icumonitoring.ch: a platform for short-term forecasting of intensive care unit occupancy during the COVID-19 epidemic in Switzerland.

In Switzerland, the COVID-19 epidemic is progressively slowing down owing to “social distancing” measures introduced by the Federal Council on 16 March 2020. However, the gradual ease of these measures may initiate a second epidemic wave, the length and intensity of which are difficult to anticipate. In this context, hospitals must prepare for a potential increase in intensive care unit (ICU) admissions of patients with acute respiratory distress syndrome. Here, we introduce icumonitoring.ch, a platform providing hospital-level projections for ICU occupancy. We combined current data on the number of beds and ventilators with canton-level projections of COVID-19 cases from two S-E-I-R models. We disaggregated epidemic projection in each hospital in Switzerland for the number of COVID-19 cases, hospitalisations, hospitalisations in ICU, and ventilators in use. The platform is updated every 3-4 days and can incorporate projections from other modelling teams to inform decision makers with a range of epidemic scenarios for future hospital occupancy.

Lung function and breathing patterns in hospitalised COVID-19 survivors: a review of post-COVID-19 Clinics.

INTRODUCTION: There is relatively little published on the effects of COVID-19 on respiratory physiology, particularly breathing patterns. We sought to determine if there were lasting detrimental effect following hospital discharge and if these related to the severity of COVID-19. METHODS: We reviewed lung function and breathing patterns in COVID-19 survivors > 3 months after discharge, comparing patients who had been admitted to the intensive therapy unit (ITU) (n = 47) to those who just received ward treatments (n = 45). Lung function included spirometry and gas transfer and breathing patterns were measured with structured light plethysmography. Continuous data were compared with an independent t-test or Mann Whitney-U test (depending on distribution) and nominal data were compared using a Fisher's exact test (for 2 categories in 2 groups) or a chi-squared test (for > 2 categories in 2 groups). A p-value of < 0.05 was taken to be statistically significant. RESULTS: We found evidence of pulmonary restriction (reduced vital capacity and/or alveolar volume) in 65.4% of all patients. 36.1% of all patients has a reduced transfer factor (TLCO) but the majority of these (78.1%) had a preserved/increased transfer coefficient (KCO), suggesting an extrapulmonary cause. There were no major differences between ITU and ward lung function, although KCO alone was higher in the ITU patients (p = 0.03). This could be explained partly by obesity, respiratory muscle fatigue, localised microvascular changes, or haemosiderosis from lung damage. Abnormal breathing patterns were observed in 18.8% of subjects, although no consistent pattern of breathing pattern abnormalities was evident. CONCLUSIONS: An "extrapulmonary restrictive" like pattern appears to be a common phenomenon in previously admitted COVID-19 survivors. Whilst the cause of this is not clear, the effects seem to be similar on patients whether or not they received mechanical ventilation or had ward based respiratory support/supplemental oxygen.

Hospitalization of Infants and Children Aged 0-4 Years with Laboratory-Confirmed COVID-19 - COVID-NET, 14 States, March 2020-February 2022.

The B.1.1.529 (Omicron) variant of SARS-CoV-2, the virus that causes COVID-19, has been the predominant circulating variant in the United States since late December 2021.* Coinciding with increased Omicron circulation, COVID-19-associated hospitalization rates increased rapidly among infants and children aged 0-4 years, a group not yet eligible for vaccination (1). Coronavirus Disease 19-Associated Hospitalization Surveillance Network (COVID-NET)† data were analyzed to describe COVID-19-associated hospitalizations among U.S. infants and children aged 0-4 years since March 2020. During the period of Omicron predominance (December 19, 2021-February 19, 2022), weekly COVID-19-associated hospitalization rates per 100,000 infants and children aged 0-4 years peaked at 14.5 (week ending January 8, 2022); this Omicron-predominant period peak was approximately five times that during the period of SARS-CoV-2 B.1.617.2 (Delta) predominance (June 27-December 18, 2021, which peaked the week ending September 11, 2021).§ During Omicron predominance, 63% of hospitalized infants and children had no underlying medical conditions; infants aged <6 months accounted for 44% of hospitalizations, although no differences were observed in indicators of severity by age. Strategies to prevent COVID-19 among infants and young children are important and include vaccination among currently eligible populations (2) such as pregnant women (3), family members, and caregivers of infants and young children (4).

