Mortality reduction in older COVID-19-patients hospitalized in Spain during the second pandemic wave from the SEMI-COVID-19 Registry

1 Vol.:(0123456789) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports Mortality reduction in older COVID‑19‑patients hospitalized in Spain during the second pandemic wave from the SEMI‑COVID‑19 Registry José‑Manuel Casas‑Rojo 1,2,92, Juan‑Miguel Antón‑Santos 1,2,92, Jesús Millán‑Núñez‑Cortés 3, Ricardo Gómez‑Huelgas 4, José‑Manuel Ramos‑Rincón 5*, Manuel Rubio‑Rivas 6, Miguel‑Ángel Corrales‑González 7, Maria‑Rosa Fernández‑Madera‑Martínez 8, José‑Luis Beato‑Pérez 9, Francisco Arnalich‑Fernández 10, Cristina Gállego‑Lezaun 11, Pablo Pérez‑Martínez 12, Sonia Molinos‑Castro 13, Yale Tung‑Chen 14, Manuel Madrazo 15, Manuel Méndez‑Bailón 16, Daniel Monge‑Monge 17, Gema‑María García‑García 18, Rosa García‑Fenoll 19, Noemí Gilabert 20, Rebeca Fuerte‑Martínez 21, Marta Contreras‑Sánchez 22, Nicolás Rhyman 23, Jorge Peris‑García 24, Carlos Lumbreras‑Bermejo 25 & The SEMI‑COVID‑19 Network * In 2020, the COVID‑19 pandemic followed a two‑wave pattern in most countries. Hospital admission for COVID‑19 in one wave or another could have affected mortality, especially among the older persons. The objective of this study was to evaluate whether the admission of older patients during the different waves, before SARS‑CoV‑2 vaccination was available, was associated with a different mortality. We compared the mortality rates of patients hospitalized during 2020 before (first wave) and after (second wave) July 7, 2020, included in the SEMI‑COVID‑19 Registry, a large, multicenter, retrospective cohort of patients admitted to 126 Spanish hospitals for COVID‑19. A multivariate logistic regression analysis was performed to control for changes in either the patient or disease profile. As of December 26, 2022, 22,494 patients had been included (17,784 from the first wave and 4710 from the second one). Overall mortality was 20.4% in the first wave and 17.2% in the second wave (risk difference (RD) − 3.2%; 95% confidence interval (95% CI) − 4.4 to − 2.0). Only patients aged 70 and older (10,973 patients: 8571 in the first wave and 2386 in the second wave) had a significant reduction in mortality (RD − 7.6%; 95% CI − 9.7 to − 5.5) (unadjusted relative risk reduction: 21.6%). After adjusting for age, comorbidities, variables related to the severity of the disease, and treatment received, admission during the second wave remained a protective factor. In Spain, patients aged 70 years and older admitted during the second wave of the COVID‑19 pandemic had a significantly lower risk of mortality, except in severely dependent persons in need of corticosteroid treatment. This effect is independent of patient characteristics, disease severity, or treatment received. This suggests a protective effect of a better standard of care, greater clinical expertise, or a lesser degree of healthcare system overload. OPEN 1Internal Medicine Department, Infanta Cristina University Hospital, Parla, Madrid, Spain. 2Instituto de Investigación Sanitaria Puerta de Hierro-Segovia de Arana (IDIPHISA), Universidad Complutense de Madrid, Madrid, Spain. 3Internal Medicine Department, Gregorio Marañón University Hospital, Madrid, Spain. 4Internal Medicine Department, Regional University Hospital of Málaga, Biomedical Research Institute of Málaga (IBIMA), University of Málaga (UMA), Málaga, Spain. 5Clinical Medicine Department, Miguel Hernandez University of Elche, Ctra N332 s/n, 03550 Sant Joan d’Alacant, Alicante, Spain. 6Internal Medicine Department, Bellvitge University Hospital, L’Hospitalet de Llobregat, Barcelona, Spain. 7Internal Medicine Department, Costa del Sol Hospital, Marbella, Málaga, Spain. 8Internal Medicine Department, Cabueñes University Hospital, Gijón, Asturias, Spain. 9Internal Medicine Department, Complejo Hospitalario Universitario de Albacete, Albacete, Spain. 10Internal 2 Vol:.(1234567890) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ The coronavirus disease 19 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a major, life-changing event that continues to overwhelm man’s collective mind. As of June 15, 2021, 175,333,154 infections and 3,793,230 deaths had been reported worldwide1. In 2020, most countries experienced at least two waves of the pandemic. Patients with COVID-19 who require hospitalization have a high mortality rate. The in-hospital mortality rate was very high in the early phases of the pandemic, though there was a great degree of variability among countries and even areas, with mortality rates ranging from 12% up to 28%2–6. Mortality is higher in older patients (especially those over 79years)5,7 as well as in patients with certain underlying conditions8. Mortality is one of the main measures of severity of any epidemic. It is prone to change over time due to improved comprehension of the disease and the development of better treatments. COVID-19 mortality has fluctuated over time9–11 and varies according to geography12,13. In most countries, the pandemic followed a twowave pattern in 2020, with a first wave in the spring and a second wave starting in late summer or early autumn. In North America10 and Europe14–16, mortality was higher in the first wave whereas in Africa17 and Brazil18, it was higher in the second wave. In Spain, this evolution has yet to be thoroughly studied. The first wave was followed by three more waves (in early autumn, late autumn, and midwinter of 2020), each of which had lower death tolls and unadjusted mortality rates. A single-center study in Reus, Spain19 has shown that, after controlling for known mortality factors, there was still a lower mortality rate in the second wave. According to our preliminary data, we hypothesized that hospital admission in the different waves could affect the mortality of patients with COVID-19, especially the older people. The primary aim of the study was to evaluate whether the admission of older patients during the different waves of 2020 was