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

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.

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study.

Background: The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24-48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection. Methods: We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients. Results: The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr.The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients. Conclusions: The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves. Funding: University of Vienna.

Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry.

COVID-19 is responsible for high mortality, but robust machine learning-based predictors of mortality are lacking. To generate a model for predicting mortality in patients hospitalized with COVID-19 using Gradient Boosting Decision Trees (GBDT). The Spanish SEMI-COVID-19 registry includes 24,514 pseudo-anonymized cases of patients hospitalized with COVID-19 from 1 February 2020 to 5 December 2021. This registry was used as a GBDT machine learning model, employing the CatBoost and BorutaShap classifier to select the most relevant indicators and generate a mortality prediction model by risk level, ranging from 0 to 1. The model was validated by separating patients according to admission date, using the period 1 February to 31 December 2020 (first and second waves, pre-vaccination period) for training, and 1 January to 30 November 2021 (vaccination period) for the test group. An ensemble of ten models with different random seeds was constructed, separating 80% of the patients for training and 20% from the end of the training period for cross-validation. The area under the receiver operating characteristics curve (AUC) was used as a performance metric. Clinical and laboratory data from 23,983 patients were analyzed. CatBoost mortality prediction models achieved an AUC performance of 84.76 (standard deviation 0.45) for patients in the test group (potentially vaccinated patients not included in model training) using 16 features. The performance of the 16-parameter GBDT model for predicting COVID-19 hospital mortality, although requiring a relatively large number of predictors, shows a high predictive capacity.

Influence of smoking history on the evolution of hospitalized in COVID-19 positive patients: Results from the SEMI-COVID-19 registry.

Introduction: Smoking can play a key role in SARS-CoV-2 infection and in the course of the disease. Previous studies have conflicting or inconclusive results on the prevalence of smoking and the severity of the coronavirus disease (COVID-19). Methods: Observational, multicenter, retrospective cohort study of 14,260 patients admitted for COVID-19 in Spanish hospitals between February and September 2020. Their clinical characteristics were recorded and the patients were classified into a smoking group (active or former smokers) or a non-smoking group (never smokers). The patients were followed up to one month after discharge. Differences between groups were analysed. A multivariate logistic regression and Kapplan Meier curves analysed the relationship between smoking and in-hospital mortality. Results: The median age was 68.6 (55.8-79.1) years, with 57.7% of males. Smoking patients were older (69.9 (59.6-78.0 years)), more frequently male (80.3%) and with higher Charlson index (4 (2-6)) than non-smoking patients. Smoking patients presented a worse evolution, with a higher rate of admission to the intensive care unit (ICU) (10.4 vs. 8.1%), higher in-hospital mortality (22.5 vs. 16.4%) and readmission at one month (5.8 vs. 4.0%) than in non-smoking patients. After multivariate analysis, smoking remained associated with these events. Conclusions: Active or past smoking is an independent predictor of poor prognosis in patients with COVID-19. It is associated with higher ICU admissions and in-hospital mortality.

Introducción: El tabaquismo puede tener un papel importante en la infección por SARS-CoV-2 y en el curso de la enfermedad. Los estudios previos muestran resultados contradictorios o no concluyentes sobre la prevalencia de fumar y la severidad en la enfermedad por coronavirus (COVID-19). Material y métodos: Estudio de cohortes observacional, multicéntrico y retrospectivo de 14.260 pacientes que ingresaron por COVID-19 en hospitales españoles desde febrero a septiembre de 2020. Se registraron sus características clínicas y se clasificaron en el grupo con tabaquismo si tabaquismo activo o previo o en el grupo sin tabaquismo si nunca habían fumado. Se realizó un seguimiento hasta un mes después del alta. Se analizaron las diferencias entre grupos. La relación entre tabaquismo y mortalidad intrahospitalaria se valoró mediante una regresión logística multivariante y curvas de Kapplan Meier. Resultados: La mediana de edad fue 68,6 (55,8­79,1) años, con un 57,7% de varones. El grupo con tabaquismo presentó mayor edad (69,9 (59,6­78,0 años)), predominio masculino (80,3%) y mayor índice de Charlson (4 (2−6)). La evolución fue peor en estos pacientes, con una mayor tasa de ingreso en UCI (10,4 vs 8,1%), mayor mortalidad intrahospitalaria (22,5 vs 16,4%) y reingreso al mes (5,8 vs 4,0%) que el grupo sin tabaquismo. Tras el análisis multivariante, el tabaquismo permanecía asociado a estos eventos. Conclusiones: El tabaquismo de forma activa o pasada es un factor predictor independiente de mal pronóstico en los pacientes con COVID-19, estando asociada a mayor probabilidad de ingreso en UCI y a mayor mortalidad intrahospitalaria.

