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This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on predicting the complications leading to Intensive Care Unit (ICU) admission or mortality, which are minority classes compared to the majority class of discharged patients. We operate within a multiclass framework comprising three distinct classes, and address the challenge of dataset imbalance, a common source of model bias. To effectively manage this, we introduce the Multi-Thresholding meta-algorithm (MTh), an innovative output-level methodology that extends traditional thresholding from binary to multiclass classification. This methodology dynamically adjusts class probabilities using misclassification costs, making it highly effective in imbalanced datasets. Our approach is further enhanced by integrating the simplicity, transparency, and effectiveness of Bayesian networks to create a robust predictive model. Using patient admission data, the model accurately identifies key risk and protective factors for COVID-19 outcomes. Our findings indicate that certain patient characteristics, such as high Charlson Index and pre-existing conditions, significantly influence the risk of ICU admission and mortality. Moreover, we introduce an explanatory model that elucidates the interrelationships among these factors, demonstrating the influence of therapeutic limits on the overall risk assessment of COVID-19 patients. Overall, our research provides a significant contribution to the field of Machine Learning by offering a novel solution for multiclass classification in the context of imbalanced datasets. This model not only enhances predictive accuracy but also supports critical decision-making processes in healthcare, potentially improving patient outcomes and optimizing clinical resource allocation., Competing Interests: Declarations Conflict of interest The authors declare no potential conflict of interests nor competing interests.github.com/RosDelgado/MTh Code availability The computer programming code (R function) that has been developed and utilized in this study, to implement Algorithm 1 (the Multi-Thresholding meta-algorithm, MTh) is accessible under an open-access (MIT) license. You can find it at https://github.com/RosDelgado/MTh. Ethical implications The use of predictive models in clinical settings raises several important ethical considerations. First and foremost, privacy and data security are critical: patient data used for training and validating models must be protected, and all practices must comply with data protection regulations. Second, it is essential to address potential biases within the model to ensure that predictions are equitable across different patient groups and to prevent discrimination in care. Third, predictive models should be used to complement, rather than replace, clinical judgment. It is important to be transparent about the limitations and uncertainties inherent in the model’s predictions. Lastly, patients should be adequately informed about the use of predictive models in their care. Clear communication is necessary to help patients to understand how these models influence clinical decisions., (© 2024. The Author(s).)

Background: Shorter duration of symptoms before remdesivir has been associated with better outcomes. Our goal was to evaluate variables associated with the need of ICU admission in a cohort of hospitalized patients for COVID-19 under remdesivir including the period from symptoms onset to remdesivir., Methods: We conducted a retrospective multicentric study analysing all patients admitted with COVID-19 in 9 Spanish hospitals who received treatment with remdesivir in October 2020. The main outcome was the need of ICU admission after 24 h of the first dose of remdesivir., Results: In our cohort of 497 patients, the median of days from symptom onset to remdesivir was 5 days, and 70 of them (14.1%) were later admitted into ICU. The clinical outcomes associated with ICU admission were days from symptoms onset (5 vs. 6; p = 0.023), clinical signs of severe disease (respiratory rate, neutrophil count, ferritin levels and very-high mortality rate in SEIMC-Score) and the use of corticosteroids and anti-inflammatory drugs before ICU. The only variable significatively associated with risk reduction in the Cox-regression analyses was ≤ 5 days from symptoms onset to RDV (HR: 0.54, CI95%: 0.31-0.92; p = 0.024)., Conclusion: For patients admitted to the hospital with COVID-19, the prescription of remdesivir within 5 days from symptoms onset diminishes the need of ICU admission., (© 2023. The Author(s).)

