COVID-19 vaccines reduce mortality in hospitalized patients with oxygen requirements: Differences between vaccine subtypes. A multicontinental cohort study.

The aim of this study was to analyze whether the coronavirus disease 2019 (COVID-19) vaccine reduces mortality in patients with moderate or severe COVID-19 disease requiring oxygen therapy. A retrospective cohort study, with data from 148 hospitals in both Spain (111 hospitals) and Argentina (37 hospitals), was conducted. We evaluated hospitalized patients for COVID-19 older than 18 years with oxygen requirements. Vaccine protection against death was assessed through a multivariable logistic regression and propensity score matching. We also performed a subgroup analysis according to vaccine type. The adjusted model was used to determine the population attributable risk. Between January 2020 and May 2022, we evaluated 21,479 COVID-19 hospitalized patients with oxygen requirements. Of these, 338 (1.5%) patients received a single dose of the COVID-19 vaccine and 379 (1.8%) were fully vaccinated. In vaccinated patients, mortality was 20.9% (95% confidence interval [CI]: 17.9-24), compared to 19.5% (95% CI: 19-20) in unvaccinated patients, resulting in a crude odds ratio (OR) of 1.07 (95% CI: 0.89-1.29; p = 0.41). However, after considering the multiple comorbidities in the vaccinated group, the adjusted OR was 0.73 (95% CI: 0.56-0.95; p = 0.02) with a population attributable risk reduction of 4.3% (95% CI: 1-5). The higher risk reduction for mortality was with messenger RNA (mRNA) BNT162b2 (Pfizer) (OR 0.37; 95% CI: 0.23-0.59; p < 0.01), ChAdOx1 nCoV-19 (AstraZeneca) (OR 0.42; 95% CI: 0.20-0.86; p = 0.02), and mRNA-1273 (Moderna) (OR 0.68; 95% CI: 0.41-1.12; p = 0.13), and lower with Gam-COVID-Vac (Sputnik) (OR 0.93; 95% CI: 0.6-1.45; p = 0.76). COVID-19 vaccines significantly reduce the probability of death in patients suffering from a moderate or severe disease (oxygen therapy).

COVID-19 Severity and Survival over Time in Patients with Hematologic Malignancies: A Population-Based Registry Study

Simple Summary There are contradictory data about coronavirus disease (COVID-19) in patients with hematological malignancies. In this population-based study we evaluated severity and survival of unvaccinated patients with hematological malignancies (HM) and COVID-19 in the Madrid region, Spain, between early February 2020 and February 2021. Also, a comparison was made with non-cancer patients from the SEMI-COVID registry and post COVID-19 conditions were evaluated. Overall, 30-day mortality was 32.7%, with higher mortality among certain groups of patients (aged ≥ 60 years, presence of ≥ 3 comorbidities, diagnosis of AML/ALL, treatment with conventional chemotherapy within 30 days of COVID-19 diagnosis, recipients of systemic corticosteroids as COVID-19 therapy). Mortality rates were similar between earlier and later phases of the pandemic, not paralleling the reduction of mortality in non-cancer patients. Up to 27.3% patients had a post COVID-19 condition. These findings will be useful to understand COVID-19 morbidity and mortality in unvaccinated patients diagnosed with HM. Abstract Mortality rates for COVID-19 have declined over time in the general population, but data in patients with hematologic malignancies are contradictory. We identified independent prognostic factors for COVID-19 severity and survival in unvaccinated patients with hematologic malignancies, compared mortality rates over time and versus non-cancer inpatients, and investigated post COVID-19 condition. Data were analyzed from 1166 consecutive, eligible patients with hematologic malignancies from the population-based HEMATO-MADRID registry, Spain, with COVID-19 prior to vaccination roll-out, stratified into early (February–June 2020;n = 769 (66%)) and later (July 2020–February 2021;n = 397 (34%)) cohorts. Propensity-score matched non-cancer patients were identified from the SEMI-COVID registry. A lower proportion of patients were hospitalized in the later waves (54.2%) compared to the earlier (88.6%), OR 0.15, 95%CI 0.11–0.20. The proportion of hospitalized patients admitted to the ICU was higher in the later cohort (103/215, 47.9%) compared with the early cohort (170/681, 25.0%, 2.77;2.01–3.82). The reduced 30-day mortality between early and later cohorts of non-cancer inpatients (29.6% vs. 12.6%, OR 0.34;0.22–0.53) was not paralleled in inpatients with hematologic malignancies (32.3% vs. 34.8%, OR 1.12;0.81–1.5). Among evaluable patients, 27.3% had post COVID-19 condition. These findings will help inform evidence-based preventive and therapeutic strategies for patients with hematologic malignancies and COVID-19 diagnosis.