Differences in clinical features and mortality in very old unvaccinated patients (≥ 80 years) hospitalized with COVID-19 during the first and successive waves from the multicenter SEMI-COVID-19 Registry (Spain).

BACKGROUND: Old age is one of the most important risk factors for severe COVID-19. Few studies have analyzed changes in the clinical characteristics and prognosis of COVID-19 among older adults before the availability of vaccines. This work analyzes differences in clinical features and mortality in unvaccinated very old adults during the first and successive COVID-19 waves in Spain. METHODS: This nationwide, multicenter, retrospective cohort study analyzes unvaccinated patients ≥ 80 years hospitalized for COVID-19 in 150 Spanish hospitals (SEMI-COVID-19 Registry). Patients were classified according to whether they were admitted in the first wave (March 1-June 30, 2020) or successive waves (July 1-December 31, 2020). The endpoint was all-cause in-hospital mortality, expressed as the case fatality rate (CFR). RESULTS: Of the 21,461 patients hospitalized with COVID-19, 5,953 (27.7%) were ≥ 80 years (mean age [IQR]: 85.6 [82.3-89.2] years). Of them, 4,545 (76.3%) were admitted during the first wave and 1,408 (23.7%) during successive waves. Patients hospitalized in successive waves were older, had a greater Charlson Comorbidity Index and dependency, less cough and fever, and met fewer severity criteria at admission (qSOFA index, PO2/FiO2 ratio, inflammatory parameters). Significant differences were observed in treatments used in the first (greater use of antimalarials, lopinavir, and macrolides) and successive waves (greater use of corticosteroids, tocilizumab and remdesivir). In-hospital complications, especially acute respiratory distress syndrome and pneumonia, were less frequent in patients hospitalized in successive waves, except for heart failure. The CFR was significantly higher in the first wave (44.1% vs. 33.3%; -10.8%; p < 0.001) and was higher among patients ≥ 95 years (54.4% vs. 38.5%; -15.9%; p < 0.001). After adjustments to the model, the probability of death was 33% lower in successive waves (OR: 0.67; 95% CI: 0.57-0.79). CONCLUSIONS: Mortality declined significantly between the first and successive waves in very old unvaccinated patients hospitalized with COVID-19 in Spain. This decline could be explained by a greater availability of hospital resources and more effective treatments as the pandemic progressed, although other factors such as changes in SARS-CoV-2 virulence cannot be ruled out.

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.