COVID-19-associated pulmonary aspergillosis (CAPA) has emerged as a frequent complication in the intensive care unit (ICU). However, little is known about this life-threatening fungal superinfection in solid organ transplant recipients (SOTRs), including whether targeted anti-mold prophylaxis might be justified in this immunosuppressed population. We performed a multicentric observational retrospective study of all consecutive ICU-admitted COVID-19 SOTRs between August 1, 2020 and December 31, 2021. SOTRs receiving antifungal prophylaxis with nebulized amphotericin-B were compared with those without prophylaxis. CAPA was defined according the ECMM/ISHAM criteria. Sixty-four SOTRs were admitted to ICU for COVID-19 during the study period. One patient received antifungal prophylaxis with isavuconazole and was excluded from the analysis. Of the remaining 63 SOTRs, nineteen (30.2%) received anti-mold prophylaxis with nebulized amphotericin-B. Ten SOTRs who did not receive prophylaxis developed pulmonary mold infections (nine CAPA and one mucormycosis) compared with one who received nebulized amphotericin-B (22.7% vs 5.3%; risk ratio 0.23; 95%CI 0.032-1.68), but with no differences in survival. No severe adverse events related to nebulized amphotericin-B were recorded. SOTRs admitted to ICU with COVID-19 are at high risk for CAPA. However, nebulized amphotericin-B is safe and might reduce the incidence of CAPA in this high-risk population. A randomized clinical trial to confirm these findings is warranted., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Rombauts, Bodro, Daniel Gumucio, Carbonell, Favà, Lladó, González-Costello, Oppenheimer, Castel-Lavilla, Len, Marquez-Algaba, Nuvials-Casals, Martínez González, Lacasa, Carratalà and Sabé.)

Objective: To determine the health status and exercise capacity of COVID-19 survivors one year after hospital discharge., Methods: This multicenter prospective study included COVID-19 survivors 12 months after hospital discharge. Participants were randomly selected from a large cohort of COVID-19 patients who had been hospitalized until 15th April 2020. They were interviewed about persistent symptoms, underwent a physical examination, chest X-ray, and a 6-minute walk test (6MWT). A multivariate analysis was performed to determine the risk factors for persistent dyspnea., Results: Of the 150 patients included, 58% were male and the median age was 63 (IQR 54-72) years. About 82% reported ≥1 symptoms and 45% had not recovered their physical health. The multivariate regression analysis revealed that the female sex, chronic obstructive pulmonary disease, and smoking were independent risk factors for persistent dyspnea. Approximately 50% completed less than 80% of the theoretical distance on the 6MWT. Only 14% had an abnormal X-ray, showing mainly interstitial infiltrates. A third of them had been followed up in outpatient clinics and 6% had undergone physical rehabilitation., Conclusion: Despite the high rate of survivors of the first wave of the COVID-19 pandemic with persistent symptomatology at 12 months, the follow-up and rehabilitation of these patients has been really poor. Studies focusing on the role of smoking in the persistence of COVID-19 symptoms are lacking., Competing Interests: The authors have declared that no competing interests exist.

