel Campo 5, Mariam Noureddine López 6, Juan Francisco López Caleya 7, Ricardo Gómez Huelgas 8, Paula María Pesqueira Fontán9, Manuel Méndez Bailón10, Mar Fernández-Garcés11, Ana Fernández Cruz 12 , Gema María García García13, Nicolás Rhyman 14, Luis Corral-Gudino 15 , Aquiles Lozano Rodríguez-Mancheño 16, María Navarro De La Chica 17, Andrea Torregrosa García18, JoséNicolás Alcalá19, Pablo Díaz Jiménez 20, Leticia Esther Royo Trallero 21, Pere Comas Casanova 22, Jesús Millán Núñez-Cortés23, José-Manuel Casas-Rojo 24 and on behalf of the SEMI-COVID-19 Network †   Citation: Balaz, D.; Wikman- Jorgensen, P.E.; Galvañ, V.G.; Rubio-Rivas, M.; de Miguel Campo, B.; López, M.N.; Caleya, J.F.L.; Huelgas, R.G.; Fontán, P.M.P.; Bailón, M.M.; et al. Evolution of the Use of Corticosteroids for the Treatment of Hospitalised COVID-19 Patients in Spain between March and November 2020: SEMI-COVID National Registry. J. Clin. Med. 2021,10, 4610. https:// doi.org/10.3390/jcm10194610 Academic Editor: Nicola Petrosillo Received: 3 September 2021 Accepted: 4 October 2021 Published: 8 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Internal Medicine, Hospital Universitario San Juan de Alicante, 03550 Alicante, Spain 2Department of Clinical Medicine, Faculty of Medicine, Miguel Hernández University, 03202 Alicante, Spain 3 Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO), Conselleria de Sanitat, 46010 Valencia, Spain 4Department of Internal Medicine, Hospital Universitario de Bellvitge, 08907 Barcelona, Spain; [email protected] 5Department of Internal Medicine, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain; [email protected] 6Department of Internal Medicine, Hospital Costa del Sol, 29603 Málaga, Spain; [email protected] 7 Department of Internal Medicine, Hospital de Cabueñes, 33394 Gijón, Asturias, Spain; [email protected] 8Department of Internal Medicine, Hospital Regional Universitario de Málaga, 29010 Málaga, Spain; [email protected] 9Department of Internal Medicine, Hospital Clínico de Santiago de Compostela, 15706 A Coruña, Spain; [email protected] 10 Department of Internal Medicine, Hospital Clínico San Carlos, 28040 Madrid, Spain; [email protected] 11 Department of Internal Medicine, Hospital Universitario Dr. Peset, 46017 Valencia, Spain; mar[email protected] 12 Department of Internal Medicine, Hospital Universitario Puerta de Hierro-Majadahonda, 28222 Madrid, Spain; [email protected] 13 Department of Internal Medicine, Complejo Hospitalario Universitario de Badajoz, 06010 Badajoz, Spain; [email protected] 14 Department of Internal Medicine, Hospital Moisès Broggi, Sant Joan Despí, 08970 Barcelona, Spain; [email protected] 15 Department of Internal Medicine, Hospital Universitario Río Hortega, 47012 Valladolid, Spain; [email protected] 16 Department of Internal Medicine, Hospital Alto Guadalquivir, 23740 Jaén, Spain; aqlozanor[email protected] 17 Department of Internal Medicine, Hospital Nuestra Señora del Prado, 45600 Toledo, Spain; [email protected] 18 Department of Internal Medicine, Hospital General Universitario de Elda, 03600 Alicante, Spain; [email protected] 19 Department of Internal Medicine, Hospital de Pozoblanco, 14400 Córdoba, Spain; [email protected] 20 Department of Internal Medicine, Hospital Universitario Virgen del Rocío, 41013 Sevilla, Spain; [email protected] 21 Department of Internal Medicine, Hospital General Defensa, 50009 Zaragoza, Spain; [email protected] 22 Department of Internal Medicine, Hospital Comarcal de Blanes, 17300 Girona, Spain; [email protected] 23 Department of Internal Medicine, Hospital Universitario Gregorio Marañón, 28007 Madrid, Spain; [email protected] 24 Department of Internal Medicine, Hospital Infanta Cristina University Hospital, 28981 Madrid, Spain; [email protected] *Correspondence: [email protected] (D.B.); [email protected] (P.E.W.