While COVID-19 vaccines have saved millions of lives, new variants, waxing immunity, unequal rollout and relaxation of mitigation strategies mean that the pandemic will keep on sending shockwaves across healthcare systems. In this context, it is crucial to equip clinicians with tools to triage COVID-19 patients and forecast who will experience the worst forms of the disease. Prediction models based on artificial intelligence could help in this effort, but the task is not straightforward. Indeed, the pandemic is defined by ever-changing factors which artificial intelligence needs to cope with. To be useful in the clinic, a prediction model should make accurate prediction regardless of hospital location, viral variants or vaccination and immunity statuses. It should also be able to adapt its output to the level of resources available in a hospital at any given time. Finally, these tools need to seamlessly integrate into clinical workflows to not burden clinicians. In response, Klén et al. built CODOP, a freely available prediction algorithm that calculates the death risk of patients hospitalized with COVID-19 (https://gomezvarelalab.em.mpg.de/codop/). This model was designed based on biochemical data from routine blood analyses of COVID-19 patients. Crucially, the dataset included 30,000 individuals from 150 hospitals in Spain, the United States, Honduras, Bolivia and Argentina, sampled between March 2020 and February 2022 and carrying most of the main COVID-19 variants (from the original Wuhan version to Omicron). CODOP can predict the death or survival of hospitalized patients with high accuracy up to nine days before the clinical outcome occurs. These forecasting abilities are preserved independently of vaccination status or viral variant. The next step is to tailor the model to the current pandemic situation, which features increasing numbers of infected people as well as accumulating immune protection in the overall population. Further development will refine CODOP so that the algorithm can detect who will need hospitalisation in the next 24 hours, and who will need admission in intensive care in the next two days. Equipping primary care settings and hospitals with these tools will help to restore previous standards of health care during the upcoming waves of infections, particularly in countries with limited resources.

Clusters of inflammation in COVID-19: descriptive analysis and prognosis on more than 15,000 patients from the Spanish SEMI-COVID-19 Registry.

Uncontrolled inflammation following COVID-19 infection is an important characteristic of the most seriously ill patients. The present study aims to describe the clusters of inflammation in COVID-19 and to analyze their prognostic role. This is a retrospective observational study including 15,691 patients with a high degree of inflammation. They were included in the Spanish SEMI-COVID-19 registry from March 1, 2020 to May 1, 2021. The primary outcome was in-hospital mortality. Hierarchical cluster analysis identified 7 clusters. C1 is characterized by lymphopenia, C2 by elevated ferritin, and C3 by elevated LDH. C4 is characterized by lymphopenia plus elevated CRP and LDH and frequently also ferritin. C5 is defined by elevated CRP, and C6 by elevated ferritin and D-dimer, and frequently also elevated CRP and LDH. Finally, C7 is characterized by an elevated D-dimer. The clusters with the highest in-hospital mortality were C4, C6, and C7 (17.4% vs. 18% vs. 15.6% vs. 36.8% vs. 17.5% vs. 39.3% vs. 26.4%). Inflammation clusters were found as independent factors for in-hospital mortality. In detail and, having cluster C1 as reference, the model revealed a worse prognosis for all other clusters: C2 (OR = 1.30, p = 0.001), C3 (OR = 1.14, p = 0.178), C4 (OR = 2.28, p < 0.001), C5 (OR = 1.07, p = 0.479), C6 (OR = 2.29, p < 0.001), and C7 (OR = 1.28, p = 0.001). We identified 7 groups based on the presence of lymphopenia, elevated CRP, LDH, ferritin, and D-dimer at the time of hospital admission for COVID-19. Clusters C4 (lymphopenia + LDH + CRP), C6 (ferritin + D-dimer), and C7 (D-dimer) had the worst prognosis in terms of in-hospital mortality.

The importance of association of comorbidities on COVID-19 outcomes: a machine learning approach.

BACKGROUND: The individual influence of a variety of comorbidities on COVID-19 patient outcomes has already been analyzed in previous works in an isolated way. We aim to determine if different associations of diseases influence the outcomes of inpatients with COVID-19. METHODS: Retrospective cohort multicenter study based on clinical practice. Data were taken from the SEMI-COVID-19 Registry, which includes most consecutive patients with confirmed COVID-19 hospitalized and discharged in Spain. Two machine learning algorithms were applied in order to classify comorbidities and patients (Random Forest -RF algorithm, and Gaussian mixed model by clustering -GMM-). The primary endpoint was a composite of either, all-cause death or intensive care unit admission during the period of hospitalization. The sample was randomly divided into training and test sets to determine the most important comorbidities related to the primary endpoint, grow several clusters with these comorbidities based on discriminant analysis and GMM, and compare these clusters. RESULTS: A total of 16,455 inpatients (57.4% women and 42.6% men) were analyzed. According to the RF algorithm, the most important comorbidities were heart failure/atrial fibrillation (HF/AF), vascular diseases, and neurodegenerative diseases. There were six clusters: three included patients who met the primary endpoint (clusters 4, 5, and 6) and three included patients who did not (clusters 1, 2, and 3). Patients with HF/AF, vascular diseases, and neurodegenerative diseases were distributed among clusters 3, 4 and 5. Patients in cluster 5 also had kidney, liver, and acid peptic diseases as well as a chronic obstructive pulmonary disease; it was the cluster with the worst prognosis. CONCLUSION: The interplay of several comorbidities may affect the outcome and complications of inpatients with COVID-19.