Objectives: To explore candidate prognostic and predictive biomarkers identified in retrospective observational studies (interleukin-6, C-reactive protein, lactate dehydrogenase, ferritin, lymphocytes, monocytes, neutrophils, d-dimer, and platelets) in patients with coronavirus disease 2019 pneumonia after treatment with tocilizumab, an anti-interleukin-6 receptor antibody, using data from the COVACTA trial in patients hospitalized with severe coronavirus disease 2019 pneumonia., Design: Exploratory analysis from a multicenter, randomized, double-blind, placebo-controlled, phase 3 trial., Setting: Hospitals in North America and Europe., Patients: Adults hospitalized with severe coronavirus disease 2019 pneumonia receiving standard care., Intervention: Randomly assigned 2:1 to IV tocilizumab 8 mg/kg or placebo., Measurements and Main Results: Candidate biomarkers were measured in 295 patients in the tocilizumab arm and 142 patients in the placebo arm. Efficacy outcomes assessed were clinical status on a seven-category ordinal scale (1, discharge; 7, death), mortality, time to hospital discharge, and mechanical ventilation (if not receiving it at randomization) through day 28. Prognostic and predictive biomarkers were evaluated continuously with proportional odds, binomial or Fine-Gray models, and additional sensitivity analyses. Modeling in the placebo arm showed all candidate biomarkers except lactate dehydrogenase and d-dimer were strongly prognostic for day 28 clinical outcomes of mortality, mechanical ventilation, clinical status, and time to hospital discharge. Modeling in the tocilizumab arm showed a predictive value of ferritin for day 28 clinical outcomes of mortality (predictive interaction, p = 0.03), mechanical ventilation (predictive interaction, p = 0.01), and clinical status (predictive interaction, p = 0.02) compared with placebo., Conclusions: Multiple biomarkers prognostic for clinical outcomes were confirmed in COVACTA. Ferritin was identified as a predictive biomarker for the effects of tocilizumab in the COVACTA patient population; high ferritin levels were associated with better clinical outcomes for tocilizumab compared with placebo at day 28., Competing Interests: Drs. Tom, Bao, Tsai, Carrasco-Triguero, and Cai received funding from the Biomedical Advanced Research and Development Authority (BARDA) under OT number HHSO100201800036C. Drs. Tom, Bao, Tsai, Qamra, Summers, Carrasco-Triguero, McBride, Rosenberger, Lin, Stubbings, and Cai disclosed they are employees of Genetech/Roche. Drs. Tom and Cai disclosed that they have a patent pending to Genetech for biomarkers for predicting response to an interleukin (IL)–6 antagonist (P36367-US). Drs. Bao, Tsai, and van der Leest received funding from Roche/Genetech. Dr. Bao received support for article research from BARDA. Drs. Bao and Tsai disclosed a patent pending for a method for treating pneumonia, including coronavirus disease 2019 pneumonia with an IL-6 antagonist (EFS ID 38946141). Drs. Bao and Blyth disclosed government work. Drs. Bao, Tsai, Qamra, Summers, Carrasco-Triguero, McBride, Stubbings, Haslem, and Cai disclosed the off-label product use of tocilizumab (Actemra). Dr. Qamra received funding from Hoffman-La Roche Canada. Dr. Summers received funding from Roche Products Ltd. Drs. McBride, Rosenberger, and Lin disclosed they own stock/stock options of Genetech. Drs. McBride, Rosenberger, and Cai disclosed work for hire. Dr. Stubbings is an employee of F Hoffmann-La Roche AG. Dr. Blyth received funding from Rocket Medical UK Ltd. Dr. Carratalà’s institution received funding from Gilead and Roche. Dr. François received funding from Aridis, AM-Pharma, Asahi Kasei, Inotrem, GlaxoSmithKline, Enlivex, Polyphor, Takeda, Transgene, and Biomérieux. Dr. Benfield received funding from Novo Nordisk, Simonsen, GlaxoSmithKline, Pfizer, Gilead, Lundbeck, Kai Hansen Foundation and personal fees from GlaxoSmithKline, Pfizer, Boehringer Ingelheim, Gilead, and Merck Sharp & Dohme outside the submitted work. Dr. Bonfanti received funding from Viiv, Gilead, and Janssen. Dr. van der Leest received funding from Bristol Myers Squibb, Merck Sharpe & Dohme, AbbVie, Boehringer Ingelheim, and AstraZeneca. Dr. Luyt’s institution received funding from Roche; he received funding from Correvio, Bayer Healthcare, Aerogen, ThermoFisher Brahms, Merck Sharpe & Dohme, Carmat, and Biomérieux. Dr. Luyt reports a grant from Roche to the Institute of Cardiometabolism and Nutrition, Sorbonne Université, Hôpital de la Pitié Salpêtrière, Assistance Publique - Hôpitaux de Paris for the COVACTA trial and a grant from Correvio and personal fees from Bayer Healthcare, Aerogen, ThermoFisher Brahms, Merck Sharp & Dohme, and Biomérieux outside the submitted work. Dr. Kheradmand received support for article research from the National Institutes of Health. Dr. Rosas received funding from Boehringer Ingelheim, Bristol Myers Squibb, and Immunomet. He reports a grant from Roche for the COVACTA trial and a grant and personal fees from Genentech/Roche outside the submitted work. Dr. Cai’s institution received funding from F. Hoffman-La Roche Ltd and the U.S. Department of Health and Human Services, the Office of the Assistant Secretary for Preparedness and Response. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc.)