COVID-19 in solid organ transplant recipients: A single-center case series from Spain.

The clinical characteristics, management, and outcome of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) after solid organ transplant (SOT) remain unknown. We report our preliminary experience with 18 SOT (kidney [44.4%], liver [33.3%], and heart [22.2%]) recipients diagnosed with COVID-19 by March 23, 2020 at a tertiary-care center at Madrid. Median age at diagnosis was 71.0 ± 12.8 years, and the median interval since transplantation was 9.3 years. Fever (83.3%) and radiographic abnormalities in form of unilateral or bilateral/multifocal consolidations (72.2%) were the most common presentations. Lopinavir/ritonavir (usually associated with hydroxychloroquine) was used in 50.0% of patients and had to be prematurely discontinued in 2 of them. Other antiviral regimens included hydroxychloroquine monotherapy (27.8%) and interferon-ß (16.7%). As of April 4, the case-fatality rate was 27.8% (5/18). After a median follow-up of 18 days from symptom onset, 30.8% (4/13) of survivors developed progressive respiratory failure, 7.7% (1/13) showed stable clinical condition or improvement, and 61.5% (8/13) had been discharged home. C-reactive protein levels at various points were significantly higher among recipients who experienced unfavorable outcome. In conclusion, this frontline report suggests that SARS-CoV-2 infection has a severe course in SOT recipients.

COVID-19 Severity and Survival over Time in Patients with Hematologic Malignancies: A Population-Based Registry Study.

Mortality rates for COVID-19 have declined over time in the general population, but data in patients with hematologic malignancies are contradictory. We identified independent prognostic factors for COVID-19 severity and survival in unvaccinated patients with hematologic malignancies, compared mortality rates over time and versus non-cancer inpatients, and investigated post COVID-19 condition. Data were analyzed from 1166 consecutive, eligible patients with hematologic malignancies from the population-based HEMATO-MADRID registry, Spain, with COVID-19 prior to vaccination roll-out, stratified into early (February-June 2020; n = 769 (66%)) and later (July 2020-February 2021; n = 397 (34%)) cohorts. Propensity-score matched non-cancer patients were identified from the SEMI-COVID registry. A lower proportion of patients were hospitalized in the later waves (54.2%) compared to the earlier (88.6%), OR 0.15, 95%CI 0.11-0.20. The proportion of hospitalized patients admitted to the ICU was higher in the later cohort (103/215, 47.9%) compared with the early cohort (170/681, 25.0%, 2.77; 2.01-3.82). The reduced 30-day mortality between early and later cohorts of non-cancer inpatients (29.6% vs. 12.6%, OR 0.34; 0.22-0.53) was not paralleled in inpatients with hematologic malignancies (32.3% vs. 34.8%, OR 1.12; 0.81-1.5). Among evaluable patients, 27.3% had post COVID-19 condition. These findings will help inform evidence-based preventive and therapeutic strategies for patients with hematologic malignancies and COVID-19 diagnosis.

Disparities between Latinx migrants and Spanish natives in COVID-19 outcome in Madrid.

Previous studies have suggested an increased susceptibility of COVID-19 among certain populations. We analyzed whether COVID-19 presentation and mortality differ between Latinx migrants and Spanish natives. METHODS AND MATERIALS: COVID-19 patients between 35-64 years old admitted between January 26th-May-5th 2020 were reviewed. Demographics, major comorbidities, symptoms, signs and analytical parameters on admission were recorded. Respiratory failure was defined as PaO2/FiO2 ≤ 200 mmHg, noninvasive or invasive mechanical ventilation requirement at any time during hospitalization. A propensity score (PS) adjustment was created between Latinx and Spanish. A multivariable logistic regression model adjusted by the PS was performed to evaluate the effects of different variables on mortality. RESULTS: 894 patients: 425 (47.5%) Latinx and 469 (52.5%) Spanish natives were included. Latinx were younger (50 vs 55 years p < 0.001) and had less comorbidities (29.4% vs 55.0% p < 0.001) than Spanish natives. More often they exhibited fever (22.1% vs 9.8% p = 0.018) and had higher inflammatory markers (PCR) (11.3 mg/dl vs 7.7 mg/dl p < 0.001). Mortality seemed lower among Latinx (4.7% vs 8.7%, p = 0.017). No association was found between ethnicity and mortality. Respiratory failure [OR = 23.978 (CI 95% 9.4-60.1) p < 0.001], LDH [OR (per unitary increment) = 1.002; CI95% (1.000-1.004;p = 0.036] and PCR [OR (per unitary increment) = 1.044 (CI95% 1.06-1.08); p = 0.02] were independently associated to mortality. CONCLUSIONS: We were unable to identify significant ethnic disparities between Latinx and Spanish natives in terms of COVID-19 mortality. Universal access to the health care system in Spain may have contributed to a better outcome of Latinx patients. Differences previously described might be a consequence of socioeconomic disparities.