Influence of smoking history on the evolution of hospitalized in COVID-19 positive patients: results from the SEMI-COVID-19 registry. / Influencia de la historia de tabaquismo en la evolución de la hospitalización en pacientes COVID-19 positivos: datos del registro SEMI-COVID-19.

INTRODUCTION: Smoking can play a key role in SARS-CoV-2 infection and in the course of the disease. Previous studies have conflicting or inconclusive results on the prevalence of smoking and the severity of the coronavirus disease (COVID-19). METHODS: Observational, multicenter, retrospective cohort study of 14,260 patients admitted for COVID-19 in Spanish hospitals between February and September 2020. Their clinical characteristics were recorded and the patients were classified into a smoking group (active or former smokers) or a non-smoking group (never smokers). The patients were followed up to one month after discharge. Differences between groups were analyzed. A multivariate logistic regression and Kapplan Meier curves analyzed the relationship between smoking and in-hospital mortality. RESULTS: The median age was 68.6 (55.8-79.1) years, with 57.7% of males. Smoking patients were older (69.9 [59.6-78.0 years]), more frequently male (80.3%) and with higher Charlson index (4 [2-6]) than non-smoking patients. Smoking patients presented a worse evolution, with a higher rate of admission to the intensive care unit (ICU) (10.4 vs 8.1%), higher in-hospital mortality (22.5 vs. 16.4%) and readmission at one month (5.8 vs. 4.0%) than in non-smoking patients. After multivariate analysis, smoking remained associated with these events. CONCLUSIONS: Active or past smoking is an independent predictor of poor prognosis in patients with COVID-19. It is associated with higher ICU admissions and in-hospital mortality.

Influence of chronic use of corticosteroids and calcineurin inhibitors on COVID-19 clinical outcomes: analysis of a nationwide registry.

OBJECTIVES: The aim of this study was to analyze whether subgroups of immunosuppressive (IS) medications conferred different outcomes in COVID-19. METHODS: The study involved a multicenter retrospective cohort of consecutive immunosuppressed patients (ISPs) hospitalized with COVID-19 from March to July, 2020. The primary outcome was in-hospital mortality. A propensity score-matched (PSM) model comparing ISP and non-ISP was planned, as well as specific PSM models comparing individual IS medications associated with mortality. RESULTS: Out of 16 647 patients, 868 (5.2%) were on chronic IS therapy prior to admission and were considered ISPs. In the PSM model, ISPs had greater in-hospital mortality (OR 1.25, 95% CI 0.99-1.62), which was related to a worse outcome associated with chronic corticoids (OR 1.89, 95% CI 1.43-2.49). Other IS drugs had no repercussions with regard to mortality risk (including calcineurin inhibitors (CNI); OR 1.19, 95% CI 0.65-2.20). In the pre-planned specific PSM model involving patients on chronic IS treatment before admission, corticosteroids were associated with an increased risk of mortality (OR 2.34, 95% CI 1.43-3.82). CONCLUSIONS: Chronic IS therapies comprise a heterogeneous group of drugs with different risk profiles for severe COVID-19 and death. Chronic systemic corticosteroid therapy is associated with increased mortality. On the contrary, CNI and other IS treatments prior to admission do not seem to convey different outcomes.