Background: Interferon-β 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-β 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-β, 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-β 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-β therapy. These patients were more severely ill. Adjusted HR for mortality with early interferon-β 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-β treatment was analyzed as a time-dependent variable., Conclusions: In this multicenter cohort of admitted COVID-19 patients, receiving early interferon-β therapy after hospital admission did not show an association with lower mortality. Whether interferon-β might be useful in the earlier stages of the disease or specific subgroups of patients requires further research., (Copyright © 2021 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Background: Hyperglycemia and obesity are associated with a worse prognosis in subjects with COVID-19 independently. Their interaction as well as the potential modulating effects of additional confounding factors is poorly known. Therefore, we aimed to identify and evaluate confounding factors affecting the prognostic value of obesity and hyperglycemia in relation to mortality and admission to the intensive care unit (ICU) due to COVID-19., Methods: Consecutive patients admitted in two Hospitals from Italy (Bologna and Rome) and three from Spain (Barcelona and Girona) as well as subjects from Primary Health Care centers. Mortality from COVID-19 and risk for ICU admission were evaluated using logistic regression analyses and machine learning (ML) algorithms., Results: As expected, among 3,065 consecutive patients, both obesity and hyperglycemia were independent predictors of ICU admission. A ML variable selection strategy confirmed these results and identified hyperglycemia, blood hemoglobin and serum bilirubin associated with increased mortality risk. In subjects with blood hemoglobin levels above the median, hyperglycemic and morbidly obese subjects had increased mortality risk than normoglycemic individuals or non-obese subjects. However, no differences were observed among individuals with hemoglobin levels below the median. This was particularly evident in men: those with severe hyperglycemia and hemoglobin concentrations above the median had 30 times increased mortality risk compared with men without hyperglycemia. Importantly, the protective effect of female sex was lost in subjects with increased hemoglobin levels., Conclusions: Blood hemoglobin substantially modulates the influence of hyperglycemia on increased mortality risk in patients with COVID-19. Monitoring hemoglobin concentrations seem of utmost importance in the clinical settings to help clinicians in the identification of patients at increased death risk., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Mayneris-Perxachs, Russo, Ramos, de Hollanda, Arxé, Rottoli, Arnoriaga-Rodríguez, Comas-Cufí, Bartoletti, Verrastro, Gudiol, Fages, Giménez, Gil, Bernante, Tinahones, Carratalà, Pagotto, Hernández-Aguado, Fernández-Aranda, Meira, Castro Guardiola, Mingrone and Fernández-Real.)

Objectives: To determine the health status, exercise capacity, and health related quality of life (HRQoL) of COVID-19 associated acute respiratory distress syndrome (ARDS) survivors, 8 months after diagnosis., Methods: All eligible patients were interviewed and underwent a physical examination, chest X-ray, and 6 min walk test (6MWT). Scales to evaluate post-traumatic stress disorder, depression, anxiety, and HRQoL were applied., Results: Of 1295 patients, 365 suffered ARDS and 166 survived to hospital discharge. Five died after discharge and 48 were lost to follow-up. Of the 113 remaining patients, 81% had persistent symptoms. More than 50% of patients completed less than 80% of the theoretical distance on the 6MWT, 50% had an abnormal X-ray and 93% of patients developed psychiatric disorders. Mean SF-36 scores were worse than in the general population. After multivariate regression analysis, female sex, non-Caucasian race, and Charlson index>2 were independent risk factors for a worse mental health component summary score on the SF-36, and age was associated with a better prognosis. Female sex and chronic obstructive pulmonary disease were independently associated with a worse physical component summary score., Conclusion: COVID-19 associated ARDS survivors have long-term consequences in health status, exercise capacity, and HRQoL. Strategies addressed to prevent these sequelae are needed., Competing Interests: Declaration of Competing Interest There are no funders to declare., (Copyright © 2021. Published by Elsevier Ltd.)

Objectives: The effect of the use of immunomodulatory drugs on the risk of developing hospital-acquired bloodstream infection (BSI) in patients with COVID-19 has not been specifically assessed. We aim to identify risk factors for, and outcomes of, BSI among hospitalized patients with severe COVID-19 pneumonia., Methods: We performed a severity matched case-control study (1:1 ratio) nested in a large multicentre prospective cohort of hospitalized adults with COVID-19. Cases with BSI were identified from the cohort database. Controls were matched for age, sex and acute respiratory distress syndrome. A Cox proportional hazard ratio model was performed., Results: Of 2005 patients, 100 (4.98%) presented 142 episodes of BSI, mainly caused by coagulase-negative staphylococci, Enterococcus faecalis and Pseudomonas aeruginosa. Polymicrobial infection accounted for 23 episodes. The median time from admission to the first episode of BSI was 15 days (IQR 9-20), and the most frequent source was catheter-related infection. The characteristics of patients with and without BSI were similar, including the use of tocilizumab, corticosteroids, and combinations. In the multivariate analysis, the use of these immunomodulatory drugs was not associated with an increased risk of BSI. A Cox proportional hazard ratio (HR) model showed that after adjusting for the time factor, BSI was associated with a higher in-hospital mortality risk (HR 2.59; 1.65-4.07; p < 0.001)., Discussion: Hospital-acquired BSI in patients with severe COVID-19 pneumonia was uncommon and the use of immunomodulatory drugs was not associated with its development. When adjusting for the time factor, BSI was associated with a higher mortality risk., (Copyright © 2021. Published by Elsevier Ltd.)