associated with a different mortality rate and whether this could be explained by differences in the characteristics of either the patients or the severity of the disease. Results Mortality estimation and case fatality rates in first and second waves As of December 26, 2020, a total of 22,494 patients had been included in the SEMI-COVID-19 Registry: 17,784 patients from the first wave and 4710 patients from the second wave. Overall mortality was 20.4% in the first wave and 17.2% in the second wave (RD −3.2%; 95% CI −4.4 to −2.0). Figure1 shows the case fatality rate stratified by age. As there were no differences in mortality according to wave in patients younger than 70years, we focused the study on patients who were 70years of age and older (10,973 patients: 8587 from the first wave and 2386 from the second wave). As has been observed in previous studies, mortality rose with age but was consistently lower in the second wave in patients older than 70years: 35.2% and 27.6% in first and second waves, respectively, which represents a 7.8% absolute risk reduction and 21.6% relative risk reduction. Base line characteristics, clinical presentation upon admission, and treatments received between waves The differences in baseline characteristics (demographics and comorbidities), clinical presentation upon admission, and treatments received between waves were analyzed (Table1). There were some differences in demographics in second-wave patients, including older age (second wave: 82.0 vs first wave: 80.8years), a greater proportion of women (second wave: 48.2% vs first wave: 45.2%), a higher proportion of patients with hypertension (second wave: 73.7% vs first wave: 71.3%) and diabetes (second wave: 30.9% vs first wave: 26.3%), and a slightly higher degree of comorbidity (Charlson Comorbidity Index in the second wave: 5.7 vs first wave: 5.4). The clinical manifestations were also slightly different. Laboratory results showed some differences: blood glucose and creatinine values were higher in patients admitted in the second wave whereas hemoglobin was lower. A high-risk inflammatory pattern was more frequent in the first wave. In the second wave, there was greater use of corticosteroids (79.3% vs 39.5%) and remdesivir (12.6% vs 0.4%). Some of these aforementioned variations could be considered protective (e.g., more women, a lower-risk inflammatory pattern) whereas others (higher age, more diabetes, higher creatinine levels) would suggest higher risk of mortality. Medicine Department, Hospital Universitario La Paz, Madrid, Spain. 11Internal Medicine Department, Hospital Royo Villanova, Zaragoza, Spain. 12Lipids and Atherosclerosis Unit, Department of Internal Medicine, Maimonides Biomedical Research Institute of Córdoba (IMIBIC), Reina Sofía University Hospital, University of Córdoba, CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. 13Internal Medicine Department, Complejo Hospitalario Universitario de Santiago, A Coruña, Spain. 14Internal Medicine Department, Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain. 15Internal Medicine Department, Hospital Universitario Doctor, Peset, Valencia, Spain. 16Internal Medicine Department, Hospital Clínico San Carlos, Madrid, Spain. 17Internal Medicine Department, Complejo Asistencial de Segovia, Segovia, Spain. 18Internal Medicine Department, Complejo Hospital Universitario de Badajoz, Badajoz, Spain. 19Internal Medicine Department. Hospital, Universitario Miguel Servet, Zaragoza, Spain. 20Internal Medicine Department, Hospital Universitario de la Princesa, Madrid, Spain. 21Internal Medicine Department, Hospital Universitario Infanta Sofía, SS de los Reyes, Madrid, Spain. 22Internal Medicine Department, Complexo Hospitalario Universitario A Coruña, A Coruña, Spain. 23Internal Medicine Department, Hospital de Sant Joan Despí Moisès Broggi, Sant Joan Despí, Barcelona, Spain. 24Internal Medicine Department, Hospital Universitari Sant Joan d’Alacant, Alicante, Spain. 25Internal Medicine Department, 12 de Octubre University Hospital, Madrid, Spain. 92These authors contributed equally: José-Manuel Casas-Rojo and Juan-Miguel Antón-Santos. *A list of authors and their affiliations appears at the end of the paper. *email: [email protected] 3 Vol.:(0123456789) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ Clinical differences in patients ≥ 70 years hospitalized for COVID‑19 by survival status A univariate analysis of mortality was performed. Almost all variables were statistically associated with mortality, denoting the large sample size. Table2 shows data on demographics, clinical manifestations, laboratory findings, and treatments received. Although some of the associations were strong, the effects were often small. The variables and interactions included in the maximal logistic regression model estimated are shown in Table3. The odds ratios and risk ratios of mortality of being admitted in the second wave versus the first one for the four different combinations of interaction covariates values are shown in Table4. The protective effect of being admitted during the second wave of COVID-19 is highest for patients without severe dependence who are not treated with corticosteroids, and it diminishes when either of these circumstances is present, becoming neutral when both converge. Discussion This study confirms a difference in mortality in patients hospitalized for COVID-19 in Spain between the first and the second wave of the pandemic. The lower mortality rate observed in the second wave is due to the lower mortality in patients ≥ 70years; no differences in mortality were observed among younger subjects. To our knowledge, this is a novel finding that has not been previously described. This reduced mortality rate found in older patients hospitalized during the second wave of the COVID-19 pandemic compared to the first wave could be due to some unmeasured or unknown confounders which may be broadly grouped into three categories: differences in the patients, differences in the disease, or differences