Estimation of Admission D-dimer Cut-off Value to Predict Venous Thrombotic Events in Hospitalized COVID-19 Patients: Analysis of the SEMI-COVID-19 Registry.

BACKGROUND: Venous thrombotic events (VTE) are frequent in COVID-19, and elevated plasma D-dimer (pDd) and dyspnea are common in both entities. OBJECTIVE: To determine the admission pDd cut-off value associated with in-hospital VTE in patients with COVID-19. METHODS: Multicenter, retrospective study analyzing the at-admission pDd cut-off value to predict VTE and anticoagulation intensity along hospitalization due to COVID-19. RESULTS: Among 9386 patients, 2.2% had VTE: 1.6% pulmonary embolism (PE), 0.4% deep vein thrombosis (DVT), and 0.2% both. Those with VTE had a higher prevalence of tachypnea (42.9% vs. 31.1%; p = 0.0005), basal O2 saturation <93% (45.4% vs. 33.1%; p = 0.0003), higher at admission pDd (median [IQR]: 1.4 [0.6-5.5] vs. 0.6 [0.4-1.2] µg/ml; p < 0.0001) and platelet count (median [IQR]: 208 [158-289] vs. 189 [148-245] platelets × 109/L; p = 0.0013). A pDd cut-off of 1.1 µg/ml showed specificity 72%, sensitivity 49%, positive predictive value (PPV) 4%, and negative predictive value (NPV) 99% for in-hospital VTE. A cut-off value of 4.7 µg/ml showed specificity of 95%, sensitivity of 27%, PPV of 9%, and NPV of 98%. Overall mortality was proportional to pDd value, with the lowest incidence for each pDd category depending on anticoagulation intensity: 26.3% for those with pDd >1.0 µg/ml treated with prophylactic dose (p < 0.0001), 28.8% for pDd for patients with pDd >2.0 µg/ml treated with intermediate dose (p = 0.0001), and 31.3% for those with pDd >3.0 µg/ml and full anticoagulation (p = 0.0183). CONCLUSIONS: In hospitalized patients with COVID-19, a pDd value greater than 3.0 µg/ml can be considered to screen VTE and to consider full-dose anticoagulation.

Frequency, risk factors, and outcomes of hospital readmissions of COVID-19 patients.

To determine the proportion of patients with COVID-19 who were readmitted to the hospital and the most common causes and the factors associated with readmission. Multicenter nationwide cohort study in Spain. Patients included in the study were admitted to 147 hospitals from March 1 to April 30, 2020. Readmission was defined as a new hospital admission during the 30 days after discharge. Emergency department visits after discharge were not considered readmission. During the study period 8392 patients were admitted to hospitals participating in the SEMI-COVID-19 network. 298 patients (4.2%) out of 7137 patients were readmitted after being discharged. 1541 (17.7%) died during the index admission and 35 died during hospital readmission (11.7%, p = 0.007). The median time from discharge to readmission was 7 days (IQR 3-15 days). The most frequent causes of hospital readmission were worsening of previous pneumonia (54%), bacterial infection (13%), venous thromboembolism (5%), and heart failure (5%). Age [odds ratio (OR): 1.02; 95% confident interval (95% CI): 1.01-1.03], age-adjusted Charlson comorbidity index score (OR: 1.13; 95% CI: 1.06-1.21), chronic obstructive pulmonary disease (OR: 1.84; 95% CI: 1.26-2.69), asthma (OR: 1.52; 95% CI: 1.04-2.22), hemoglobin level at admission (OR: 0.92; 95% CI: 0.86-0.99), ground-glass opacification at admission (OR: 0.86; 95% CI:0.76-0.98) and glucocorticoid treatment (OR: 1.29; 95% CI: 1.00-1.66) were independently associated with hospital readmission. The rate of readmission after hospital discharge for COVID-19 was low. Advanced age and comorbidity were associated with increased risk of readmission.