-J.); [email protected] (V.G.G.) J. Clin. Med. 2021,10, 4610. https://doi.org/10.3390/jcm10194610 https://www.mdpi.com/journal/jcm J. Clin. Med. 2021,10, 4610 2 of 17 † A complete list of the SEMI-COVID-19 Network members is provided in the Appendix A. Abstract: Objectives: Since the results of the RECOVERY trial, WHO recommendations about the use of corticosteroids (CTs) in COVID-19 have changed. The aim of the study is to analyse the evolutive use of CTs in Spain during the pandemic to assess the potential influence of new recommendations. Material and methods: A retrospective, descriptive, and observational study was conducted on adults hospitalised due to COVID-19 in Spain who were included in the SEMI-COVID-19 Registry from March to November 2020. Results: CTs were used in 6053 (36.21%) of the included patients. The patients were older (mean (SD)) (69.6 (14.6) vs. 66.0 (16.8) years; p< 0.001), with hypertension (57.0% vs. 47.7%; p< 0.001), obesity (26.4% vs. 19.3%; p< 0.0001), and multimorbidity prevalence (20.6% vs. 16.1%; p< 0.001). These patients had higher values (mean (95% CI)) of C-reactive protein (CRP) (86 (32.7–160) vs. 49.3 (16–109) mg/dL; p< 0.001), ferritin (791 (393–1534) vs. 470 (236–996) µ g/dL; p< 0.001), D dimer (750 (430–1400) vs. 617 (345–1180) µ g/dL; p< 0.001), and lower Sp0 2 /Fi0 2 (266 (91.1) vs. 301 (101); p< 0.001). Since June 2020, there was an increment in the use of CTs (March vs. September; p< 0.001). Overall, 20% did not receive steroids, and 40% received less than 200 mg accumulated prednisone equivalent dose (APED). Severe patients are treated with higher doses. The mortality benefit was observed in patients with oxygen saturation </=90%. Conclusions: Patients with greater comorbidity, severity, and inflammatory markers were those treated with CTs. In severe patients, there is a trend towards the use of higher doses. The mortality benefit was observed in patients with oxygen saturation </=90%. Keywords: COVID-19; corticosteroids; Spain; comorbidities 1. Introduction Effective therapies for patients with coronavirus disease 2019 (COVID-19) are lacking. The role of corticosteroids (CTs) in the management of COVID-19 is of great interest. At the beginning of the pandemic, the use of CTs was contraindicated by several health authorities. The initial recommendation in March and May 2020 of the World Health Organisation (WHO) contraindicated the use of CTs for the management of COVID-19 [ 1 ]. Known data from previous studies on MERS, SARS, and influenza pointed to the absence of benefit, increased risk for secondary effects, and some concerns about prolonged virus clearance [2–5]. Data from the RECOVERY clinical trial published in July 2020 have revealed that lowdose dexamethasone reduced mortality by one-third in ventilated patients with COVID-19 at day 28, compared with patients treated with standard of care alone. Another endpoint was a decrease in mortality by one-fifth in patients receiving oxygen therapy; nonetheless, there was no benefit in patients not requiring respiratory support [6]. The CT beneficial effect is associated with modulation of the inflammatory response. The poor course of the disease is the consequence of an uncontrolled systemic autoinflammatory situation with the virus as the trigger of the so-called “cytokine storm”, an uncontrolled inflammatory response characterised by high levels of IL-1 β , IFN- γ , IL-10, and MCP1 that activates the T-helper 1 (Th1) cell response and causes severe lung damage [ 7 , 8 ]. Systemic inflammation takes the form of elevated C-reactive protein, ferritin, procalcitonin, and Il-6, and autopsy results reveal exudative and proliferative changes characteristic of diffuse alveolar damage, including an inflammatory infiltrate comprising macrophages and lymphocytes [ 9 ]. Due to these pathogenic mechanisms, prescribing CTs for the treatment of COVID-19 seems reasonable [ 10 , 11 ]. In September 2020, WHO published an updated document [ 12 ] recommending the use of CTs in patients with severe and critical COVID-19 infection and contraindicating their use in patients with mild-to- moderate infection. Similarly, the Infectious Diseases Society of America (IDSA) advised the use of CTs, preferring dexamethasone and methylprednisolone or prednisone, in hospitalised patients with