Hospitalizations and Mortality From Non-SARS-CoV-2 Causes Among Medicare Beneficiaries at US Hospitals During the SARS-CoV-2 Pandemic.

Importance: The increased hospital mortality rates from non-SARS-CoV-2 causes during the SARS-CoV-2 pandemic are incompletely characterized. Objective: To describe changes in mortality rates after hospitalization for non-SARS-CoV-2 conditions during the COVID-19 pandemic and how mortality varies by characteristics of the admission and hospital. Design, Setting, and Participants: Retrospective cohort study from January 2019 through September 2021 using 100% of national Medicare claims, including 4626 US hospitals. Participants included 8 448 758 individuals with non-COVID-19 medical admissions with fee-for-service Medicare insurance. Main Outcomes and Measures: Outcome was mortality in the 30 days after admission with adjusted odds generated from a 3-level (admission, hospital, and county) logistic regression model that included diagnosis, demographic variables, comorbidities, hospital characteristics, and hospital prevalence of SARS-CoV-2. Results: There were 8 448 758 non-SARS-CoV-2 medical admissions in 2019 and from April 2020 to September 2021 (mean [SD] age, 73.66 [12.88] years; 52.82% women; 821 569 [11.87%] Black, 438 453 [6.34%] Hispanic, 5 351 956 [77.35%] White, and 307 218 [4.44%] categorized as other). Mortality in the 30 days after admission increased from 9.43% in 2019 to 11.48% from April 1, 2020, to March 31, 2021 (odds ratio [OR], 1.20; 95% CI, 1.19-1.21) in multilevel logistic regression analyses including admission and hospital characteristics. The increase in mortality was maintained throughout the first 18 months of the pandemic and varied by race and ethnicity (OR, 1.27; 95% CI, 1.23-1.30 for Black enrollees; OR, 1.25; 95% CI, 1.23-1.27 for Hispanic enrollees; and OR, 1.18; 95% CI, 1.17-1.19 for White enrollees); Medicaid eligibility (OR, 1.25; 95% CI, 1.24-1.27 for Medicaid eligible vs OR, 1.18; 95% CI, 1.16-1.18 for noneligible); and hospital quality score, measured on a scale of 1 to 5 stars with 1 being the worst and 5 being the best (OR, 1.27; 95% CI, 1.22-1.31 for 1 star vs OR, 1.11; 95% CI, 1.08-1.15 for 5 stars). Greater hospital prevalence of SARS-CoV-2 was associated with greater increases in odds of death from the prepandemic period to the pandemic period; for example, comparing mortality in October through December 2020 with October through December 2019, the OR was 1.44 (95% CI, 1.39-1.49) for hospitals in the top quartile of SARS-CoV-2 admissions vs an OR of 1.19 (95% CI, 1.16-1.22) for admissions to hospitals in the lowest quartile. This association was mostly limited to admissions with high-severity diagnoses. Conclusions and Relevance: The prolonged elevation in mortality rates after hospital admission in 2020 and 2021 for non-SARS-CoV-2 diagnoses contrasts with reports of improvement in hospital mortality during 2020 for SARS-CoV-2. The results of this cohort study suggest that, with the continued impact of SARS-CoV-2, it is important to implement interventions to improve access to high-quality hospital care for those with non-SARS-CoV-2 diseases.

The effect of COVID-19 lockdown on the incidence of emergency department visits due to injuries and the most typical fractures in 4 Finnish hospitals.