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry.

AIM: To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). METHODS: Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. RESULTS: As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p = 0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.211, 95%CI 0.067-0.667, p = 0.008). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). CONCLUSIONS: Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality.

The Prognostic Value of Eosinophil Recovery in COVID-19: A Multicentre, Retrospective Cohort Study on Patients Hospitalised in Spanish Hospitals.

OBJECTIVES: A decrease in blood cell counts, especially lymphocytes and eosinophils, has been described in patients with serious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), but there is no knowledge of their potential role of the recovery in these patients' prognosis. This article aims to analyse the effect of blood cell depletion and blood cell recovery on mortality due to COVID-19. DESIGN: This work was a retrospective, multicentre cohort study of 9644 hospitalised patients with confirmed COVID-19 from the Spanish Society of Internal Medicine's SEMI-COVID-19 Registry. SETTING: This study examined patients hospitalised in 147 hospitals throughout Spain. PARTICIPANTS: This work analysed 9644 patients (57.12% male) out of a cohort of 12,826 patients ≥18 years of age hospitalised with COVID-19 in Spain included in the SEMI-COVID-19 Registry as of 29 May 2020. MAIN OUTCOME MEASURES: The main outcome measure of this work is the effect of blood cell depletion and blood cell recovery on mortality due to COVID-19. Univariate analysis was performed to determine possible predictors of death, and then multivariate analysis was carried out to control for potential confounders. RESULTS: An increase in the eosinophil count on the seventh day of hospitalisation was associated with a better prognosis, including lower mortality rates (5.2% vs. 22.6% in non-recoverers, OR 0.234; 95% CI, 0.154 to 0.354) and lower complication rates, especially regarding the development of acute respiratory distress syndrome (8% vs. 20.1%, p = 0.000) and ICU admission (5.4% vs. 10.8%, p = 0.000). Lymphocyte recovery was found to have no effect on prognosis. Treatment with inhaled or systemic glucocorticoids was not found to be a confounding factor. CONCLUSION: Eosinophil recovery in patients with COVID-19 who required hospitalisation had an independent prognostic value for all-cause mortality and a milder course.

Clinical Characteristics and Prognosis of COPD Patients Hospitalized with SARS-CoV-2.

Objective: To describe the characteristics and prognosis of patients with COPD admitted to the hospital due to SARS-CoV-2 infection. Methods: The SEMI-COVID registry is an ongoing retrospective cohort comprising consecutive COVID-19 patients hospitalized in Spain since the beginning of the pandemic in March 2020. Data on demographics, clinical characteristics, comorbidities, laboratory tests, radiology, treatment, and progress are collected. Patients with COPD were selected and compared to patients without COPD. Factors associated with a poor prognosis were analyzed. Results: Of the 10,420 patients included in the SEMI-COVID registry as of May 21, 2020, 746 (7.16%) had a diagnosis of COPD. Patients with COPD are older than those without COPD (77 years vs 68 years) and more frequently male. They have more comorbidities (hypertension, hyperlipidemia, diabetes mellitus, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, kidney failure) and a higher Charlson Comorbidity Index (2 vs 1, p<0.001). The mortality rate in COPD patients was 38.3% compared to 19.2% in patients without COPD (p<0.001). Male sex, a history of hypertension, heart failure, moderate-severe chronic kidney disease, presence of cerebrovascular disease with sequelae, degenerative neurological disease, dementia, functional dependence, and a higher Charlson Comorbidity Index have been associated with increased mortality due to COVID-19 in COPD patients. Survival was higher among patients with COPD who were treated with hydroxychloroquine (87.1% vs 74.9%, p<0.001) and with macrolides (57.9% vs 50%, p<0.037). Neither prone positioning nor non-invasive mechanical ventilation, high-flow nasal cannula, or invasive mechanical ventilation were associated with a better prognosis. Conclusion: COPD patients admitted to the hospital with SARS-CoV-2 infection have more severe disease and a worse prognosis than non-COPD patients.