severe COVID-19. The indiscriminate and routine use of CT in J. Clin. Med. 2021,10, 4610 3 of 17 patients with COVID 19 is not recommended [ 13 ] and continues to be discouraged in patients without hypoxemia [14]. Although recent studies showed a beneficial effect in severe and critical cases, the controversy is not over. The main reason is the high heterogeneity of existing studies, making a global analysis and interpretation of the evidence difficult. Spain has had one of the biggest incidences of infections by SARS-CoV-2 in the world since the outbreak of the pandemic. Although the use of CTs was generally not recommended in the first months of the pandemic, the initial favourable reports from China [ 15 ] caused the use of CTs in daily clinical practice from the beginning of the pandemic. Our aim was to analyse how CTs were used in Spain, using data from the SEMICOVID-19 Registry. We aimed to identify what type of patients were more likely to receive CTs, the dosage used, how CT use changed over time, and clinical outcomes (mortality). 2. Material and Methods We focused on common comorbidities, biochemical parameters, basic X-ray findings, clinical outcomes, dosage, and timing of corticosteroids in the evolution of pandemics. Blood sampling and basic analysis of chest X-rays were performed on the first day of hospital admission. 2.1. The SEMI-COVID-19 Registry The SEMI-COVID-19 Registry is an ongoing nationwide, multicentre, observational, retrospective cohort registry [ 16 ]. A total number of 150 hospitals through the 17 regions of Spain participate in the registry, thus ensuring a representative nationwide sample. All consecutive hospitalised patients with confirmed SARS-CoV-2 infection who were discharged or died were eligible for inclusion. The inclusion criteria were age ≥ 18 years and first admission to the hospital due to SARS-CoV-2 infection confirmed by reverse transcription–polymerase chain reaction (RT-PCR) testing of a nasopharyngeal sputum sample or bronchoalveolar lavage sample, through a positive result on serological test with a clinically compatible presentation, according to World Health Organisation (WHO) recommendations [ 17 ]. The exclusion criteria were subsequent admissions of the same patient and denial or withdrawal of informed consent. The admission and treatment of patients were at the discretion of the attending physicians based on their clinical judgment, local protocols, and the updated recommendations from the Spanish Ministry of Health. Personal data processing strictly complied with the applicable European Union and Spanish laws on biomedical research and personal data protection. The SEMI-COVID-19 Registry has been approved by the Provincial Research Ethics Committee of Málaga (Spain) as per the recommendation of the Spanish Agency of Medicines and Medical Products (AEMPS, for its initials in Spanish). All patients gave informed consent. When there were biosafety concerns and/or when the patient had already been discharged, verbal informed consent was requested and noted on the medical record. The conduct and reporting of the study were carried out pursuant to the STROBE statement guidelines [18]. 2.2. Procedures An online electronic data capture system (DCS) was developed for the SEMI-COVID- 19 Registry. After receiving training, at least one physician from the internal medicine department in each participating hospital was responsible for acquiring and entering the requested data into the DCS. This work was performed on a voluntary basis, and physicians received no remuneration for it. To ensure the quality of data collection, a database manager and data verification procedures were designed. The study’s scientific steering committee and an independent external agency performed database monitoring. Data analysis and logistics coordination were also carried out by independent external agencies. Alphanumeric sequences of characters were used as identification codes to pseudo-anonymise dissociated patient identifiable data; as such, the DCS did not contain J. Clin. Med. 2021,10, 4610 4 