BACKGROUND AND PURPOSE: COVID-19 lockdowns have affected personal mobility and behavior worldwide. This study compared the number of emergency department (ED) visits due to injuries and typical low-energy fractures in Finland during the COVID-19 lockdown period in spring 2020 to the reference period in 2019. PATIENTS AND METHODS: The data was collected retrospectively from the electronic patient records of 4 hospitals covering 1/5 of the Finnish population. We included the patients who were admitted to a hospital ED due to any injury during the lockdown period (March 18-May 31, 2020) and the reference period (March 18-May 31, 2019). We compared the differences between the average daily ED admissions in the 2 years using the zero-inflated Poisson regression model. RESULTS: The overall number of ED visits due to injuries decreased by 16% (mean 134/day vs. 113/day, 95% CI -18 to -13). The number of ED visits due to wrist fractures decreased among women aged over 50 years by 40% (CI -59 to -9). Among women, the number of ED visits due to ankle fractures decreased by 32% (CI -52 to -5). The number of ED visits due to fractures of the upper end of the humerus decreased by 52% (CI -71 to -22) among women. The number of ED visits due to hip fractures increased by 2% (CI -16 to 24). INTERPRETATION: Restrictions in personal mobility decreased the number of ED visits due to injuries during the pandemic. The effect can mainly be seen as a decreased number of the most typical low-energy fractures among women. In contrast, lockdown restrictions had no effect on the number of hip fractures.

The Burden of Coronavirus Disease 2019-Related Cases, Hospitalizations, and Mortality Based on Vaccination Status and Mandated Mask Use: Statewide Data From Wisconsin and Narrative Review of the Literature.

BACKGROUND: Coronavirus disease 2019 (COVID-19) cases continue to surge in the United States with the emergence of new variants. Statewide variability and inconsistency in implementing risk mitigation strategies are widespread, particularly in regards to enforcing mask mandates and encouraging the public to become fully vaccinated. METHODS: This is a cross-sectional study conducted on July 31, 2021, utilizing publicly available data from the Wisconsin Department of Health Services. The authors abstracted data on total COVID-19-related cases, hospitalizations, and deaths in the state of Wisconsin. The primary objective was comparison of total COVID-19-related cases, hospitalizations, and deaths in vaccinated versus unvaccinated people in the state of Wisconsin over a 31-day period (July 2021). Furthermore, we also performed a narrative review of the literature on COVID-19-related outcomes based on mask use and vaccination status. RESULTS: In the state of Wisconsin during July 2021, total COVID-19 cases was 125.4 per 100,000 fully vaccinated people versus 369.2 per 100,000 not fully vaccinated people (odds ratio [OR] = 0.34, 95% confidence interval [CI], 0.33-0.35; P < .001). Total COVID-19 hospitalizations was 4.9 per 100,000 fully vaccinated people versus 18.2 per 100,000 not fully vaccinated people (OR = 0.27, 98% CI, 0.22-0.32; P < .001). Total COVID-19 deaths was 0.1 per 100,000 fully vaccinated people versus 1.1 per 100,000 not fully vaccinated people (OR = 0.09, 95% CI, 0.03-0.29; P < .001). Narrative review of the literature demonstrated high vaccine effectiveness against COVID-19 infection prevention (79%-100% among fully vaccinated people), COVID-19-related hospitalization (87%-98% among fully vaccinated people), and COVID-19-related death (96.7%-98% among fully vaccinated people). Studies have also generally reported that mask use was associated with increased effectiveness in preventing COVID-19 infection ≤70%. CONCLUSIONS: Strict adherence to public mask use and fully vaccinated status are associated with improved COVID-19-related outcomes and can mitigate the spread, morbidity, and mortality of COVID-19. Anesthesiologists and intensivists should adhere to evidence-based guidelines in their approach and management of patients to help mitigate spread.

Hospitalizations of Children and Adolescents with Laboratory-Confirmed COVID-19 - COVID-NET, 14 States, July 2021-January 2022.