Clinical Differences and Outcomes of COVID-19 Associated Pulmonary Thromboembolism in Comparison with Non-COVID-19 Pulmonary Thromboembolism.

(1) Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been reported to increase the risk of pulmonary thromboembolism (PTE). The aim of this study is to elucidate whether Coronavirus disease COVID-19-associated PTE has a different clinical expression than non-COVID-19 PTE due to a different pathophysiology. (2) Methods: retrospective study of PTE episodes conducted at our hospital between January 2019 and December 2020, comparing the group of COVID-19-associated PTE patients with a control group of non-COVID-19 PTE patients. (3) Results: A total of 229 patients with PTE were registered, 79 of whom had COVID-19. Cancer (15.2% vs. 39.3%; p < 0.001), previous surgery (0% vs. 8%; p = 0.01), previous VTE (2.5% vs. 15.3%; p = 0.003), signs and/or symptoms of deep venous thrombosis (DVT) (7.6% vs. 22.7%; p = 0.004) and syncope (1.3% vs. 8.1%; p = 0.035) were less frequent in the COVID-19 group. Central thrombosis was more frequent in the control group (35.3% vs. 13.9%; p = 0.001). No VTE recurrent episodes were observed in the COVID-19 group, whereas four (2.7%) episodes were recorded for the control group. One-month bleeding rate was higher in the COVID-19 group (10.1% vs. 1.3%; p = 0.004). (4) Conclusion: COVID-19-associated PTE has clinical characteristics that differ from those of PTE without COVID-19, including inferior severity and a lower rate of VTE recurrence. Physicians should be aware of the high risk of bleeding in the first month of COVID-19-associated PTE.

Making EHRs Trustable: A Quality Analysis of EHR-Derived Datasets for COVID-19 Research.

One approach to verifying the quality of research data obtained from EHRs is auditing how complete and correct the data are in comparison with those collected by manual and controlled methods. This study analyzed data quality of an EHR-derived dataset for COVID-19 research, obtained during the pandemic at Hospital Universitario 12 de Octubre. Data were extracted from EHRs and a manually collected research database, and then transformed into the ISARIC-WHO COVID-19 CRF model. Subsequently, a data analysis was performed, comparing both sources through this convergence model. More concepts and records were obtained from EHRs, and PPV (95% CI) was above 85% in most sections. In future studies, a more detailed analysis of data quality will be carried 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.

Higher mortality of hospitalized haematologic patients with COVID-19 compared to non-haematologic is driven by thrombotic complications and development of ARDS: An age-matched cohorts study.

BACKGROUND AND OBJECTIVES: The characteristics of COVID-19 in haematologic patients compared to non-haematologic patients have seldom been analyzed. Our aim was to analyze whether there are differences in clinical characteristics and outcome of haematologic patients with COVID-19 as compared to non-haematologic. PATIENTS AND METHODS: Retrospective cohort study in 2 University hospitals of patients admitted with laboratory-confirmed COVID-19 included in the SEMICOVID19 database. The cohort with underlying haematologic disease was compared to a cohort of age and date-of-COVID-19-matched controls without haematologic disease (1:2). RESULTS: 71 cases and 142 controls were included from March-May 2020.Twenty (28.1%) had received recent chemotherapy. Twelve (16.9%) were stem cell transplant recipients (SCT). Eleven (15.5%) were neutropenic concurrently with COVID-19 diagnosis.Haematologic patients presented ARDS (58.5 vs 20.7%, p = 0.0001), thrombotic complications (15.7 vs 2.1%, p = 0.002), DIC (5.7 vs 0.0%, p = 0.011), heart failure (14.3 vs 4.9%, p = 0.029) and required ICU admission (15.5 vs 2.8%, p = 0.001), MV (14.1% vs 2.1%, p 0.001), steroid (64.8 vs 33.1%, p = 0.0001), tocilizumab (33.8 vs 8.5%, p = 0.0001) or anakinra treatment (9.9% vs 0%, p = 0.0001) more often. In-hospital mortality was significantly higher (38.0% vs 18.3%, p = 0.002). CONCLUSIONS: Our results suggest COVID-19 has worse outcomes in haematologic patients than in non-haematologic, independently of age, and that the development of ARDS and thrombotic complications drive the higher in-hospital mortality.