of 17 any direct patient identifiers. The database platform is hosted on a secure server, and all information is fully encrypted through a valid TLS certificate. Multiple variables were retrospectively collected under various headings, which included the following: (1) inclusion criteria, (2) epidemiological data, (3) RT-PCR and SARS-CoV-2 serology data, (4) personal medical and medication history, (5) symptoms and physical examination findings at admission, (6) laboratory (blood gases, metabolic panel, complete blood count, coagulation) and diagnostic imaging tests, (7) additional data at seven days after hospital admission or at admission to the ICU, (8) pharmacological treatment during the hospitalisation and ventilator support, (9) complications during the hospitalisation, and (10) progress after discharge and/or 30 days from diagnosis. The Charlson Comorbidity Index was calculated from the collected data [19]. 2.3. Statistical Analysis Quantitative variables are expressed as medians (interquartile range) for not normally distributed variables or means (standard deviation) for normally distributed variables. Categorical variables are expressed as absolute values and percentages. Categorical variables were compared using the chi-square test and quantitative variables using Student’s t-test for variables with a normal distribution and the Mann-Whitney test for non-parametric variables. The alpha significance level was established as 0.05. Analysis of time-dependent variables was carried out by visual inspections of point plots with 95% CI with an overlying smooth curve of conditional means with standard error shadow computed with the LOESS method. A stratified logistic regression was performed to assess the impact of corticosteroids on mortality. The analysis was conducted with R statistical software. 3. Results In total, 16,717 hospitalised patients due to COVID-19 in Spain were included in the SEMI-COVID-19 registry (Figure 1). In this retrospective cohort study, data from February to November of 2020 were analysed. Corticosteroids were administrated in an important part of hospitalised patients adding to a standard-of-care therapy. A total of 6053 (36.21%) patients received treatment with corticosteroids. We analysed 9595 (57.4%) male and 7122 (42.6%) female patients. J. Clin. Med. 2021, 10, x FOR PEER REVIEW 5 of 18 Figure 1. Patient selection flowchart. 3.1. Comorbidities Among all 16,717 enrolled patients in our cohort, the most frequent comorbidities were: high blood pressure (HBP) n = 8542 (51.1%), dyslipidaemia (DLP) n = 6612 (39.59%), obesity n = 3359 (20.09%), diabetes mellitus with no end organ damage (DMnEOD) n = 2416 (14.45%) and atrial fibrillation (AF) n = 1840 (11.01%). Asthma was the most common respiratory disease, n = 1185 (7.09%). More comorbidities in our cohort are summarised in Table 1. Table 1. Basic characteristics and comorbidities in the cohort. Variable Total Cohort CT nCT p-Value Participants, n (%) 16,717 6053 (36.21) 10,664 (63.79) <0.001 Age (years), Mean (SD) 67.33 (16.13) 69.65 (14.58) 66.01 (16.80) <0.001 Men, n (%) 9595 (57.39) 3840 (63.44) 5755 (54.07) <0.001 HBP, n (%) 8542 (51.1) 3450 (57) 5092 (47.75) <0.001 DMnEOD, n (%) 2416 (14.45) 983 (16.24) 1433 (13.44) <0.001 DMEOD, n (%) 896 (5.36) 363 (6) 533 (5) 0.0071 DLP, n (%) 6612 (39.55) 2601 (43.02) 4011 (37.64) <0.001 Alcohol abuse, n (%) 751 (4.62) 313 (5.35) 438 (4.22) <0.001 Smoking, n (%) Never 11,137 (69.49) 3765 (65.12) 533 (5.20) Ex-smoker 4053 (25.29) 1714 (29.64) 2339 (22.83) <0.001 Smoker 835 (5.2) 302 (5.22) 7372 (71.96) AF, n (%) 1840 (11.01) 711 (11.76) 1129 (10.59) 0.022 Anxiety disorder, n (%) 1292 (7.74) 486 (8.05) 806 (7.57) 0.273 Depression, n (%) 1757 (10.5) 669 (11.09) 1083 (10.17) 0.069 Obesity, n (%) 3359 (20.09) 1463 (26.37) 1896 (19.33) <0.001 Neurodegenerative disease, n (%) 1484 (8.88) 540 (8.92) 944 (8.85) 0.899 Angina, n (%) 591 (3.53) 258 (4.26) 333 (3.12) 0.001 HF, n (%) 1187 (7.1) 491 (8.11) 696 (6.53) 0.001 COPD, n (%) 1132 (6.77) 585 (9.67) 547 (5.13) <0.001 Asthma, n (%) 1185 (7.09) 474 (7.8) 711 (6.67) 0.006 TIA, n (%) 789 (4.72) 322 (5.32) 467 (4.38) 0.007 Stroke, n (%) 462 (2.76) 177 (2.92) 285 (2.67) 0.372 Figure 1. Patient selection flowchart. 