Objective: To develop and validate a prediction model of mortality in patients with COVID-19 attending hospital emergency rooms., Design: Multivariable prognostic prediction model., Setting: 127 Spanish hospitals., Participants: Derivation (DC) and external validation (VC) cohorts were obtained from multicentre and single-centre databases, including 4035 and 2126 patients with confirmed COVID-19, respectively., Interventions: Prognostic variables were identified using multivariable logistic regression., Main Outcome Measures: 30-day mortality., Results: Patients' characteristics in the DC and VC were median age 70 and 61 years, male sex 61.0% and 47.9%, median time from onset of symptoms to admission 5 and 8 days, and 30-day mortality 26.6% and 15.5%, respectively. Age, low age-adjusted saturation of oxygen, neutrophil-to-lymphocyte ratio, estimated glomerular filtration rate by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, dyspnoea and sex were the strongest predictors of mortality. Calibration and discrimination were satisfactory with an area under the receiver operating characteristic curve with a 95% CI for prediction of 30-day mortality of 0.822 (0.806-0.837) in the DC and 0.845 (0.819-0.870) in the VC. A simplified score system ranging from 0 to 30 to predict 30-day mortality was also developed. The risk was considered to be low with 0-2 points (0%-2.1%), moderate with 3-5 (4.7%-6.3%), high with 6-8 (10.6%-19.5%) and very high with 9-30 (27.7%-100%)., Conclusions: A simple prediction score, based on readily available clinical and laboratory data, provides a useful tool to predict 30-day mortality probability with a high degree of accuracy among hospitalised patients with COVID-19., Competing Interests: Competing interests: JB reports grants and personal fees from GILEAD, MSD and ViiV Healthcare; and personal fees from JANSSEN, outside the submitted work. PR reports grants and personal fees from GILEAD and MSD; and personal fees from AbbVie and ViiV Healthcare, outside the submitted work. IJ reports personal fees from GILEAD and ViiV Healthcare, outside the submitted work. JRA reports grants and personal fees from GILEAD and ViiV Healthcare; and personal fees from ALEXA, MSD, JANSSEN, SERONO and TEVA, outside the submitted work. The remaining authors have nothing to disclose., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality., Methods: In this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model., Findings: Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)-phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2·5% (95% CI 1·4-4·3) for patients with phenotype A, 30·5% (28·5-32·6) for patients with phenotype B, and 60·7% (53·7-67·2) for patients with phenotype C (log-rank test p<0·0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5·3% [95% CI 3·4-8·1] for phenotype A, 31·3% [28·5-34·2] for phenotype B, and 59·5% [48·8-69·3] for phenotype C; external validation cohort: 3·7% [2·0-6·4] for phenotype A, 23·7% [21·8-25·7] for phenotype B, and 51·4% [41·9-60·7] for phenotype C)., Interpretation: Patients admitted to hospital with COVID-19 can be classified into three phenotypes that correlate with mortality. We developed and validated a simplified tool for the probabilistic assignment of patients into phenotypes. These results might help to better classify patients for clinical management, but the pathophysiological mechanisms of the phenotypes must be investigated., Funding: Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation, and Fundación SEIMC/GeSIDA., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Introduction: Community-acquired pneumonia (CAP) continues to be a major health problem worldwide and is one of the main reasons for prescribing antibiotics. However, the causative agent is often not identified, resulting in antibiotic overtreatment, which is a key driver of antimicrobial resistance and adverse events. We aim to test the hypothesis that comprehensive molecular testing, compared with routine microbiological testing, would be effective in reducing antibiotic use in patients with CAP., Methods and Analysis: We will perform a randomised, controlled, open-label clinical trial with two parallel groups (1:1) at two tertiary hospitals between 2020 and 2022. Non-severely immunosuppressed adults hospitalised for CAP will be considered eligible. Patients will be randomly assigned to receive either the experimental diagnosis (comprehensive molecular testing plus routine microbiological testing) or standard diagnosis (only microbiological routine testing). The primary endpoint will be antibiotic consumption measured as days of antibiotic therapy per 1000 patient-days. Secondary endpoints will be de-escalation to narrower antibiotic treatment, time to switch from intravenous to oral antibiotics, days to reaching an aetiological diagnosis, antibiotic-related side effects, length of stay, days to clinical stability, intensive care unit admission, days of mechanical ventilation, hospital readmission up to 30 days after randomisation and death from any cause by 48 hours and 30 days after randomisation. We will need to include 440 subjects to be able to reject the null hypothesis that both groups have equal days of antibiotic therapy per 1000 patient-days with a probability >0.8., Ethics and Dissemination: Ethical approval has been obtained from the Ethics Committee of Bellvitge Hospital (AC028/19) and from the Spanish Medicines and Medical Devices Agency, and it is valid for all participating centres under existing Spanish legislation. Results will be presented at international meetings and will be made available to patients, their caregivers and funders., Trial Registration Number: ClinicalTrials: NCT04158492. EudraCT: 2018-004880-29., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