The first U.S. case of COVID-19 attributed to the Omicron variant of SARS-CoV-2 (the virus that causes COVID-19) was reported on December 1, 2021 (1), and by the week ending December 25, 2021, Omicron was the predominant circulating variant in the United States.* Although COVID-19-associated hospitalizations are more frequent among adults,† COVID-19 can lead to severe outcomes in children and adolescents (2). This report analyzes data from the Coronavirus Disease 19-Associated Hospitalization Surveillance Network (COVID-NET)§ to describe COVID-19-associated hospitalizations among U.S. children (aged 0-11 years) and adolescents (aged 12-17 years) during periods of Delta (July 1-December 18, 2021) and Omicron (December 19, 2021-January 22, 2022) predominance. During the Delta- and Omicron-predominant periods, rates of weekly COVID-19-associated hospitalizations per 100,000 children and adolescents peaked during the weeks ending September 11, 2021, and January 8, 2022, respectively. The Omicron variant peak (7.1 per 100,000) was four times that of the Delta variant peak (1.8), with the largest increase observed among children aged 0-4 years.¶ During December 2021, the monthly hospitalization rate among unvaccinated adolescents aged 12-17 years (23.5) was six times that among fully vaccinated adolescents (3.8). Strategies to prevent COVID-19 among children and adolescents, including vaccination of eligible persons, are critical.*.

Trends in ICU Mortality From Coronavirus Disease 2019: A Tale of Three Surges.

OBJECTIVES: To determine the association between time period of hospitalization and hospital mortality among critically ill adults with coronavirus disease 2019. DESIGN: Observational cohort study from March 6, 2020, to January 31, 2021. SETTING: ICUs at four hospitals within an academic health center network in Atlanta, GA. PATIENTS: Adults greater than or equal to 18 years with coronavirus disease 2019 admitted to an ICU during the study period (i.e., Surge 1: March to April, Lull 1: May to June, Surge 2: July to August, Lull 2: September to November, Surge 3: December to January). MEASUREMENTS AND MAIN RESULTS: Among 1,686 patients with coronavirus disease 2019 admitted to an ICU during the study period, all-cause hospital mortality was 29.7%. Mortality differed significantly over time: 28.7% in Surge 1, 21.3% in Lull 1, 25.2% in Surge 2, 30.2% in Lull 2, 34.7% in Surge 3 (p = 0.007). Mortality was significantly associated with 1) preexisting risk factors (older age, race, ethnicity, lower body mass index, higher Elixhauser Comorbidity Index, admission from a nursing home); 2) clinical status at ICU admission (higher Sequential Organ Failure Assessment score, higher d-dimer, higher C-reactive protein); and 3) ICU interventions (receipt of mechanical ventilation, vasopressors, renal replacement therapy, inhaled vasodilators). After adjusting for baseline and clinical variables, there was a significantly increased risk of mortality associated with admission during Lull 2 (relative risk, 1.37 [95% CI = 1.03-1.81]) and Surge 3 (relative risk, 1.35 [95% CI = 1.04-1.77]) as compared to Surge 1. CONCLUSIONS: Despite increased experience and evidence-based treatments, the risk of death for patients admitted to the ICU with coronavirus disease 2019 was highest during the fall and winter of 2020. Reasons for this increased mortality are not clear.

Trends and Risk Factors of In-Hospital Mortality of Patients with COVID-19 in Germany: Results of a Large Nationwide Inpatient Sample.