3.1. Comorbidities Among all 16,717 enrolled patients in our cohort, the most frequent comorbidities were: high blood pressure (HBP) n= 8542 (51.1%), dyslipidaemia (DLP) n= 6612 (39.59%), J. Clin. Med. 2021,10, 4610 5 of 17 obesity n= 3359 (20.09%), diabetes mellitus with no end organ damage (DMnEOD) n= 2416 (14.45%) and atrial fibrillation (AF) n= 1840 (11.01%). Asthma was the most common respiratory disease, n= 1185 (7.09%). More comorbidities in our cohort are summarised in Table 1. Table 1. Basic characteristics and comorbidities in the cohort. Variable Total Cohort CT nCT p-Value Participants, n(%) 16,717 6053 (36.21) 10,664 (63.79) <0.001 Age (years), Mean (SD) 67.33 (16.13) 69.65 (14.58) 66.01 (16.80) <0.001 Men, n(%) 9595 (57.39) 3840 (63.44) 5755 (54.07) <0.001 HBP, n(%) 8542 (51.1) 3450 (57) 5092 (47.75) <0.001 DMnEOD, n(%) 2416 (14.45) 983 (16.24) 1433 (13.44) <0.001 DMEOD, n(%) 896 (5.36) 363 (6) 533 (5) 0.0071 DLP, n(%) 6612 (39.55) 2601 (43.02) 4011 (37.64) <0.001 Alcohol abuse, n(%) 751 (4.62) 313 (5.35) 438 (4.22) <0.001 Smoking, n(%) Never 11,137 (69.49) 3765 (65.12) 533 (5.20) Ex-smoker 4053 (25.29) 1714 (29.64) 2339 (22.83) <0.001 Smoker 835 (5.2) 302 (5.22) 7372 (71.96) AF, n(%) 1840 (11.01) 711 (11.76) 1129 (10.59) 0.022 Anxiety disorder, n(%) 1292 (7.74) 486 (8.05) 806 (7.57) 0.273 Depression, n(%) 1757 (10.5) 669 (11.09) 1083 (10.17) 0.069 Obesity, n(%) 3359 (20.09) 1463 (26.37) 1896 (19.33) <0.001 Neurodegenerative disease, n(%) 1484 (8.88) 540 (8.92) 944 (8.85) 0.899 Angina, n(%) 591 (3.53) 258 (4.26) 333 (3.12) 0.001 HF, n(%) 1187 (7.1) 491 (8.11) 696 (6.53) 0.001 COPD, n(%) 1132 (6.77) 585 (9.67) 547 (5.13) <0.001 Asthma, n(%) 1185 (7.09) 474 (7.8) 711 (6.67) 0.006 TIA, n(%) 789 (4.72) 322 (5.32) 467 (4.38) 0.007 Stroke, n(%) 462 (2.76) 177 (2.92) 285 (2.67) 0.372 Hemiplegia, n(%) 272 (1.62) 165 (1.54) 107 (1.76) 0.310 Dementia, n(%) 1659 (9.9) 559 (9.24) 1100 (10.3) 0.03 PAD, n(%) 765 (4.58) 315 (5.21) 450 (4.23) 0.004 Cirrhosis, n(%) 175 (1.04) 72 (1.19) 103 (0.97) 0.19 CKD, n(%) 1008 (6.18) 440 (7.28) 568 (5.33) <0.001 Active Neoplasm, n(%) 1034 (6.18) 405 (6.69) 629 (5.9) 0.05 Leukaemia, n(%) 194 (1.1) 96 (1.59) 98 (0.92) <0.001 Lymphoma n(%) 220 (1.3) 95 (1.57) 125 (1.17) 0.04 CTD, n(%) 416 (2.49) 204 (3.37) 212 (1.99) <0.001 Lupus, n(%) 31 (0.1) 19 (0.31) 12 (0.11) <0.001 Rheumatoid arthritis, n(%) 240 (1.43) 118 (1.95) 128 (1.2) <0.001 AIDS, n(%) 51 (3.05) 28 (0.46) 23 (0.22) 0.009 AHS, n(%) 575 (6.16) 451 (7.5) 555 (5.2) <0.001 CCI > 3, n(%) 2899 (17.73) 1225 (20.6) 1674 (16.1) <0.001 HBP: high blood pressure; DMnEOD: diabetes mellitus no end-organ damage; DMEOD: diabetes mellitus end-organ damage; DLP: dyslipidaemia; AF: atrial fibrillation; HF: heart failure; COPD: chronic obstructive pulmonary disease; TIA: transient ischemic attack; PAD: peripheral artery disease; CKD: chronic kidney disease; CTD: connective tissue disease; AHS: apnoea hypopnea syndrome; CCI: Charlson Comorbidity Index; AIDS: acquired immune deficiency syndrome In addition, we found that the patients with comorbidities including high blood pressure (HBP), dyslipidaemia (DLP), diabetes mellitus no end-organ damage (DMnEOD), diabetes mellitus with end-organ damage (DMEOD), obesity, stable angina, heart failure (HF), chronic obstructive pulmonary disease (COPD), asthma, apnoea hypopnea syndrome (AHS), transient ischemic attack (TIA), dementia, periphery arterial disease (PAD), chronic kidney disease (CKD), leukaemia, lymphoma, connective tissue disease, and AIDS received CTs significantly more often than patients without these comorbidities. There was no significant difference among the groups according to the presence of depression, anxiety disorder, dementia, stroke, cirrhosis, active neoplasm, and AF. J. Clin. Med. 2021,10, 4610 6 of 17 3.2. Biochemical and Gasometrical Parameters The patients in the CT group had lower oxygen saturation measured by pulse oximetry