We assessed the antibiotic use in SARS-CoV-2-infected patients during four different waves of the COVID-19 pandemic, as well as its trends over the period and associated risk factors. We performed a cross-sectional retrospective analysis nested in a prospectively collected cohort of hospitalized adult patients with COVID-19 at a university hospital in Spain. A total of 2415 patients were included in this study, among whom 1120 corresponded to the first wave. The highest percentage of patients receiving some sort of antibiotic treatment was higher during the first wave (77.6%) than during the others; nevertheless, our calculation of the average DOT (days of antibiotic treatment) per 100 patient days of stay found that the highest antibiotic prescription rate corresponded to the second pandemic wave (61.61 DOT/100 patient days), which was associated with a higher ICU admission rate and a lower SpO2/FiO2 ratio at admission. After the second wave, the prescription rates presented a steady downward trend. With regard to the use of specific antibiotic families, amoxicillin/clavulanate was the most used antibiotic in our cohort (14.20 DOT/100 patient days) due to a high prescription rate during the first wave. According to the "AWaRe" WHO classification, antibiotics corresponding to the "Watch" group were the most prescribed (27.92 DOT/100 patient days). The antibiotic use rate fell progressively, but it remained high during all four waves analyzed. In conclusion, antibiotic use was high throughout all the waves that were analyzed, despite a relatively low incidence of bacterial coinfection and superinfection. Efforts should be made to keep antimicrobial stewardship programs active, especially in complicated epidemiological situations, such as the SARS-CoV-2 pandemic. [ABSTRACT FROM AUTHOR]

Aim: To analyze trends in the prescription of COVID-19 treatments for hospitalized patients during the pandemic. Methods: Multicenter, ecological, time-series study of aggregate data for all adult patients with COVID-19 treated in five acute-care hospitals in Barcelona, Spain, between March 2020 and May 2021. Trends in the monthly prevalence of drugs used against COVID-19 were analyzed by the Mantel–Haenszel test. Results: The participating hospitals admitted 22,277 patients with COVID-19 during the study period, reporting an overall mortality of 10.8%. In the first months of the pandemic, lopinavir/ritonavir and hydroxychloroquine were the most frequently used antivirals, but these fell into disuse and were replaced by remdesivir in July 2020. By contrast, the trend in tocilizumab use varied, first peaking in April and May 2020, declining until January 2021, and showing a discrete upward trend thereafter. Regarding corticosteroid use, we observed a notable upward trend in the use of dexamethasone 6 mg per day from July 2020. Finally, there was a high prevalence of antibiotics use, especially azithromycin, in the first three months, but this decreased thereafter. Conclusions: Treatment for patients hospitalized with COVID-19 evolved with the changing scientific evidence during the pandemic. Initially, multiple drugs were empirically used that subsequently could not demonstrate clinical benefit. In future pandemics, stakeholders should strive to promote the early implementation of adaptive randomized clinical trials. [ABSTRACT FROM AUTHOR]

The clinical manifestations of SARS-CoV-2 infection vary widely, from asymptomatic infection to the development of acute respiratory distress syndrome (ARDS) and death. The host response elicited by SARS-CoV-2 plays a key role in determining the clinical outcome. We hypothesized that determining the dynamic whole blood transcriptomic profile of hospitalized adult COVID-19 patients and characterizing the subgroup that develops severe disease and ARDS would broaden our understanding of the heterogeneity in clinical outcomes. We recruited 60 hospitalized patients with RT-PCR-confirmed SARS-CoV-2 infection, among whom 19 developed ARDS. Peripheral blood was collected using PAXGene RNA tubes within 24 h of admission and on day 7. There were 2572 differently expressed genes in patients with ARDS at baseline and 1149 at day 7. We found a dysregulated inflammatory response in COVID-19 ARDS patients, with an increased expression of genes related to pro-inflammatory molecules and neutrophil and macrophage activation at admission, in addition to an immune regulation loss. This led, in turn, to a higher expression of genes related to reactive oxygen species, protein polyubiquitination, and metalloproteinases in the latter stages. Some of the most significant differences in gene expression found between patients with and without ARDS corresponded to long non-coding RNA involved in epigenetic control. [ABSTRACT FROM AUTHOR]