in treatment and overall management. The second-wave patients in this study were slightly older, had a higher degree of dependence (as measured by Barthel index), and a greater comorbidity burden. Given that these factors are associated with a worse COVID-19 prognosis, the clinical and epidemiologic differences between patients hospitalized in the first and the second wave20 do not explain the lower mortality observed in the second wave. Although patients admitted during the second wave were slightly less severe at admission, after adjustments in the multivariate analysis, admission during the second wave remained an independent protective factor. In the second wave, the use of treatments that have been shown to reduce COVID-19 mortality increased, namely corticosteroids21, tocilizumab22,23, and remdesivir24. However, the lower mortality in the second wave was unchanged after adjusting for the use of these therapies. Our results suggest that there may be some factor (or, more probably, combination of factors), associated with hospitalization that influences mortality and changed between the waves. Some potential candidates are changes in the overall management of patients, improvements in clinical expertise, and a lesser degree of hospital overload (Fig.2). Interestingly, the analysis of the interactions between severe dependency and systemic corticosteroid treatment with the hospital admission wave has allowed us to discover that the protective effect of the second wave on mortality is highest when both factors are absent, somewhat reduced when either of them is present, and neutral when both are present. It is likely, then, that the factors, largely unknown, that explain an average protective effect of the second wave, may not succeed in improving the prognosis of the most fragile and severe patients, such as those requiring corticosteroid treatment. Figure1. Case Fatality Rate (CFR) according to age in patients hospitalized during the first and second waves of COVID-19, expressed as percentage (deceased patients/total patients). The diamonds indicate the point estimate of the risk difference, and the horizontal bars represent its 95% confidence interval. 4 Vol:.(1234567890) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ Variable First wave (n = 8587) Second wave (n = 2386) OR (95% CI OR) p Demographics and comorbidities Age (years), mean (SD) 80.8 (7.0) 82.0 (7.1) < 0.001 Age (years), categorized < 0.001 70–79 (%) 50.3 41.5 1 (ref.) 80–89 (%) 38.8 43.8 1.37 (1.24–1.51) ≥ 90 (%) 10.9 14.8 1.64 (1.43–1.89) Female sex (%) 45.2 48.2 1.13 (1.03–1.24) 0.008 Severe dependence (%)a29.1 39.0 1.56 (1.42–1.71) < 0.001 Age-adjusted Charlson Comorbidity Index, mean (SD) 5.4 (2.1) 5.7 (2.1) < 0.001 Age-adjusted Charlson Comorbidity Index score, categorized < 0.001 Moderate (3–4) (%) 39.9 34.2 1 (ref.) Severe (> 4) (%) 60.1 65.8 1.27 (1.16–1.40) Hypertension (%) 71.3 73.7 1.13 (1.02–1.25) 0.021 Diabetes (%) 26.3 30.9 1.25 (1.13–1.38) < 0.001 Cardiovascular disease (%)c32.8 32.4 0.98 (0.89–1.08) 0.723 Obesity (%)b20.3 18.7 0.90 (0.80–1.02) 0.095 Obstructive respiratory disease (%)d23.0 22.7 0.99 (0.89–1.10) 0.808 Dementia (%) 18.5 20.9 1.16 (1.04–1.30) 0.009 Malignancy (%)e13.8 14.1 1.03 (0.90–1.17) 0.692 CKD (%)f9.6 10.4 1.10 (0.94–1.27) 0.235 Moderate-severe chronic liver disease (%) 1.1 1.1 1.03 (0.67–1.59) 0.887 Clinical presentation upon admission Days from symptom onset, mean (SD) 6.1 (4.7) 5.5 (4.6) < 0.001 Dyspnea (%) 57.6 58.4 1.03 (0.94–1.13) 0.519 Fatigue (%) 40.4 45.7 1.24 (1.13–1.36) < 0.001 Anorexia (%) 21.1 23.9 1.18 (1.06–1.31) 0.003 Myalgia (%) 21.8 18.2 0.80 (0.71–0.90) < 0.001 Clouding of consciousness (%) 20.5 21.1 1.04 (0.93–1.16) 0.486 Diarrhea (%) 19.4 18.3 0.93 (0.83–1.05) 0.241 Abdominal pain (%) 6.3 5.3 0.84 (0.69–1.03) 0.089 Anosmia (%) 4.1 5.1 1.26 (1.02–1.56) 0.035 Sore throat (%) 7.1 5.6 0.78 (0.64–0.95) 0.010 Systolic blood pressure, mmHg, mean (SD) 131.0 (23.1) 131.5 (23.3) 0.418 Arterial stiffness (%)g47.1 48.0 1.04 (0.94–1.13) 0.459 Tachypnea (%)h36.9 32.2 0.81 (0.74–0.89) < 0.001 Fever (%)i20.5 12.4 0.55 (0.48–0.63) < 0.001 Tachycardia (%)j16.9 13.7 0.78 (0.68–0.89) < 0.001 Oxygen saturation (pulse oximetry, %), median [IQR] 93.0 [90.0–96.0] 94.0 [91.0–96.0] < 0.001 Rales (%) 57.9 51.0 0.76 (0.69–0.83) < 0.001  pO2/FiO2 ratio (%), mean (SD) 273.4 (94.3) 290.6 (87.9) < 0.001 Laboratory and radiological findings at admission Hemoglobin (g/dL), mean (SD) 13.2 (2.0) 12.8 (2.1) < 0.001 Platelet count, ×109/L, median [IQR] 182.0 [141.0–241.0] 185.0 [141.0–247.0] 0.353 White blood cell count, ×109/L, median [IQR] 6.6 [4.9–9.2] 6.6 [4.8–9.4] 0.942 Eosinophil count, ×109/L, median [IQR] 0.0 [0.0–0.0] 0.0 [0.0–0.0] 0.009 Lymphocyte count, ×109/L, median [IQR] 0.9 [0.6–1.2] 0.9 [0.6–1.2] 0.245 Blood glucose (mg/dL), median [IQR] 119.0 [102.0–149.0] 122.0 [104.0–154.0] < 0.001 Glucose (mg/dL) < 0.001 < 140 (%) 69.4 65.3 1 (ref.) 140–179 (%) 16.4 18.6 1.21 (1.07–1.36) ≥ 180 (%) 14.2 16.1 1.21 (1.06–1.38) C-Reactive protein (mg/L), median [IQR] 69.8 [23.2–141.0] 67.6 [27.0–129.8] 0.380 Creatinine (mg/dL), median [IQR] 1.0 [0.8–1.4] 1.0 [0.8–1.5] 0.003 Inflammatory patternk < 0.001 Continued 5 Vol.:(0123456789) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ We have learned that there are a lot of “intangibles” that influence the prognosis of COVID-19 hospitalization. Quick identification of respiratory failure, thromboprophylaxis and early mobilization, an adequate state of hydration, proper management of stress hyperglycemia, nutritional support, physical rehabilitation, and more have become the new standard of care and are potential uncontrolled factors that could explain the better prognosis in the second wave. Most of these factors will have a greater impact on the older people, as they are frailer and thus prone to physical deconditioning, dehydration, or confusional states. Another possibility is that greater clinical expertise led to improved prognosis. Indeed, in the USA, a progressive decline in