than the non-CT group at the time of hospital admission (91.3% ± 6.68 vs. 93.8% ± 5.31, p< 0.001). Moreover, there were significantly more patients with lower than 93% oxygen saturation at time of admission in the CT group (n= 2812 (47.6%) vs. n= 2778 (26.8%), p< 0.001) . In general, comparing basic gasometric parameters between two branches, there were significant differences in pO2, pCO2, PaO2/FiO2. We observed significant differences between both groups in blood levels of wellknown inflammatory markers, such as CRP, procalcitonin, LDH, ferritin, and D-dimer. Complete results are presented in Table 2. Table 2. Analytical, gasometric, and X-ray characteristics of the CT and non-CT group. Variable Total Cohort Corticosteroids Not Corticosteroids p-Value O2saturation %, SD 92.9 ±5.9 91.3 ±6.68 93.8 ±5.31 <0.001 O2sat < 93% n(%) 5590 (34.3) 2812 (47.6) 2778 (26.8) <0.001 PO2mmHg 68.82 ±21.8 66.2 ±21.4 70.9 ±21.9 <0.001 PCO2mmHg 35.52 ±8.68 35.3 ±8.8 35.7 ±8.59 0.01 pH 7.43 ±0.18 7.44 ±0.14 7.43 ±0.22 0.03 PaO2/FiO2282.82 ±98.29 266 ±91.1 301 ±101 <0.001 Leucocytes 106/L 6300 (4800–8600) 6700 (4978–9250) 6110 (4700–8160) <0.001 Lymphocytes 106/L 940 (690–1300) 860 (600–1200) 1000 (700–1360) 0.14 Neutrophils 106/L 4600 (3220–6720) 5100 (3500–7552) 4340 (3100–6292) <0.001 Platelets 109/L 190 (148–247) 187 (146–246) 192 (149–247) 0.078 Haemoglobin gr/dL 13.8 (12.6–15) 13.6 ±1.96 13.7 ±1.85 0.047 CRP (C-reactive protein mg/dL) 61.5 (20.2–129.79) 86 (32.7–160) 49.3 (16–109) <0.001 Creatinine (mg/dL) 0.9 (0.73–1.16) 0.97 (0.78–1.25) 0.87 (0.71–1.1) <0.001 Urea (mg/dL) 37 (27–55) 42 (30–62) 35 (26–51) <0.001 LDH (lactate dehydrogenase UI/L) 321 (247–431) 353 (268–468) 305 (237–411) <0.001 Ferritin (µg/mL) 608 (285–1230.8) 791 (393–1534) 470 (236–996) <0.001 Procalcitonin (ng/mL) 0.1 (0.05–0.22) 0.13 (0.07–0.3) 0.09 (0.05–0.18) <0.001 Dimer-D (ng/mL) 669 (373–1261) 750 (430–1400) 617 (345–1180) <0.001 X-ray consolidation No 8687 (52.6) 3001 (50.1) 5679 (54) <0.001 Unilateral 2796 (16.9) 920 (15.4) 1881 (17.9) Bilateral 5016 (30.4) 2068 (34.5) 2950 (28.1) X-ray interstitial infiltrates/ground glass opacities No 5915 (35.84) 1877 (31.3) 4038 (38.4) <0.001 Unilateral 1699 (10.29) 515 (8.59) 1184 (11.3) Bilateral 8889 (53.86) 3604 (60.1) 5285 (50.3) Pleural effusion No 15,727 (95.32) 5712 (95.3) 10,015 (95.4) 0.58 Unilateral 497 (3.01) 189 (3.15) 308 (2.93) Bilateral 274 (1.6) 94 (1.57) 180 (1.71) Outcome Discharge home 12,119 (72.42) 3946 (65.12) 8173 (76.6) <0.001 Discharge care centre 1226 (7.32) 422 (6.96) 804 (7.55) Death 3388 (20.24) 1692 (27.92) 1696 (15.9) 3.3. Chest X-ray The chest X-ray presented bilateral condensations (n= 2068 (34.5%) vs. n= 2950 (28.1%), p< 0.001) or bilateral interstitial infiltrate image (n= 3604 (60.1%) vs. n= 5285 (50.3%), p< 0.001) more often in corticosteroids group. On the contrary, chest X ray without a pathological image (no presence of bilateral condensation or interstitial infiltrate) was more frequent in the non-CT group (n= 3001 (51.1%) vs. n= 5679 (54%), p< 0.001) and ( n= 1877 (31.3%) vs. n= 4038 (38.4%), p< 0.001), respectively. No difference was observed between both groups regarding the presence of pleural effusion (Table 2). J. Clin. Med. 2021,10, 4610 7 of 17 3.4. Administration of Corticosteroids during Pandemic We analysed the change in the use of CT over time. When all patients hospitalised were analysed, we observed that in the month of March, 33.7% received steroids, while in September, 76.4% of patients received corticosteroids (p< 0.001, Figure 2A). When the analysis was restricted to severe patients, oxygen saturation <=90%, according to WHO criteria [ 1 ] the proportion of patients receiving corticosteroids increased from 51.6% in March month to 85.7% in September (p= 0.02, Figure 2B). J. Clin. Med. 2021, 10, 4610 8 of 18 (A) (B) Figure 2. (A) Proportion of patients receiving and not receiving corticosteroids during the pandemic in hospitalised patients with COVID-19; (B) proportion of patients receiving and not receiving corticosteroids during the pandemic in patients with severe