An Early Th1 Response Is a Key Factor for a Favorable COVID-19 Evolution.

The Th1/Th2 balance plays a crucial role in the progression of different pathologies and is a determining factor in the evolution of infectious diseases. This work has aimed to evaluate the early, or on diagnosis, T-cell compartment response, T-helper subsets and anti-SARS-CoV-2 antibody specificity in COVID-19 patients and to classify them according to evolution based on infection severity. A unicenter, randomized group of 146 COVID-19 patients was divided into four groups in accordance with the most critical events during the course of disease. The immunophenotype and T-helper subsets were analyzed by flow cytometry. Asymptomatic SARS-CoV-2 infected individuals showed a potent and robust Th1 immunity, with a lower Th17 and less activated T-cells at the time of sample acquisition compared not only with symptomatic patients, but also with healthy controls. Conversely, severe COVID-19 patients presented with Th17-skewed immunity, fewer Th1 responses and more activated T-cells. The multivariate analysis of the immunological and inflammatory parameters, together with the comorbidities, showed that the Th1 response was an independent protective factor for the prevention of hospitalization (OR 0.17, 95% CI 0.03-0.81), with an AUC of 0.844. Likewise, the Th1 response was found to be an independent protective factor for severe forms of the disease (OR 0.09, 95% CI: 0.01-0.63, p = 0.015, AUC: 0.873). In conclusion, a predominant Th1 immune response in the acute phase of the SARS-CoV-2 infection could be used as a tool to identify patients who might have a good disease evolution.

Analysis of the factors predicting clinical response to tocilizumab therapy in patients with severe COVID-19.

BACKGROUND: Controversy remains about the efficacy of tocilizumab (TCZ) for the treatment of severe COVID-19. We aimed to analyze the profile of TCZ-respondent patients. METHODS: We retrospectively analyzed a cohort of patients with severe COVID-19 who received off-label TCZ after recommendation by a local committee and were admitted to the University Hospital "12 de Octubre" until May 2020. The primary end point was a significant clinical improvement (SCI) on day 14 after administration of TCZ. Factors independently related to SCI were analyzed by multivariate logistic regression models. RESULTS: Of 428 (63.3%) patients treated with TCZ, 271 (63.3%) experienced SCI. After adjustment for factors related to unfavorable outcomes, TCZ administration within the first 48 hours from admission (odds ratio [OR]: 1.98, 95% confidence Interval [95% CI]: 1.1-3.55; P = 0.02) and ALT levels >100 UI/L at day 0 (OR: 3.28; 95% CI: 1.3-8.1; P = 0.01) were independently related to SCI. The rate of SCI significantly decreased according to the time of TCZ administration: 70.2% in the first 48 hours from admission, 58.5% on days 3-7, and 45.1% after day 7 (P = 0.03 and P = 0.001, respectively). CONCLUSION: TCZ improves the prognosis of patients with COVID-19 the most if treatment starts within the first 48 hours after admission.

Longitudinal dynamics of SARS-CoV-2-specific cellular and humoral immunity after natural infection or BNT162b2 vaccination.