Unselected data of nationwide studies of hospitalized patients with COVID-19 are still sparse, but these data are of outstanding interest to avoid exceeding hospital capacities and overloading national healthcare systems. Thus, we sought to analyze seasonal/regional trends, predictors of in-hospital case-fatality, and mechanical ventilation (MV) in patients with COVID-19 in Germany. We used the German nationwide inpatient samples to analyze all hospitalized patients with a confirmed COVID-19 diagnosis in Germany between 1 January and 31 December in 2020. We analyzed data of 176,137 hospitalizations of patients with confirmed COVID-19-infection. Among those, 31,607 (17.9%) died, whereby in-hospital case-fatality grew exponentially with age. Overall, age ≥ 70 years (OR 5.91, 95%CI 5.70-6.13, p < 0.001), pneumonia (OR 4.58, 95%CI 4.42-4.74, p < 0.001) and acute respiratory distress syndrome (OR 8.51, 95%CI 8.12-8.92, p < 0.001) were strong predictors of in-hospital death. Most COVID-19 patients were treated in hospitals in urban areas (n = 92,971) associated with the lowest case-fatality (17.5%), as compared to hospitals in suburban (18.3%) or rural areas (18.8%). MV demand was highest in November/December 2020 (32.3%, 20.3%) in patients between the 6th and 8th age decade. In the first age decade, 78 of 1861 children (4.2%) with COVID-19-infection were treated with MV, and five of them died (0.3%). The results of our study indicate seasonal and regional variations concerning the number of COVID-19 patients, necessity of MV, and case fatality in Germany. These findings may help to ensure the flexible allocation of intensive care (human) resources, which is essential for managing enormous societal challenges worldwide to avoid overloaded regional healthcare systems.

Tocilizumab improves 28-day survival in hospitalized patients with severe COVID-19: an open label, prospective study.

BACKGROUND: Data on the safety and efficacy profile of tocilizumab in patients with severe COVID-19 needs to be enriched. METHODS: In this open label, prospective study, we evaluated clinical outcomes in consecutive patients with COVID-19 and PaO2/FiO2 < 200 receiving tocilizumab plus usual care versus usual care alone. Tocilizumab was administered at the time point that PaO2/FiO2 < 200 was observed. The primary outcome was 28-day mortality. Secondary outcomes included time to discharge, change in PaO2/FiO2 at day 5 and change in WHO progression scale at day 10. FINDINGS: Overall, 114 patients were included in the analysis (tocilizumab plus usual care: 56, usual care: 58). Allocation to usual care was associated with significant increase in 28-day mortality compared to tocilizumab plus usual care [Cox proportional-hazards model: HR: 3.34, (95% CI: 1.21-9.30), (p = 0.02)]. There was not a statistically significant difference with regards to hospital discharge over the 28 day period for patients receiving tocilizumab compared to usual care [11.0 days (95% CI: 9.0 to 16.0) vs 14.0 days (95% CI: 10.0-24.0), HR: 1.32 (95% CI: 0.84-2.08), p = 0.21]. ΔPaO2/FiO2 at day 5 was significantly higher in the tocilizumab group compared to the usual care group [42.0 (95% CI: 23.0-84.7) vs 15.8 (95% CI: - 19.4-50.3), p = 0.03]. ΔWHO scale at day 10 was significantly lower in the tocilizumab group compared to the usual care group (-0.5 ± 2.1 vs 0.6 ± 2.6, p = 0.005). CONCLUSION: Administration of tocilizumab, at the time point that PaO2/FiO2 < 200 was observed, improved survival and other clinical outcomes in hospitalized patients with severe COVID-19 irrespective of systemic inflammatory markers levels.

SARS-CoV-2 spike-specific memory B cells express higher levels of T-bet and FcRL5 after non-severe COVID-19 as compared to severe disease.

SARS-CoV-2 infection elicits a robust B cell response, resulting in the generation of long-lived plasma cells and memory B cells. Here, we aimed to determine the effect of COVID-19 severity on the memory B cell response and characterize changes in the memory B cell compartment between recovery and five months post-symptom onset. Using high-parameter spectral flow cytometry, we analyzed the phenotype of memory B cells with reactivity against the SARS-CoV-2 spike protein or the spike receptor binding domain (RBD) in recovered individuals who had been hospitalized with non-severe (n = 8) or severe (n = 5) COVID-19. One month after symptom onset, a substantial proportion of spike-specific IgG+ B cells showed an activated phenotype. In individuals who experienced non-severe disease, spike-specific IgG+ B cells showed increased expression of markers associated with durable B cell memory, including T-bet and FcRL5, as compared to individuals who experienced severe disease. While the frequency of T-bet+ spike-specific IgG+ B cells differed between the two groups, these cells predominantly showed an activated switched memory B cell phenotype in both groups. Five months post-symptom onset, the majority of spike-specific memory B cells had a resting phenotype and the percentage of spike-specific T-bet+ IgG+ memory B cells decreased to baseline levels. Collectively, our results highlight subtle differences in the B cells response after non-severe and severe COVID-19 and suggest that the memory B cell response elicited during non-severe COVID-19 may be of higher quality than the response after severe disease.