COVID-19-related mortality was described after the passage of just a few months9. Healthcare system overload could be an important driver of mortality in the COVID-19 pandemic25–27. The first wave in Spain was explosive and overwhelmed hospitals in some areas. For instance, in Madrid, COVID-19 occupancy reached nearly 300% of the nominal ICU capacity and nearly 105% of the general ward capacity28, paralyzing non-emergency surgical procedures. On the contrary, the second wave has been less dramatic, leading to a smaller impact on hospital occupancy and healthcare activity. It may well be that the lower mortality in the second wave is mainly a reflection of less healthcare system overload. As our study does not include data on the true workload borne by the hospitals, this notion remains a hypothesis. In extreme cases, healthcare system overload leads to shortages, which can also have a greater effect on the older persons due to implementation of triage criteria. If it were confirmed that healthcare system overload causes greater mortality in the older persons, it would be a moral imperative for us as a society to quickly adopt robust preventative measures as soon as another wave is upon us and there is risk of healthcare system overload. Our registry cannot answer this crucial question, as we lack data on hospital or ICU patient loads at the time of the patients’ admissions. We recognize several limitations in our study. The large number of researchers involved and/or variability in the availability of data from each hospital could have led to information bias. Selection bias could have been introduced given the voluntary participation of each center. A potential source of uncontrolled confounding factors is the severity of underlying conditions and overall frailty of patients. The lower mortality in the second wave could also be explained by a “harvesting effect” that may be present if the most severely ill patients had already died in the first wave, though patients admitted during the second wave were older and had more dementia and comorbidities. However, we did not analyze frailty, a prognostic factor that is more potent than dependence or age in older patients20. In terms of limitations regarding treatment-related variables, the effect of remdesivir and tocilizumab on mortality are strongly time-dependent with a narrow window of opportunity and both tocilizumab and corticosteroids are indicated for a worsening respiratory or inflammatory condition. Our registry includes data on the timing of the drug initiation but does not include clinical and laboratory findings at that moment, so it is not Table 1. Clinical differences in patients ≥ 70years admitted in the first and second waves of COVID-19. a Severe dependence: Barthel Index < 60; bObesity: BMI ≥ 30kg/m2; cCardiovascular disease: Ischemic heart disease, heart failure, transient ischemic attack, stroke, or peripheral artery disease; dObstructive respiratory disease: chronic obstructive pulmonary disease, asthma, chronic bronchitis, or obstructive sleep apnea; eMalignancy: solid tumor, leukemia, lymphoma; fCKD: chronic kidney disease (patients on dialysis or with serum creatinine > 3mg/dL); gArterial stiffness: pulse pressure ≥ 60mmHg; hTachypnea: > 20 breaths per minute; iFever: temperature > 37.8°C; jTachycardia: heart rate > 100bpm; kInflammatory pattern: see description in “Methods”. Categoric variables are expressed as percentages and compared using likelihoodratio chi-square test for statistical significance. OR = odds ratio; 95% CI OR: 95% confidence interval for odds ratio. The odds ratios have been calculated with respect to the first category. Quantitative variables are expressed as mean (standard deviation) or median [interquartile range] and were compared for statistical significance using Student’s t-test (with equal or unequal variances) or Mann–Whitney U test, as appropriate. Variable First wave (n = 8587) Second wave (n = 2386) OR (95% CI OR) p Low (%) 1.9 1.9 1 (ref.) Moderate (%) 13.7 18.0 1.27 (0.88–1.81) Severe (%) 84.4 80.1 0.92 (0.65–1.30) Infiltrates (any) in chest X-ray (%) 84.5 77.7 0.64 (0.57–0.71) < 0.001 Treatments received Oxygen via high flow nasal cannula (%) 8.1 8.4 1.04 (0.88–1.22) 0.673 Non-invasive mechanical ventilation (%) 5.8 6.4 1.11 (0.92–1.34) 0.269 Invasive mechanical ventilation (%) 5.3 4.5 0.85 (0.69–1.06) 0.142 Systemic corticosteroids (%) 39.5 79.3 5.88 (5.28–6.55) < 0.001 Remdesivir < 0.001 No (%) 99.6 87.4 1 (ref.) Yes, ≤ 10days from symptoms onset (%) 0.2 11.5 79.22 (46.20–135.85) Yes, > 10days from symptoms onset (%) 0.2 1.1 5.24 (2.96–9.29) Tocilizumab (%) 7.1 7.0 0.98 (0.82–1.17) 0.825 Death (admission or re-admission) (%) 35.2 27.6 0.70 (0.64–0.78) < 0.001 6 Vol:.(1234567890) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ Variable Survivors (n = 7290) No survivors (n = 3683) OR (95% CI OR) p Demographics and comorbidities Age (years), mean (SD) 80.0 (6.9) 83.1 (6.9) < 0.001 Age (years), categorized < 0.001 70–79 (%) 55.0 35.4 1 (ref.) 80–89 (%) 35.8 48.0 2.08 (1.91–2.27) ≥ 90 (%) 9.2 16.7 2.82 (2.49–3.20) Female sex (%) 49.0 39.7 0.69 (0.63–0.74) < 0.001 Severe dependence (%)a25.2 43.5 2.28 (2.10–2.48) < 0.001 Age-adjusted Charlson Comorbidity Index, mean (SD) 5.2 (2.0) 6.1 (2.2) < 0.001 Age-adjusted Charlson Comorbidity Index score, categorized < 0.001 Moderate (3–4) (%) 45.1 25.8 1 (ref.) Severe (> 4) (%) 54.9 74.2 2.37 (2.17–2.58) Hypertension (%) 70.2 74.9 1.27 (1.16–1.39) < 0.001 Diabetes (%) 25.9 30.1 1.23 (1.13–1.34) < 0.001 Cardiovascular disease (%)c28.5 41.0 1.74 (1.60–1.89) < 0.001 Obesity (%)b20.0 19.8 0.99 (0.89–1.10) 0.836 Obstructive respiratory disease (%)d21.9 24.9 1.18 (1.07–1.29) < 0.001 Dementia (%) 15.4 26.2 1.94 (1.76–2.14) < 0.001 Malignancy (%)e12.6 16.2 1.34 (1.19–1.49) < 0.001 CKD (%)f7.7 13.9 1.93 (1.70–2.19) < 0.001 Moderate-severe chronic liver disease (%) 1.1 1.1 1.05 (0.72–1.53) 0.788 Clinical presentation upon admission Days from symptom onset, mean (SD) 6.4 (4.8) 5.1 (4.3) < 0.001 Dyspnea (%) 51.2 70.9 2.32 (2.13–2.53) < 0.001 Fatigue (%) 42.7 39.2 0.87 (0.80–0.94) < 0.001 Anorexia (%) 21.4 22.4 1.07 (0.97–1.17) 0.202 Myalgia (%) 23.2 16.5 0.65 (0.59–0.72) < 0.001 Clouding of consciousness (%) 14.2 33.4 3.02 (2.74–3.32) < 0.001 Diarrhea (%) 21.6 14.4 0.61 (0.55–0.68) < 0.001 Abdominal pain (%) 6.7 4.7 0.69 (0.58–0.83) < 0.001 Anosmia (%) 5.8 1.3 0.22 (0.16–0.30) < 0.001 Sore throat (%) 7.6 5.1 0.66 (0.55–0.78) < 0.001 Systolic blood pressure, mmHg, mean (SD) 132.5 (22.4) 128.5 (24.2) < 0.001 Arterial stiffness (%)g48.4 45.2 0.88 (0.81–0.95) 0.002 Tachypnea (%)h26.3 55.0 3.44 (3.16–3.74) < 0.001 Fever (%)i17.0 22.2 1.39 (1.26–1.54) < 0.001 Tachycardia (%)j13.4 21.7 1.79 (1.62–1.99) < 0.001 Oxygen saturation (pulse oximetry, %), median [IQR] 94.0 [91.0–96.0] 91.0 [86.0–95.0] < 0.001 Rales (%) 53.6 61.9 1.41 (1.30–1.53) < 0.001  pO2/FiO2 ratio (%), mean (SD) 298.9 (85.7) 239.3 (93.5) < 0.001 Laboratory and radiological findings at admission Hemoglobin (g/dL), mean (SD) 13.2 (1.9) 13.0 (2.2) < 0.001 Platelet count, × 109/L, median [IQR] 185.0 [143.0–246.5] 178.0 [138.0–233.0] < 0.001 White blood cell count, × 109/L, median [IQR] 6.3 [4.8–8.6] 7.4 [5.3–10.6] < 0.001 Eosinophil count, × 109/L, median [IQR] 0.0 [0.0–0.0] 0.0 [0.0–0.0] < 0.001 Lymphocyte count, × 109/L, median [IQR] 0.9 [0.7–1.3] 0.8 [0.5–1.1] < 0.001 Blood glucose (mg/dL), median [IQR] 116.0 [100.0–142.0] 129.0 [108.0–169.0] < 0.001 Glucose (mg/dL) < 0.001 < 140 (%) 73.2 59.2 1 (ref.) 140–179 (%) 15.3 19.9 1.60 (1.44–1.79) ≥ 180 (%) 11.4 20.9 2.26 (2.02–2.53) C-Reactive protein (mg/L), median [IQR] 56.5 [19.0–116.9] 101.0 [40.9–179.0] < 0.001 Creatinine (mg/dL), median [IQR] 0.9 [0.8–1.2] 1.2 [0.9–1.7] < 0.001 Inflammatory patternk < 0.001 Low (%) 2.6 0.4 1 (ref.) Moderate (%) 19.1 6.1 1.88 (1.09–3.26) Continued 7 Vol.:(0123456789) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ possible to evaluate the exact effect of the drugs. The deleterious effect of corticosteroids or tocilizumab in our multivariate analysis should be interpreted as a marker of the patient’s worsening condition. The strengths of this study include its multicenter, nationwide design as well as the large number of patients included, which provides strong statistical power. The consecutive inclusion of patients in each center limits selection bias. In conclusion, mortality in the older patients hospitalized in Spain with COVID-19 has been significantly lower in the second wave even after adjusting for baseline clinical condition, disease severity upon admission, and pharmacological treatment with proven benefits in treating COVID-19, except in severely dependent persons in need of corticosteroid treatment. Our results suggest that this reduction of mortality could be related to a better standard of care, improvements in clinical expertise, less healthcare system overload, or a combination of these three factors, though other unknown confounding factors cannot be ruled out. Methods Study design This is a retrospective cohort study comparing the first and second waves of the COVID-19 epidemic in Spain. The first wave was defined as the period between January 1 and July 7, 2020. The second wave was defined as the period between July 8, 2020 and December 26, 2020, before SARS-CoV-2 vaccination was available. The final weeks of the first wave and the initial weeks of the second one thus defined periods with a low incidence of COVID-19 and few hospital admissions. However, this cut-off point reflects the transition from the greater healthcare system overload which occurred in the initial months to the lesser healthcare system overload of the later months. Registry design The SEMI-COVID-19 Registry is an ongoing, nationwide, retrospective cohort launched in March 2020 that comprises most consecutive patients hospitalized in Spain who are discharged with confirmed COVID-19 disease. It has become one of the largest repositories of COVID-19 patient data and includes more than 20,000 patients to date. Its characteristics have been thoroughly described elsewhere6. Inclusion criteria for the registry were age ≥ 18years and first hospital discharge with a confirmed diagnosis of COVID-19. Exclusion criteria were subsequent admissions of the same patient and denial or withdrawal of informed consent. Consecutive patients who required hospital admission and who had SARS-CoV-2 infection confirmed by a positive result on real-time polymerase chain reaction (RT-PCR) testing of a nasopharyngeal, bronchoalveolar lavage, or sputum sample and who provided verbal consent were included in the registry. With the advent of the second wave, the inclusion criteria were expanded with two modifications: antigen testing was accepted as a method for confirming diagnosis and reinfections (> 3months from the initial infection) of the same patient were accepted for inclusion. From March 23, 2020, to December 26, 2021, a total of 22,494 patients from 126 hospitals throughout the country were included in the registry. Table 2. Clinical differences in patients ≥ 70years hospitalized for COVID-19 by survival status. a S evere dependence: Barthel Index < 60; bObesity: BMI ≥ 30kg/m2; cCardiovascular disease: Ischemic heart disease, heart failure, transient ischemic attack, stroke, or peripheral artery disease; dObstructive respiratory disease: chronic obstructive pulmonary disease, asthma, chronic bronchitis, or obstructive sleep apnea; eMalignancy: solid tumor, leukemia, lymphoma; fCKD: chronic kidney disease (patients on dialysis or with serum creatinine > 3mg/dL); gArterial stiffness: pulse pressure ≥ 60mmHg; hTachypnea: > 20 breaths per minute; iFever: temperature > 37.8 ºC; jTachycardia: heart rate > 100bpm; kInflammatory pattern: see description in “Methods”. Categoric variables are expressed as percentages and compared using likelihood-ratio chi-square test for statistical significance. OR = odds ratio; 95% CI OR: 95% confidence interval for odds ratio. The odds ratios have been calculated with respect to the first