The timing of the development of specific adaptive immunity after natural SARS-CoV-2 infection, and its relevance in clinical outcome, has not been characterized in depth. Description of the long-term maintenance of both cellular and humoral responses elicited by real-world anti-SARS-CoV-2 vaccination is still scarce. Here we aimed to understand the development of optimal protective responses after SARS-CoV-2 infection and vaccination. We performed an early, longitudinal study of S1-, M- and N-specific IFN-γ and IL-2 T cell immunity and anti-S total and neutralizing antibodies in 88 mild, moderate or severe acute COVID-19 patients. Moreover, SARS-CoV-2-specific adaptive immunity was also analysed in 234 COVID-19 recovered subjects, 28 uninfected BNT162b2-vaccinees and 30 uninfected healthy controls. Upon natural infection, cellular and humoral responses were early and coordinated in mild patients, while weak and inconsistent in severe patients. The S1-specific cellular response measured at hospital arrival was an independent predictive factor against severity. In COVID-19 recovered patients, four to seven months post-infection, cellular immunity was maintained but antibodies and neutralization capacity declined. Finally, a robust Th1-driven immune response was developed in uninfected BNT162b2-vaccinees. Three months post-vaccination, the cellular response was comparable, while the humoral response was consistently stronger, to that measured in COVID-19 recovered patients. Thus, measurement of both humoral and cellular responses provides information on prognosis and protection from infection, which may add value for individual and public health recommendations.

Clinical Outcomes of Hospitalized COVID-19 Patients Treated with Remdesivir-NEAT ID 909REM Study

Background There are few real-world data on the use of remdesivir (RDV) looking at timing of initiation in relation to symptom onset and severity of presenting disease. Methods We conducted multi-country retrospective study of clinical practice and use of RDV in COVID-19 patients. De-identified medical records data were entered into an e-CRF. Primary endpoints were all-cause mortality at day 28 and hospitalization duration. We assessed time from symptom onset to RDV start and re-admission. We included adults with PCR-confirmed symptomatic COVID-19 who were hospitalized after Aug 31, 2020 and received at least 1 dose of RDV. Descriptive analyses were conducted. Kaplan-Meier methods were used to calculate the mortality rate, LogRank test to compare groups defined by severity of disease. Competing risk regression with discharge and death as competing events was used to estimate duration of hospitalization, and Gray’s test to compare the groups. Results 448 patients in 5 countries (12 sites) were included. Demographics are summarized (table) by 3 disease severity groups at baseline: no supplemental oxygen (NSO), low flow oxygen ≤6 L/min (LFO), and high-flow oxygen > 6L/min (HFO). No demographic differences were found between groups except for the higher percentage of cancer/chemotherapy patients in NSO group. Corticosteroids use was HFO 73.6%, LFO 62.7%, NSO 58.0%. Mortality rate was significantly lower in NSO, and LFO groups compared with HFO (6.2%, 10.2%, 23.6%, respectively;Fig1). Median duration of hospitalization was 9 (95%CI 8-10), 9 (8-9), 13 (10-15) days, respectively (Fig2). Median time from first symptom to RDV start was 7 days in all 3 groups. Patients started RDV on day 1 of hospitalization in HFO and LFO and day 2 on NSO groups. And received a 5 day course (median). Readmission within 28-days of discharge was < 5% and similar across all 3 groups. Table 1. Patients baseline characteristics and primary and secondary outcomes Figure 1. Kaplan-Meier estimates of mortality Figure 2. Competing-risks regression of discharge from hospital Conclusion In this real-world cohort of COVID-19 positive hospitalized patients, RDV use was consistent across countries. RDV was started within a median of 7 days from symptom within 2 days of admission and given for a median of 5 days. Higher mortality rate and duration of hospitalization was seen in the HFO group and similar rates seen in the LFO and NSO groups. Readmission was consistently low across all 3 groups. Disclosures François Raffi, MD, PhD, Gilead Sciences (Consultant, Scientific Research Study Investigator, Advisor or Review Panel member)Janssen (Consultant)MSD (Consultant, Scientific Research Study Investigator, Advisor or Review Panel member)Roche (Consultant)Theratechnologies (Advisor or Review Panel member)ViiV (Consultant, Scientific Research Study Investigator, Advisor or Review Panel member) Nadir Arber, MD, MSc, MHA, Check cap (Consultant)Coved cd 24 (Board Member)Israel Innovation Authority (Research Grant or Support)Nucleix (Advisor or Review Panel member)Zion Pharmaceuticals (Advisor or Review Panel member) Casper Rokx, MD PhD, Gilead Sciences (Grant/Research Support, Advisor or Review Panel member, Research Grant or Support)Merck (Grant/Research Support, Research Grant or Support)ViiV (Grant/Research Support, Advisor or Review Panel member, Research Grant or Support) Ameet Bakhai, MBBS, MD, FRCP, FESC, Bayer AG (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor)Boehringer Ingelheim (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor)Bristol-Myers Squibb (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor)Daiichi-Sankyo Europe (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor)Gilead Sciences (Grant/Research Support, Scientific Research Study Investigator)Janssen (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor)Johnson & Johnson (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor)MSD (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor)Novartis (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor)Pfizer (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor)Roche (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor)Sanofi (Consultant, Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support, Speaker's Bureau, Independent Contractor) Alex Soriano, MD, Angelini (Speaker's Bureau)Gilead Sciences (Research Grant or Support, Speaker's Bureau)Menarini (Speaker's Bureau)MSD (Research Grant or Support, Speaker's Bureau)Pfizer (Research Grant or Support, Speaker's Bureau)Shionogi (Speaker's Bureau) Carlos Lumbreras, MD, PhD, Gilead Sciences (Grant/Research Support)MSD (Consultant) Vicente Estrada, MD, PhD, Gilead Sciences (Consultant, Grant/Research Support)Janssen (Advisor or Review Panel member)MSD (Consultant, Grant/Research Support)Theratechnologies (Consultant)ViiV (Consultant) Adrian Curran, MD, PhD, Gilead Sciences (Advisor or Review Panel member, Research Grant or Support)Janssen (Advisor or Review Panel member, Research Grant or Support)MSD (Advisor or Review Panel member, Research Grant or Support)ViiV (Advisor or Review Panel member, Research Grant or Support) Essy Mozaffari, PharmD, MPH, MBA, Gilead Sciences (Employee, Shareholder) Richard Haubrich, MD, Gilead Sciences (Employee, Shareholder) Paul Hodgkins, PhD, MSc, Gilead Sciences (Employee, Shareholder) Anton Pozniak, MD, FRCP, Gilead Sciences (Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support)Janssen (Grant/Research Support, Research Grant or Support)Merck (Advisor or Review Panel member)Theratec (Grant/Research Support, Advisor or Review Panel member, Research Grant or Support)ViiV (Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Research Grant or Support)