Baseline clinical features of COVID-19 patients, delay of hospital admission and clinical outcome: A complex relationship.

INTRODUCTION: Delay between symptom onset and access to care is essential to prevent clinical worsening for different infectious diseases. For COVID-19, this delay might be associated with the clinical prognosis, but also with the different characteristics of patients. The objective was to describe characteristics and symptoms of community-acquired (CA) COVID-19 patients at hospital admission according to the delay between symptom onset and hospital admission, and to identify determinants associated with delay of admission. METHODS: The present work was based on prospective NOSO-COR cohort data, and restricted to patients with laboratory confirmed CA SARS-CoV-2 infection admitted to Lyon hospitals between February 8 and June 30, 2020. Long delay of hospital admission was defined as ≥6 days between symptom onset and hospital admission. Determinants of the delay between symptom onset and hospital admission were identified by univariate and multiple logistic regression analysis. RESULTS: Data from 827 patients were analysed. Patients with a long delay between symptom onset and hospital admission were younger (p<0.01), had higher body mass index (p<0.01), and were more frequently admitted to intensive care unit (p<0.01). Their plasma levels of C-reactive protein were also significantly higher (p<0.01). The crude in-hospital fatality rate was lower in this group (13.3% versus 27.6%), p<0.01. Multiple analysis with correction for multiple testing showed that age ≥75 years was associated with a short delay between symptom onset and hospital admission (≤5 days) (aOR: 0.47 95% CI (0.34-0.66)) and CRP>100 mg/L at admission was associated with a long delay (aOR: 1.84 95% CI (1.32-2.55)). DISCUSSION: Delay between symptom onset and hospital admission is a major issue regarding prognosis of COVID-19 but can be related to multiple factors such as individual characteristics, organization of care and severe pathogenic processes. Age seems to play a key role in the delay of access to care and the disease prognosis.

Pandemic-related declines in hospitalization for non-COVID-19-related illness in the United States from January through July 2020.

BACKGROUND: The COVID-19 pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has substantially impacted healthcare utilization worldwide. The objective of this retrospective analysis of a large hospital discharge database was to compare all-cause and cause-specific hospitalizations during the first six months of the pandemic in the United States with the same months in the previous four years. METHODS: Data were collected from all hospitals in the Premier Healthcare Database (PHD) and PHD Special Release reporting hospitalizations from January through July for each year from 2016 through 2020. Hospitalization trends were analyzed stratified by age group, major diagnostic categories (MDCs), and geographic region. RESULTS: The analysis included 286 hospitals from all 9 US Census divisions. The number of all-cause hospitalizations per month was relatively stable from 2016 through 2019 and then fell by 21% (57,281 fewer hospitalizations) between March and April 2020, particularly in hospitalizations for non-respiratory illnesses. From April onward there was a rise in the number of monthly hospitalizations per month. Hospitalizations per month, nationally and in each Census division, decreased for 20 of 25 MDCs between March and April 2020. There was also a decrease in hospitalizations per month for all age groups between March and April 2020 with the greatest decreases in hospitalizations observed for patients 50-64 and ≥65 years of age. CONCLUSIONS: Rates of hospitalization declined substantially during the first months of the COVID-19 pandemic, suggesting delayed routine, elective, and emergency care in the United States. These lapses in care for illnesses not related to COVID-19 may lead to increases in morbidity and mortality for other conditions. Thus, in the current stage of the pandemic, clinicians and public-health officials should work, not only to prevent SARS-CoV-2 transmission, but also to ensure that care for non-COVID-19 conditions is not delayed.

Predicting regional COVID-19 hospital admissions in Sweden using mobility data.

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.