COVID-19 (modified WHO criteria). Prop: proportion, corsis: systemic corticosteroids. Accumulated prednisone equivalent dose (APED) used changed from March when 6.2, 5.7, 3.7 and 8.8% of patients received 200–400, 400–600, 600–800 or more than 800 mg, respectively, to September, when 31.4, 11.2, 13.4 and 6.7% received those doses (p < 0.001, Figure 2. ( A ) Proportion of patients receiving and not receiving corticosteroids during the pandemic in hospitalised patients with COVID-19; ( B ) proportion of patients receiving and not receiving corticosteroids during the pandemic in patients with severe COVID-19 (modified WHO criteria). Prop: proportion, corsis: systemic corticosteroids. Accumulated prednisone equivalent dose (APED) used changed from March when 6.2, 5.7, 3.7 and 8.8% of patients received 200–400, 400–600, 600–800 or more than 800 mg, respectively, to September, when 31.4, 11.2, 13.4 and 6.7% received those doses (p< 0.001, Figure 3A). When the analysis was restricted to patients with severe pneumonia, the rise increased from 8.5, 8.1, 5.7, 15.3% to 14.2, 21.4, 7.1, 35.7, 21.4% (p< 0.001, Figure 3B). J. Clin. Med. 2021,10, 4610 8 of 17 J. Clin. Med. 2021, 10, 4610 9 of 18 Figure 3A). When the analysis was restricted to patients with severe pneumonia, the rise increased from 8.5, 8.1, 5.7, 15.3% to 14.2, 21.4, 7.1, 35.7, 21.4% (p < 0.001, Figure 3B). (A) (B) Figure 3. (A) Evolution of the dose of accumulated prednisone equivalents administered to patients admitted with COVID-19; (B) evolution of the dose of accumulated prednisone equivalent administered dose to patients admitted with severe COVID-19 (modified WHO criteria). When we analysed patients’ outcomes, higher mortality is noticed in patients receiving CT (27.9% vs. 15.7% p < 0.001). When the analysis was restricted to severe patients, mortality was lower in the group of patients treated with steroids at 40.7% (CI95%, 38.6– 42.9%) vs. no steroids at 46.2% (CI95%, 43.91–48.6%) (p < 0.001). Mortality varied with steroid dose across oxygen saturation groups (Figure 4). To evaluate this finding, a logistic regression model of mortality risk factors was fit for each group of oxygen saturation. For Figure 3. ( A ) Evolution of the dose of accumulated prednisone equivalents administered to patients admitted with COVID-19; ( B ) evolution of the dose of accumulated prednisone equivalent administered dose to patients admitted with severe COVID-19 (modified WHO criteria). When we analysed patients’ outcomes, higher mortality is noticed in patients receiving CT (27.9% vs. 15.7% p< 0.001). When the analysis was restricted to severe patients, mortality was lower in the group of patients treated with steroids at 40.7% (CI95%, 38.6–42.9% ) vs. no steroids at 46.2% (CI95%, 43.91–48.6%) (p< 0.001). Mortality varied with steroid dose across oxygen saturation groups (Figure 4). To evaluate this finding, a logistic regression model of mortality risk factors was fit for each group of oxygen saturation. For patients with an oxygen saturation on admission above 94%, corticosteroids were associated with higher mortality across all dose ranges (OR 2.1, 1.7, 1.9, 2.0, p< 0.05 for all categories). In patients with an oxygen saturation between 91 and 94%, they were not associated with a higher nor a lower risk (OR 1.2, 1.3, 0.9, 0.9, p> 0.05 for all categories). In severe patients, with an oxygen saturation of 90% or less, corticosteroids were associated with a lower mortality risk at doses higher than 400 mg of APED (OR 1 (p= 0.7), 0.7 (p= 0.007), 0.5 (p= 0.0006), 0.6 (p= 0.0002), Table 3) in comparison to the group that did not receive CTs. J. Clin. Med. 2021,10, 4610 9 of 17 J. Clin. Med. 2021, 10, 4610 10 of 18 patients with an oxygen saturation on admission above 94%, corticosteroids were associated with higher mortality across all dose ranges (OR 2.1, 1.7, 1.9, 2.0, p < 0.05 for all categories). In patients with an oxygen saturation between 91 and 94%, they were not associated with a higher nor a lower risk (OR 1.2, 1.3, 0.9, 0.9, p > 0.05 for all categories). In severe