Which outcomes are most important to measure in patients with COVID-19 and how and when should these be measured? Development of an international standard set of outcomes measures for clinical use in patients with COVID-19: a report of the International Consortium for Health Outcomes Measurement (ICHOM) COVID-19 Working Group.

OBJECTIVES: The COVID-19 pandemic has resulted in widespread morbidity and mortality with the consequences expected to be felt for many years. Significant variation exists in the care even of similar patients with COVID-19, including treatment practices within and between institutions. Outcome measures vary among clinical trials on the same therapies. Understanding which therapies are of most value is not possible unless consensus can be reached on which outcomes are most important to measure. Furthermore, consensus on the most important outcomes may enable patients to monitor and track their care, and may help providers to improve the care they offer through quality improvement. To develop a standardised minimum set of outcomes for clinical care, the International Consortium for Health Outcomes Measurement (ICHOM) assembled a working group (WG) of 28 volunteers, including health professionals, patients and patient representatives. DESIGN: A list of outcomes important to patients and professionals was generated from a systematic review of the published literature using the MEDLINE database, from review of outcomes being measured in ongoing clinical trials, from a survey distributed to patients and patient networks, and from previously published ICHOM standard sets in other disease areas. Using an online-modified Delphi process, the WG selected outcomes of greatest importance. RESULTS: The outcomes considered by the WG to be most important were selected and categorised into five domains: (1) functional status and quality of life, (2) mental functioning, (3) social functioning, (4) clinical outcomes and (5) symptoms. The WG identified demographic and clinical variables for use as case-mix risk adjusters. These included baseline demographics, clinical factors and treatment-related factors. CONCLUSION: Implementation of these consensus recommendations could help institutions to monitor, compare and improve the quality and delivery of care to patients with COVID-19. Their consistent definition and collection could also broaden the implementation of more patient-centric clinical outcomes research.

A Short Corticosteroid Course Reduces Symptoms and Immunological Alterations Underlying Long-COVID.

Despite the growing number of patients with persistent symptoms after acute SARS-CoV-2 infection, the pathophysiology underlying long-COVID is not yet well characterized, and there is no established therapy. We performed a deep immune profiling in nine patients with persistent symptoms (PSP), before and after a 4-day prednisone course, and five post-COVID-19 patients without persistent symptoms (NSP). PSP showed a perturbed distribution of circulating mononuclear cell populations. Symptoms in PSP were accompanied by a pro-inflammatory phenotype characterized by increased conventional dendritic cells and augmented expression of antigen presentation, co-stimulation, migration, and activation markers in monocytes. The adaptive immunity compartment in PSP showed a Th1-predominance, decreased naïve and regulatory T cells, and augmentation of the PD-1 exhaustion marker. These immune alterations reverted after the corticosteroid treatment and were maintained during the 4-month follow-up, and their normalization correlated with clinical amelioration. The current work highlights an immunopathogenic basis together with a possible role for steroids in the treatment for long-COVID.