Mortality predictors of hospitalized patients with COVID-19: Retrospective cohort study from Nur-Sultan, Kazakhstan.

BACKGROUND: First reported case of Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) in Kazakhstan was identified in March 2020. Many specialized tertiary hospitals in Kazakhstan including National Research Cardiac Surgery Center (NRCSC) were re-organized to accept coronavirus disease 2019 (COVID-19) infected patients during summer months of 2020. Although many studies from worldwide reported their experience in treating patients with COVID-19, there are limited data available from the Central Asia countries. The aim of this study is to identify predictors of mortality associated with COVID-19 in NRCSC tertiary hospital in Nur-Sultan, Kazakhstan. METHODS: This is a retrospective cohort study of patients admitted to the NRCSC between June 1st-August 31st 2020 with COVID-19. Demographic, clinical and laboratory data were collected from electronic records. In-hospital mortality was assessed as an outcome. Patients were followed-up until in-hospital death or discharge from the hospital. Descriptive statistics and factors associated with mortality were assessed using univariate and multivariate logistic regression models. RESULTS: Two hundred thirty-nine admissions were recorded during the follow-up period. Mean age was 57 years and 61% were males. Median duration of stay at the hospital was 8 days and 34 (14%) patients died during the hospitalization. Non-survivors were more likely to be admitted later from the disease onset, with higher fever, lower oxygen saturation and increased respiratory rate compared to survivors. Leukocytosis, lymphopenia, anemia, elevated liver and kidney function tests, hypoproteinemia, elevated inflammatory markers (C-reactive protein (CRP), ferritin, and lactate dehydrogenase (LDH)) and coagulation tests (fibrinogen, D-dimer, international normalized ratio (INR), and activated partial thromboplastin time (aPTT)) at admission were associated with mortality. Age (OR 1.2, CI:1.01-1.43), respiratory rate (OR 1.38, CI: 1.07-1.77), and CRP (OR 1.39, CI: 1.04-1.87) were determined to be independent predictors of mortality. CONCLUSION: This study describes 14% mortality rate from COVID-19 in the tertiary hospital. Many abnormal clinical and laboratory variables at admission were associated with poor outcome. Age, respiratory rate and CRP were found to be independent predictors of mortality. Our finding would help healthcare providers to predict the risk factors associated with high risk of mortality. Further investigations involving large cohorts should be provided to support our findings.

Impact of early interferon-ß treatment on the prognosis of patients with COVID-19 in the first wave: A post hoc analysis from a multicenter cohort.

BACKGROUND: Interferon-ß is an attractive drug for repurposing and use in the treatment of COVID-19, based on its in vitro antiviral activity and the encouraging results from clinical trials. The aim of this study was to analyze the impact of early interferon-ß treatment in patients admitted with COVID-19 during the first wave of the pandemic. METHODS: This post hoc analysis of a COVID-19@Spain multicenter cohort included 3808 consecutive adult patients hospitalized with COVID-19 from 1 January to 17 March 2020. The primary endpoint was 30-day all-cause mortality, and the main exposure of interest was subcutaneous administration of interferon-ß, defined as early if started ≤ 3 days from admission. Multivariate logistic and Cox regression analyses were conducted to identify the associations of different variables with receiving early interferon-ß therapy and to assess its impact on 30-day mortality. A propensity score was calculated and used to both control for confounders and perform a matched cohort analysis. RESULTS: Overall, 683 patients (17.9%) received early interferon-ß therapy. These patients were more severely ill. Adjusted HR for mortality with early interferon-ß was 1.03 (95% CI, 0.82-1.30) in the overall cohort, 0.96 (0.82-1.13) in the PS-matched subcohort, and 0.89 (0.60-1.32) when interferon-ß treatment was analyzed as a time-dependent variable. CONCLUSIONS: In this multicenter cohort of admitted COVID-19 patients, receiving early interferon-ß therapy after hospital admission did not show an association with lower mortality. Whether interferon-ß might be useful in the earlier stages of the disease or specific subgroups of patients requires further research.