patients, with an oxygen saturation of 90% or less, corticosteroids were associated with a lower mortality risk at doses higher than 400 mg of APED (OR 1 (p = 0.7), 0.7 (p = 0.007), 0.5 (p = 0.0006), 0.6 (p = 0.0002), Table 3) in comparison to the group that did not receive CTs. Figure 4. Mortality of patients by corticosteroid dose (accumulated prednisone equivalent dose) and oxygen saturation group. Table 3. Multivariate analysis with a logistic regression model by oxygen saturation group. Variable Oxygen Saturation Above 94 Oxygen Saturation Between 94–91% Oxygen Saturation Less or Equal to 90% OR (95% CI) p-Value OR (95% CI) p-Value OR (95% CI) p-Value APED (accumulated prednisone equivalent dose) 200–400 2.1 (1.5–2.9) <0.001 1.2 (0.9–1.7) 0.19 1 (0.8–1.4) 0.7 APED 400–600 1.7 (1.2–2.4) 0.003 1.3 (0.9–1.8) 0.12 0.7 (0.5–0.9) 0.007 APED 600–800 1.9 (1.3–2.8) 0.001 0.88 (0.6–1.4) 0.59 0.5 (0.4–0.8) 0.0006 APED > 800 2.0 (1.4–2.7) <0.001 0.9 (0.7–1.3) 0.66 0.6 (0.5–0.8) 0.0002 Charlson index >= 3 2.4 <0.001 2.1 (1.7–2.6) <0.001 1.9 (1.6-2.3)) <0.001 Lymphocytes > 1000 cells/mL 0.6 (0.5-0.7) <0.001 0.6 (0.5–0.8) <0.001 0.7 (0.6–0.8)) <0.001 Age 60–70 y 2.7 (1.8–4.1) <0.001 2.2 (1.5–3.5) <0.001 2.4 (1.7–3.4) <0.001 Age 70–80 y 7.3 (5.2–10.6) <0.001 8.4 (5.9–12.3) <0.001 6.0 (4.4–8.2) <0.001 Age >80 y 23.7 (16.9–33.9) <0.001 18.6 (13.1–27.2) <0.001 13.9 (10.3–19.2) <0.001 LDH (lacate dehydrogenase) 250– 500 IU/L 1.5 (1.2–1.9) 0.0005 1.66 (1.3–4.1) <0.001 2.5 (1.9–3.2) <0.001 LDH > 500 IU/L 3.8 (2.9–5.1) <0.001 3.1 (2.3–4.1) <0.001 6.5 (4.9–8.7) <0.001 Woman 0.8 (0.7–1.0) 0.13 0.7 (0.6–0.9) 0.0006 0.7 (0.6–0.8) <0.001 Figure 4. Mortality of patients by corticosteroid dose (accumulated prednisone equivalent dose) and oxygen saturation group. Table 3. Multivariate analysis with a logistic regression model by oxygen saturation group. Variable Oxygen Saturation above 94 Oxygen Saturation between 94–91% Oxygen Saturation Less or Equal to 90% OR (95% CI) p-Value OR (95% CI) p-Value OR (95% CI) p-Value APED (accumulated prednisone equivalent dose) 200–400 2.1 (1.5–2.9) <0.001 1.2 (0.9–1.7) 0.19 1 (0.8–1.4) 0.7 APED 400–600 1.7 (1.2–2.4) 0.003 1.3 (0.9–1.8) 0.12 0.7 (0.5–0.9) 0.007 APED 600–800 1.9 (1.3–2.8) 0.001 0.88 (0.6–1.4) 0.59 0.5 (0.4–0.8) 0.0006 APED > 800 2.0 (1.4–2.7) <0.001 0.9 (0.7–1.3) 0.66 0.6 (0.5–0.8) 0.0002 Charlson index >= 3 2.4 <0.001 2.1 (1.7–2.6) <0.001 1.9 (1.6-2.3)) <0.001 Lymphocytes > 1000 cells/mL 0.6 (0.5-0.7) <0.001 0.6 (0.5–0.8) <0.001 0.7 (0.6–0.8)) <0.001 Age 60–70 y 2.7 (1.8–4.1) <0.001 2.2 (1.5–3.5) <0.001 2.4 (1.7–3.4) <0.001 Age 70–80 y 7.3 (5.2–10.6) <0.001 8.4 (5.9–12.3) <0.001 6.0 (4.4–8.2) <0.001 Age >80 y 23.7 (16.9–33.9) <0.001 18.6 (13.1–27.2) <0.001 13.9 (10.3–19.2) <0.001 LDH (lacate dehydrogenase) 250–500 IU/L 1.5 (1.2–1.9) 0.0005 1.66 (1.3–4.1) <0.001 2.5 (1.9–3.2) <0.001 LDH > 500 IU/L 3.8 (2.9–5.1) <0.001 3.1 (2.3–4.1) <0.001 6.5 (4.9–8.7) <0.001 Woman 0.8 (0.7–1.0) 0.13 0.7 (0.6–0.9) 0.0006 0.7 (0.6–0.8) <0.001 Tocilizumab 2.5 (1.7–3.5) <0.001 2.7 (2.0–3.6) <0.001 1.09 (0.8–1.8) 0.47 Remdesivir 0.4 (0.1–1.0) 0.08 0.5 (0.2–1.0) 0.069 0.8 (0.3–1.8) 0.6 4. Discussion Our retrospective analysis showed that CTs were used mostly in severe and critical cases of COVID-19. Patients that received CTs were generally older, more frequently male, and more affected by comorbidities. The prevalence of hypertension, cardiovascular diseases, and diabetes has been reported to be two- to threefold higher in patients with a severe type of COVID-19 than in their non-severe counterparts, and these comorbidities have been described as risk factors for mortality [20–23]. The most common comorbidity in our cohort was high blood pressure with a prevalence of 51.1% which is higher than in other published data. The prevalence of high blood pressure in other works ranges between 10 and 34% [ 24 ]. Higher prevalence in our cohort is congruent with national data of arterial hypertension in Spain, where one in three adults is hypertensive (66% in those >60 years) [25]. 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