Clinical Characteristics and Risk Factors of Respiratory Failure in a Cohort of Young Patients Requiring Hospital Admission with SARS-CoV2 Infection in Spain: Results of the Multicenter SEMI-COVID-19 Registry.

BACKGROUND: Age is a risk factor for COVID severity. Most studies performed in hospitalized patients with SARS-CoV2 infection have shown an over-representation of older patients and consequently few have properly defined COVID-19 in younger patients who require hospital admission. The aim of the present study was to analyze the clinical characteristics and risk factors for the development of respiratory failure among young (18 to 50 years) hospitalized patients with COVID-19. METHODS: This retrospective nationwide cohort study included hospitalized patients from 18 to 50 years old with confirmed COVID-19 between March 1, 2020, and July 2, 2020. All patient data were obtained from the SEMI-COVID Registry. Respiratory failure was defined as the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO2/FiO2 ratio) ≤200 mmHg or the need for mechanical ventilation and/or high-flow nasal cannula or the presence of acute respiratory distress syndrome. RESULTS: During the recruitment period, 15,034 patients were included in the SEMI-COVID-19 Registry, of whom 2327 (15.4%) were younger than 50 years. Respiratory failure developed in 343 (14.7%), while mortality occurred in 2.3%. Patients with respiratory failure showed a higher incidence of major adverse cardiac events (44 (13%) vs 14 (0.8%), p<0.001), venous thrombosis (23 (6.7%) vs 14 (0.8%), p<0.001), mortality (43 (12.5%) vs 7 (0.4%), p<0.001), and longer hospital stay (15 (9-24) vs 6 (4-9), p<0.001), than the remaining patients. In multivariate analysis, variables associated with the development of respiratory failure were obesity (odds ratio (OR), 2.42; 95% confidence interval (95% CI), 1.71 to 3.43; p<0.0001), alcohol abuse (OR, 2.40; 95% CI, 1.26 to 4.58; p=0.0076), sleep apnea syndrome (SAHS) (OR, 2.22; 95% CI, 1.07 to 3.43; p=0.032), Charlson index ≥1 (OR, 1.77; 95% CI, 1.25 to 2.52; p=0.0013), fever (OR, 1.58; 95% CI, 1.13 to 2.22; p=0.0075), lymphocytes ≤1100 cells/µL (OR, 1.67; 95% CI, 1.18 to 2.37; p=0.0033), LDH >320 U/I (OR, 1.69; 95% CI, 1.18 to 2.42; p=0.0039), AST >35 mg/dL (OR, 1.74; 95% CI, 1.2 to 2.52; p=0.003), sodium <135 mmol/L (OR, 2.32; 95% CI, 1.24 to 4.33; p=0.0079), and C-reactive protein >8 mg/dL (OR, 2.42; 95% CI, 1.72 to 3.41; p<0.0001). CONCLUSIONS: Young patients with COVID-19 requiring hospital admission showed a notable incidence of respiratory failure. Obesity, SAHS, alcohol abuse, and certain laboratory parameters were independently associated with the development of this complication. Patients who suffered respiratory failure had a higher mortality and a higher incidence of major cardiac events, venous thrombosis, and hospital stay.

Tocilizumab for the treatment of adult patients with severe COVID-19 pneumonia: A single-center cohort study.