category. Quantitative variables are expressed as mean (standard deviation) or median [interquartile range] and were compared for statistical significance using Student’s t-test (with equal or unequal variances) or Mann–Whitney U test, as appropriate. Variable Survivors (n = 7290) No survivors (n = 3683) OR (95% CI OR) p Severe (%) 78.3 93.4 7.01 (4.13–11.90) Infiltrates (any) in chest X-ray (%) 80.8 87.4 1.65 (1.47–1.85) < 0.001 Treatments received Oxygen via high flow nasal cannula (%) 5.3 13.7 2.82 (2.45–3.24) < 0.001 Non-invasive mechanical ventilation (%) 3.0 11.7 4.24 (3.59–5.02) < 0.001 Invasive mechanical ventilation (%) 2.9 9.5 3.49 (2.93–4.16) < 0.001 Systemic corticosteroids (%) 45.1 54.4 1.46 (1.35–1.58) < 0.001 Remdesivir < 0.001 No (%) 96.4 98.0 1 (ref.) Yes, ≤ 10days from symptoms onset (%) 3.1 1.7 0.53 (0.40–0.71) Yes, > 10days from symptoms onset (%) 0.5 0.3 0.65 (0.34–1.26) Tocilizumab (%) 6.6 8.1 1.24 (1.07–1.45) 0.005 8 Vol:.(1234567890) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ Table 3. Logistic regression multivariate model showing the average effect of being admitted in the second wave after adjusting for confounding variables. a,c See description in “Methods”. bBarthel index < 60. dInteraction between wave and severe dependence. eInteraction between wave ant systemic corticosteroid therapy. The odds ratio of Second wave represents the average effect of hospital admission during the second wave, adjusted for confounding terms and interaction. Table4 displays the effect for each combination of interaction term values. Death Adjusted odds ratio (95% CI) p-value Second wave 0.35 (0.24–0.52) < 0.001 Age (years) 70–79 1 (ref.) 80–89 1.88 (1.65–2.15) < 0.001 ≥ 90 2.32 (1.91–2.81) < 0.001 Female sex 0.64 (0.57–0.71) < 0.001 Age-adjusted Charlson comorbidity index Moderate (3–4) 1 (ref.) Severe (> 4) 1.45 (1.27–1.66) < 0.001 Arterial hypertension 1.00 (0.89–1.14) 0.957 Arterial stiffnessa0.86 (0.77–0.96) 0.007 Severe dependenceb1.65 (1.42–1.91) < 0.001 Days from beginning of symptoms 0.95 (0.94–0.96) < 0.001 Clouding of consciousness 1.76 (1.54–2.02) < 0.001 Tachypnea 1.92 (1.71–2.15) < 0.001 Oxygen saturation/FiO2 ratio 1.00 (0.99–1.00) < 0.001 Blood glucose (mg/dL) < 140 1 (ref.) 140–179 1.25 (1.08–1.44) 0.003 ≥ 180 1.45 (1.24–1.68) < 0.001 Inflammatory patternc Low 1 (ref) Moderate 1.44 (0.75–2.78) 0.277 Severe 3.66 (1.94–6.92) < 0.001 Bilateral pneumonia 1.37 (1.22–1.55) < 0.001 Tocilizumab 0.91 (0.73–1.12) 0.372 Remdesivir, start date No 1 (ref.) Yes, ≤ 10days from beginning of symptoms 0.52 (0.36–0.75) 0.001 Yes, > 10days from beginning of symptoms 0.63 (0.26–1.50) 0.294 Systemic corticosteroids 1.14 (1.00–1.29) 0.047 Ventilatory support No 1 (ref.) Non-invasive 4.09 (3.41–4.89) < 0.001 Invasive 6.36 (4.99–8.11) < 0.001 Wave#severe dependenced1.50 (1.14–1.97) 0.004 Wave#systemic corticosteroidse1.59 (1.09–2.33) 0.017 Constant 0.31 (0.15–0.64) 0.002 Table 4. Effect of second wave on mortality in patients ≥ 70years hospitalized for COVID-19. AOR: adjusted odds ratio; ARR: adjusted risk ratio calculated with delta-method standard errors for the wave covariate. Severe dependence (Barthel index < 60) Treatment with systemic corticosteroids AOR (95% CI AOR) ARR (95% CI ARR) No No 0.35 (0.24–0.52) 0.54 (0.42–0.69) No Yes 0.56 (0.45–0.69) 0.73 (0.65–0.82) Yes No 0.52 (0.37–0.75) 0.73 (0.60–0.88) Yes Yes 0.83 (0.67–1.04) 0.92 (0.84–1.02) 9 Vol.:(0123456789) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ Patients were treated at their attending physician’s discretion according to local protocols and clinical judgment. Patients included in open-label clinical trials could be included in the registry provided that all information about treatment was available. Due to its observational nature, the registry caused no inconvenience to the patients included. Data collection Clinical investigators all over the country collected data from medical records using a standardized online data capture system (DCS). The DCS includes both a database manager and the set of procedures for the verification of data. Patient identifiable data are dissociated and pseudonymized using an alphanumeric sequence and each researcher keeps a protected registry (patient log) for the purpose of data verification and quality control. The database platform is hosted in a secure server and both the database and each client–server transfer are encrypted. The pseudonymization system allows for safeguarding patient privacy while also complying with ethical considerations and data protection regulations. Data on more than 300 variables are collected retrospectively after patient discharge and grouped under various headings: inclusion criteria, epidemiological data, RT-PCR and serology data, prior comorbidities and medication history, findings (symptoms and physical examination) at admission, laboratory (blood gases, metabolic panel, complete blood count, coagulation) and diagnostic imaging tests at admission, additional data at seven days after admission or at admission to the intensive care unit, pharmacological treatment (antiviral drugs, immunomodulators, antibiotics) and ventilatory support during the hospitalization, complications during the hospitalization, and progress after discharge and/or 30days from diagnosis. The variables in the registry have previously been described6. A number of secondary variables were calculated from the primary variables in the registry. Some qualitative variables were classified into binary categories whereas some quantitative variables were categorized as normal or abnormal; age was categorized into decades. Arterial stiffness was defined as a pulse pressure greater than or equal to 60 mmHg29. Blood glucose levels were categorized into three groups according to standard glycemic Figure2. Several characteristics of patients, differences in COVID-19 severity and management could confound the estimation of mortality between waves. After adjusting for these factors, differences in the outcome could be explained by unmeasured patient characteristics and COVID-19 severity factors, or uncontrolled COVID-19 management. 