Patients with coronavirus disease 2019 (COVID-19) have an increased risk of ventilator-associated pneumonia (VAP). This systematic review updates information on the causative agents of VAP and resistance to antibiotics in COVID-19 patients. We searched the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed/MEDLINE, and LILACS databases from December 2019 to December 2021. Studies that described the frequency of causative pathogens associated with VAP and their antibiotic resistance patterns in critically ill COVID-19 adult patients were included. The Newcastle-Ottawa Quality Assessment Scale was used for critical appraisal. The data are presented according to the number or proportions reported in the studies. A total of 25 articles were included, involving 2766 VAP cases in COVID-19 patients (range 5–550 VAP cases). Most of the studies included were carried out in France (32%), Italy (20%), Spain (12%) and the United States (8%). Gram-negative bacteria were the most frequent causative pathogens of VAP (range of incidences in studies: P. aeruginosa 7.5–72.5%, K. pneumoniae 6.9–43.7%, E. cloacae 1.6–20% and A. baumannii 1.2–20%). S. aureus was the most frequent Gram-positive pathogen, with a range of incidence of 3.3–57.9%. The median incidence of Aspergillus spp. was 6.4%. Few studies have recorded susceptibility patterns among Gram-negative causative pathogens and have mainly reported extended-spectrum beta-lactamase (ESBL), AmpC, and carbapenem resistance. The median frequency of methicillin resistance among S. aureus isolates was 44.4%. Our study provides the first comprehensive description of the causative agents and antibiotic resistance in COVID-19 patients with VAP. Gram-negative bacteria were the most common pathogens causing VAP. Data on antibiotic resistance patterns in the published medical literature are limited, as well as information about VAP from low- and middle-income countries. [ABSTRACT FROM AUTHOR]

The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22–0.47; p < 0.001) for tocilizumab, 0.82 (0.71–1.30; p 0.82) for IHDC, 0.61 (0.43–0.86; p 0.006) for PDC, and 1.17 (0.86–1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02–0.17; p < 0.001). Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situation. Image 1 [ABSTRACT FROM AUTHOR]

To analyse the characteristics and predictors of death in hospitalized patients with coronavirus disease 2019 (COVID-19) in Spain. A retrospective observational study was performed of the first consecutive patients hospitalized with COVID-19 confirmed by real-time PCR assay in 127 Spanish centres until 17 March 2020. The follow-up censoring date was 17 April 2020. We collected demographic, clinical, laboratory, treatment and complications data. The primary endpoint was all-cause mortality. Univariable and multivariable Cox regression analyses were performed to identify factors associated with death. Of the 4035 patients, male subjects accounted for 2433 (61.0%) of 3987, the median age was 70 years and 2539 (73.8%) of 3439 had one or more comorbidity. The most common symptoms were a history of fever, cough, malaise and dyspnoea. During hospitalization, 1255 (31.5%) of 3979 patients developed acute respiratory distress syndrome, 736 (18.5%) of 3988 were admitted to intensive care units and 619 (15.5%) of 3992 underwent mechanical ventilation. Virus- or host-targeted medications included lopinavir/ritonavir (2820/4005, 70.4%), hydroxychloroquine (2618/3995, 65.5%), interferon beta (1153/3950, 29.2%), corticosteroids (1109/3965, 28.0%) and tocilizumab (373/3951, 9.4%). Overall, 1131 (28%) of 4035 patients died. Mortality increased with age (85.6% occurring in older than 65 years). Seventeen factors were independently associated with an increased hazard of death, the strongest among them including advanced age, liver cirrhosis, low age-adjusted oxygen saturation, higher concentrations of C-reactive protein and lower estimated glomerular filtration rate. Our findings provide comprehensive information about characteristics and complications of severe COVID-19, and may help clinicians identify patients at a higher risk of death. Image 1 [ABSTRACT FROM AUTHOR]