Coronavirus disease 2019 (COVID-19) can lead to a massive cytokine release. The use of the anti-interleukin-6 receptor monoclonal antibody tocilizumab (TCZ) has been proposed in this hyperinflammatory phase, although supporting evidence is limited. We retrospectively analyzed 88 consecutive patients with COVID-19 pneumonia that received at least one dose of intravenous TCZ in our institution between 16 and 27 March 2020. Clinical status from day 0 (first TCZ dose) through day 14 was assessed by a 6-point ordinal scale. The primary outcome was clinical improvement (hospital discharge and/or a decrease of ≥2 points on the 6-point scale) by day 7. Secondary outcomes included clinical improvement by day 14 and dynamics of vital signs and laboratory values. Rates of clinical improvement by days 7 and 14 were 44.3% (39/88) and 73.9% (65/88). Previous or concomitant receipt of subcutaneous interferon-ß (adjusted odds ratio [aOR]: 0.23; 95% confidence interval [CI]: 0.06-0.94; P = .041) and serum lactate dehydrogenase more than 450 U/L at day 0 (aOR: 0.25; 95% CI: 0.06-0.99; P = .048) were negatively associated with clinical improvement by day 7. All-cause mortality was 6.8% (6/88). Body temperature and respiratory and cardiac rates significantly decreased by day 1 compared to day 0. Lymphocyte count and pulse oximetry oxygen saturation/FiO2 ratio increased by days 3 and 5, whereas C-reactive protein levels dropped by day 2. There were no TCZ-attributable adverse events. In this observational single-center study, TCZ appeared to be useful and safe as immunomodulatory therapy for severe COVID-19 pneumonia.

A predictive score at admission for respiratory failure among hospitalized patients with confirmed 2019 Coronavirus Disease: a simple tool for a complex problem.

Coronavirus Disease 2019 (COVID-19) pandemic has implacably stricken on the wellness of many countries and their health-care systems. The aim of the present study is to analyze the clinical characteristics of the initial wave of patients with COVID-19 attended in our center, and to identify the key variables predicting the development of respiratory failure. Prospective design study with concurrent data retrieval from automated medical records of all hospitalized adult patients who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rRT-PCR assay performed on respiratory samples from March 2nd to 18th, 2020. Patients were followed up to May 1st, 2020 or death. Respiratory failure was defined as a PaO2/FiO2 ratio ≤ 200 mm Hg or the need for mechanical ventilation (either non-invasive positive pressure ventilation or invasive mechanical ventilation). We included 521 patients of whom 416 (81%) had abnormal Chest X-ray on admission. Median age was 64.6 ± 18.2 years. One hundred eighty-one (34.7%) developed respiratory failure after a median time from onset of symptoms of 9 days (IQR 6-11). In-hospital mortality was 23.8% (124/521). The modeling process concluded into a logistic regression multivariable analysis and a predictive score at admission. Age, peripheral pulse oximetry, lymphocyte count, lactate dehydrogenase and C-reactive protein were the selected variables. The model has a good discriminative capacity with an area under the ROC curve of 0.85 (0.82-0.88). The application of a simple and reliable score at admission seems to be a useful tool to predict respiratory failure in hospitalized COVID-19 patients.

Combination therapy with tocilizumab and corticosteroids for aged patients with severe COVID-19 pneumonia: A single-center retrospective study.

BACKGROUND: The role of combination immunomodulatory therapy with systemic corticosteroids and tocilizumab (TCZ) for aged patients with COVID-19-associated cytokine release syndrome remains unclear. METHODS: A retrospective single-center study was conducted on consecutive patients aged ≥65 years who developed severe COVID-19 between 03 March and 01 May 2020 and were treated with corticosteroids at various doses (methylprednisolone 0.5mg/kg/12h to 250mg/24h), either alone (CS group) or associated with intravenous tocilizumab (400-600mg, one to three doses) (CS-TCZ group). The primary outcome was all-cause mortality by day +14, whereas secondary outcomes included mortality by day +28 and clinical improvement (discharge and/or a ≥2 point decrease on a 6-point ordinal scale) by day +14. Propensity score (PS)-based adjustment and inverse probability of treatment weights (IPTW) were applied. RESULTS: Totals of 181 and 80 patients were included in the CS and CS-TCZ groups, respectively. All-cause 14-day mortality was lower in the CS-TCZ group, both in the PS-adjusted (hazard ratio [HR]: 0.34; 95% confidence interval [CI]: 0.17-0.68; P=0.002) and IPTW-weighted models (odds ratio [OR]: 0.38; 95% CI: 0.21-0.68; P=0.001). This protective effect was also observed for 28-day mortality (PS-adjusted HR: 0.38; 95% CI: 0.21-0.72; P=0.003). Clinical improvement by day +14 was higher in the CS-TCZ group with IPTW analysis only (OR: 2.26; 95% CI: 1.49-3.41; P<0.001). The occurrence of secondary infection was similar between both groups. CONCLUSIONS: The combination of corticosteroids and TCZ was associated with better outcomes among patients aged ≥65 years with severe COVID-19.