16 Vol:.(1234567890) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ Ana Andréu Arnanz37, Octavio Arce García37, Marta Bajo González37, Pablo Borque Sanz37, Alberto Cozar Llisto37, Sonia de Pedro Baena37, Beatriz Del Hoyo Cuenda37, Martin Fabregate‑Fuente37, María Alejandra Gamboa Osorio37, Isabel García Sánchez37, Andrés González García37, Oscar Alberto López Cisneros37, Luis Manzano37, Miguel Martínez‑Lacalzada37, Borja Merino Ortiz37, Jimena Rey‑García37, Elisa Riera González37, Cristina Sánchez Díaz37, Grisell Starita Fajardo37, Cecilia Suárez Carantoña37, Adrian Viteri‑Noël37, Svetlana Zhilina Zhilina37, Gloria María Alonso Claudio38, Víctor Barreales Rodríguez38, Cristina Carbonell Muñoz38, Adela Carpio Pérez38, María Victoria Coral Orbes38, Daniel Encinas Sánchez38, Sandra Inés Revuelta38, Miguel Marcos Martín38, José Ignacio Martín González38, José Ángel Martín Oterino38, Leticia Moralejo Alonso38, Sonia Peña Balbuena38, María Luisa Pérez García38, Ana Ramon Prados38, Beatriz Rodríguez‑Alonso38, Ángela Romero Alegría38, Maria Sanchez Ledesma38, Rosa Juana Tejera Pérez38, Julio César Blázquez Encinar39, Carmen Martínez Cilleros40, Isabel Jiménez Martínez40, Teresa García Delange40, Raquel Fernández González41, Amara Gonzalez Noya41, Carlos Hernández Ceron41, Isabel Izuzquiza Avanzini41, Ana Latorre Diez41, Pablo López Mato41, Ana María Lorenzo Vizcaya41, Daniel Peña Benítez41, Milagros María Peña Zemsch41, Lucía Pérez Expósito41, Marta Pose Bar41, Lara Rey González41, Laura Rodrigo Lara41, Dafne Cabañero42, María Calabuig Ballester42, Pascual Císcar Fernández42, Ricardo Gil Sánchez42, Marta Jiménez Escrig42, Cristina Marín Amela42, Laura Parra Gómez42, Carlos Puig Navarro42, José Antonio Todolí Parra42, Carlota Tuñón de Almeida43, María Esther Fraile Villarejo43, Victoria Palomar Calvo43, Sara Pintos Otero43, Beatriz García López43, Carlos Aldasoro Frías43, Víctor Madrid Romero43, Luis Arribas Pérez43, Emilia Martínez Velado43, Raquel Aranega González44, Ramon Boixeda44, Javier Fernández Fernández44, Carlos Lopera Mármol44, Marta Parra Navarro44, Ainhoa Rex Guzmán44, Aleix Serrallonga Fustier44, José López Castro45, Manuel Lorenzo López Reboiro45, Cristina Sardiña González45, Enrique Rodilla Sala46, Jose María Pascual Izuel46, Zineb Karroud Zamrani46, Hortensia Alvarez Diaz47, Tamara Dalama Lopez47, Estefania Martul Pego47, Carmen Mella Pérez47, Ana Pazos Ferro47, Sabela Sánchez Trigo47, Dolores Suarez Sambade47, Maria Trigas Ferrin47, Maria del Carmen Vázquez Friol47, Laura Vilariño Maneiro47, Begoña Cortés Rodríguez48, María Esther Guisado Espartero49, Lorena Montero Rivas49, Maria de la Sierra Navas Alcántara49, Raimundo Tirado‑Miranda49, Marta Nataya Solís Marquínez50, Víctor Arenas García50, Demelsa Blanco Suárez50, Natalia García Arenas50, Paula Martínez García50, David Castrodá Copa50, Andrea Álvarez García50, Jaime Casal Álvarez50, María Jose Menéndez Calderón50, Raquel García Noriega50, María Caño Rubia50, Joaquin Llorente García50, Luis Trapiella Martínez50, José Ferreiro Celeiro50, Diego Eduardo Olivo Aguilar50, Irene Maderuelo Riesco50, Juan Valdés Bécares50, Alba Barragán Mateos50, Andrés Astur Treceño García50, Joaquín Delgado Casamayor51, Diego García Silvera51, Andrea Afonso Díaz51, Carolina Hernández Carballo51, Alicia Tejera51, María José Monedero Prieto51, María Blanca Monereo Muñoz51, José Manuel Del Arco Delgado51, Daniel Rodríguez Díaz51, Marta Bethencourt Feria51, Francisco Javier Herrera Herrera51, María de la Luz Padilla Salazar51, Rubén Hernández Luis51, Eduardo Mauricio Calderón Ledezma51, María del Mar López Gámez51, Laura Torres Hernández51, Sara Castaño Pérez51, Selena Gala Aguilera García51, Guillermo Castro Gainett51, Alba Gómez Hidalgo51, Julia Marfil Daza51, Marcelino Hayek Peraza51, Reyes Aparicio Santos52, Máximo Bernabeu‑Wittel52, Santiago Rodríguez Suárez52, María Nieto52, Luis Giménez Miranda52, Rosa María Gámez Mancera52, Fátima Espinosa Torre52, Carlos Hernandez Quiles52, Concepción Conde Guzmán52, Juan Delgado de la Cuesta52, Jara Eloisa Ternero Vega52, María del Carmen López Ríos52, Pablo Díaz Jiménez52, Bosco Baron Franco52, Carlos Jiménez de Juan52, Sonia Gutiérrez Rivero52, Julia Lanseros Tenllado52, Verónica Alfaro Lara52, Aurora González Estrada52, Javier Ena53, José Enrique Gómez Segado53, Ruth Gonzalez Ferrer54, Virginia Gracia Lorenzo54, Raquel Monsalvo Arroyo54, Marcos Guzmán García55, Francisco Javier Vicente Hernández55, Ángel Luis Martínez González56, Beatriz Vicente Montes56, Rosario María García Die56, 17 Vol.:(0123456789) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ Alberto Muela Molinero56, Manuel Martín Regidor56, Raquel Rodríguez Díez56, Bárbara Hernández Sierra57, Luis Felipe Díez García57, Iris El Attar Acedo57, Carmen Mar Sánchez Cano57, Virginia Herrero García58, Berta Román Bernal58, Júlia Calvo Jiménez59, Emmanuel Coloma Bazán59, Aina Capdevila Reniu59, Joan Ribot Grabalosa59, Joaquim Fernández Solà59, Irene Carbonell De Boulle59, Cristina Gabara Xancó59, Olga Rodríguez Núñez59, Carlos Jorge Ripper60, Anyuli Gracia Gutiérrez61, Leticia Esther Royo Trallero61, Marta Fernández‑Ayala Novo62, José Javier Napal Lecumberri62, Nuria Puente Ruiz62, Jose Riancho62, Isabel Sampedro García62, Pablo Conde Baena63, Joaquín Escobar Sevilla63, Laura Gallo Padilla63, Patricia Gómez Ronquillo63, Pablo González Bustos63, María Navío Botías63, Jessica Ramírez Taboada63, Mar Rivero Rodríguez63, Víctor Asensi Alvarez64, Noelia Morán Suárez64, Sara Rodríguez Suárez64, Silvia Suárez Díaz64, Lucia Suárez Pérez64, Maria Folgueras Gómez64, Claudia Moran Castaño64, Lucía Meijide Rodríguez64, Carlos Vázquez64, Itxasne Cabezón Estévanez64, Carmen Yllera Gutiérrez64, Maria Martinez Sela64, Sara Fuente Cosío65, César Manuel Gallo Álvaro65, Julia Lobo García65, Antía Pérez Piñeiro65, Yolanda Casillas Viera66, Lucía Cayuela Rodríguez66, Carmen de Juan Alvarez66, Gema Flox Benitez66, Laura García Escudero66, Juan Martin Torres66, Patricia Moreira Escriche66, Susana Plaza Canteli66, M. Carmen Romero Pérez66, Jorge Andrés Soler67, Marián Bennasar Remolar67, Alejandro Cardenal Álvarez67, Daniela Díaz Carlotti67, María José Esteve Gimeno67, Sergio Fabra Juana67, Paula García López67, María Teresa Guinot Soler67, Daniela Palomo de la Sota67, Guillem Pascual Castellanos67, Ignacio Pérez Catalán67, Celia Roig Martí67, Paula Rubert Monzó67, Javier Ruiz Padilla67, Nuria Tornador Gaya67, Jorge Usó Blasco67, M. Angeles Martinez Pascual68, Leyre Jorquer Vidal68, Ana Alberich Conesa69, Mari Cruz Almendros Rivas69, Miquel Hortos Alsina69, José Marchena Romero69, Anabel Martin‑Urda Diez‑Canseco69, Francisco Amorós Martínez70, Erika Ascuña Vásquez70, José Carlos Escribano Stablé70, Adriana Hernández Belmonte70, Ana Maestre Peiró70, Raquel Martínez Goñi70, M. Carmen Pacheco Castellanos70, Bernardino Soldan Belda70, David Vicente Navarro70, Ana Suárez Lombraña71, Jon Cabrejas Ugartondo72, Ana Belén Mancebo Plaza72, Arturo Noguerado Asensio72, Bethania Pérez Alves72, Natalia Vicente López72, Marta León Téllez73, Francisco Epelde74, Isabel Torrente74, Pablo Guisado Vasco75, Ana Roda Santacruz75, Ana Valverde Muñoz75, Mª José Esteban Giner76, Alejo Erice Calvo‑Sotelo77, Eva García Sardón78, Javier Galán González78, Luis Gámez Salazar78, Angela Agea Garcia78, Itziar Montero Días78, Alvaro Santaella Gomez78, Marta Correa Matos78, Selene Núñez Gaspar78, Antonio González Nieto78, Raquel Gómez Méndez79, Ana Rodríguez Álvarez79, Onán Pérez Hernández80, Alina Pérez Ramírez80, María Candelaria Martín González80, Miguel Nicolas Navarrete Lorite80, Lourdes González Navarrete80, Julio Cesar Alvisa Negrin80, José Fernando Armas González80, Iballa Jiménez80, Paula Ortega Toledo80, Esther Martin Ponce80, Xjoylin Teresita Egües Torres81, Sara Gutiérrez González81, Cristina Novoa Fernández81, Pablo Tellería Gómez81, Oriol Alonso Gisbert82, Mercé Blázquez Llistosella82, Pere Comas Casanova82, Angels Garcia Flores82, Anna Garcia Hinojo82, Ana Inés Méndez Martínez82, Maria del Carmen Nogales Nieves82, Agnés Rivera Austrui82, Alberto Zamora Cervantes82, Vanesa Alende Castro83, Ana María Baz Lomba83, Ruth Brea Aparicio83, Marta Fernández Morales83, Jesús Manuel Fernández Villar83, María Teresa López Monteagudo83, Cristina Pérez García83, Lorena Rodríguez Ferreira83, Diana Sande Llovo83, Maria Begoña Valle Feijoo83, Juan Antonio Montes Romero84, Jose Luis Serrano Carrillo de Albornoz84, Manuel Jesus Soriano Pérez84, Encarna Sánchez Martín84, Thamar Capel Astrua85, Paola Tatiana Garcia Giraldo85, Maria Jesús González Juárez85, Victoria Marquez Fernandez85, Ada Viviana Romero Echevarry85, José F. Varona Arche86, María Gloria Rojano Rivero87, Adrián Montaño Martínez88, Reina Valle Bernad89, Cristina Limia89, Cristina Amado Fernández89, Andrea Tejero Fernández89, Lucia Paz Fajardo89, Tomás de Vega Santos89, Antonio López Ruiz90 & Hector Meijide Míguez91 26Hospital General Uuniversitario de Elda, Alicante, Spain. 27H. de Pozoblanco, Córdoba, Spain. 28Hospital. U. Río Hortega, Valladolid, Spain. 29Hospital Nuestra Señora del Prado, Talavera de la Reina, Toledo, Spain. 30H. 18 Vol:.(1234567890) Scientific Reports | (2023) 13:17731 | https://doi.org/10.1038/s41598-023-42735-5 www.nature.com/scientificreports/ de Urduliz Alfredo Espinosa, Vizcaya, Spain. 31H. Virgen de la Salud, Toledo, Spain. 32H. U. Son Llàtzer, Palma de Mallorca, Spain. 33H. Santa Marina, Bilbao, Spain. 34H. Juan Ramón Jiménez, Huelva, Spain. 35H. San Pedro, Logroño, La Rioja, Spain. 36H. del Henares, Coslada, Madrid, Spain. 37H. U. Ramón y Cajal, Madrid, Spain. 38C. A. U. de Salamanca, Salamanca, Spain. 39H. U. Torrevieja, Alicante, Spain. 40H. HLA Moncloa, Madrid, Spain. 41C. H. U. Ourense, Ourense, Spain. 42H. U. La Fe. Valencia, Valencia, Spain. 43C. Asistencial de Zamora, Zamora, Spain. 44H. de Mataró, Barcelona, Spain. 45H. Público de Monforte de Lemos, Lugo, Spain. 46H. de Sagunto, Valencia, Spain. 47C. H. U. de Ferrol, A Coruña, Spain. 48H. Alto Guadalquivir, Andújar, Jaén, Spain. 49H. Infanta Margarita, Cabra, Córdoba, Spain. 50H. U. San Agustin, Avilés, Asturias, Spain. 51H. Univ. Ntra. Sra. Candelaria, Sta. Cruz de Tenerife, Spain. 52H. U. Virgen del Rocío, Seville, Spain. 53H. Marina Baixa, Villajoyosa, Alicante, Spain. 54H. del Tajo, Aranjuez, Madrid, Spain. 55H. San Juan de la Cruz, Úbeda, Jaén, Spain. 56C. Asist. Univ. de León, León, Spain. 57H. Torrecárdenas, Almería, Spain. 58H. Dr. José Molina Orosa, Lanzarote, Arrecife, Spain. 59H. Clinic Barcelona, Barcelona, Spain. 60H. Insular de Gran Canaria, Las Palmas G. C., Spain. 61H. General Defensa, Zaragoza, Spain. 62H. U. Marqués de Valdecilla, Santander, Spain. 63H. U. Virgen de las Nieves, Granada, Spain. 64H. U. C. de Asturias, Oviedo, Spain. 65H. Valle del Nalón, Riaño-Langreo, Asturias, Spain. 66H. U. Severo Ochoa, Leganés, Madrid, Spain. 67H. G. U. de Castellón, Castelló de La Plana, Spain. 68H. Francesc de Borja, Gandía, Valencia, Spain. 69H. de Palamós, Gerona, Spain. 70H. U. del Vinalopó, Elche, Alicante, Spain. 71H. Platón, Barcelona, Spain. 72H. U. del Sureste, Arganda del Rey, Madrid, Spain. 73H. Santa Bárbara, Soria, Spain. 74H. Parc Tauli, Sabadell, Barcelona, Spain. 75H. U. Quironsalud Madrid, Madrid, Spain. 76H. Virgen de los Lirios, Alcoy, Alicante, Spain. 77H. Asepeyo Coslada, Madrid, Spain. 78H. San Pedro de Alcántara, Cáceres, Spain. 79H. U. Lucus Augusti, Lugo, Spain. 80H. U. de Canarias, Sta. Cruz de Tenerife, Spain. 81H. Clínico Universitario de Valladolid, Valladolid, Spain. 82H. Comarcal de Blanes, Girona, Spain. 83H. do Salnes. Vilagarcía de Arousa, Pontevedra, Spain. 84H. de Poniente, El Ejido, Almería, Spain. 85H. Virgen del Mar, Madrid, Spain. 86H. U. HM Montepríncipe, Madrid, Spain. 87H. Infanta Elena, Huelva, Spain. 88H. de Montilla, Córdoba, Spain. 89H. Sierrallana, Torrelavega, Cantabria, Spain. 90H. de la Axarquía, Vélez-Málaga, Málaga, Spain. 91H. Quironsalud A Coruña, A Coruña, Spain.