Predictive factors of six-week mortality in critically ill patients with SARS-CoV-2: A multicenter prospective study.

The objective of the study is to identify the risk factors associated with mortality at six weeks, especially by analyzing the role of antivirals and munomodulators. Prospective descriptive multicenter cohort study. 26 Intensive care units (ICU) from Andalusian region in Spain. Consecutive critically ill patients with confirmed SARS-CoV-2 infection were included from March 8 to May 30. None. Variables analyzed were demographic, severity scores and clinical condition. Support therapy, drug and mortality were analyzed. An univariate followed by multivariate Cox regression with propensity score analysis was applied. 495 patients were enrolled, but 73 of them were excluded for incomplete data. Thus, 422 patients were included in the final analysis. Median age was 63 years and 305 (72.3%) were men. ICU mortality: 144/422 34%; 14 days mortality: 81/422 (19.2%); 28 days mortality: 121/422 (28.7%); 6-week mortality 152/422 36.5%. By multivariable Cox proportional analysis, factors independently associated with 42-day mortality were age, APACHE II score, SOFA score at ICU admission >6, Lactate dehydrogenase at ICU admission >470U/L, Use of vasopressors, extrarenal depuration, %lymphocytes 72h post-ICU admission 6, Lactate dehydrogenase at ICU admission >470U/L, Use of vasopressors, extrarenal depuration, %lymphocytes 72h post-ICU admission 470U/L, Use of vasopressors, extrarenal depuration, %lymphocytes 72h post-ICU admission Age, APACHE II, SOFA>value of 6 points, along with vasopressor requirements or renal replacement therapy have been identified as predictor factors of mortality at six weeks. Administration of corticosteroids showed no benefits in mortality, as did treatment with tocilizumab. Lopinavir/ritonavir administration is identified as a protective factor

The association of cardiovascular failure with treatment for ventilator-associated lower respiratory tract infection

Purpose: Ventilator associated-lower respiratory tract infections (VA-LRTIs), either ventilator-associated pneumonia (VAP) or tracheobronchitis (VAT), accounts for most nosocomial infections in intensive care units (ICU) including. Our aim was to determine if appropriate antibiotic treatment in patients with VA-LRTI will effectively reduce mortality in patients who had cardiovascular failure. Methods: This was a pre-planned subanalysis of a large prospective cohort of mechanically ventilated patients for at least 48 h in eight countries in two continents. Patients with a modified Sequential Organ Failure Assessment (mSOFA) cardiovascular score of 4 (at the time of VA-LRTI diagnosis and needed be present for at least 12 h) were defined as having cardiovascular failure. Results: VA-LRTI occurred in 689 (23.2%) out of 2960 patients and 174 (25.3%) developed cardiovascular failure. Patients with cardiovascular failure had significantly higher ICU mortality than those without (58% vs. 26.8%; p < 0.001; OR 3.7; 95% CI 2.6–5.4). A propensity score analysis found that the presence of inappropriate antibiotic treatment was an independent risk factor for ICU mortality in patients without cardiovascular failure, but not in those with cardiovascular failure. When the propensity score analysis was conducted in patients with VA-LRTI, the use of appropriate antibiotic treatment conferred a survival benefit for patients without cardiovascular failure who had only VAP. Conclusions: Patients with VA-LRTI and cardiovascular failure did not show an association to a higher ICU survival with appropriate antibiotic treatment. Additionally, we found that in patients without cardiovascular failure, appropriate antibiotic treatment conferred a survival benefit for patients only with VAP. Trial registry: ClinicalTrials.gov, number NCT01791530. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature

CTA contributions to the 33rd International Cosmic Ray Conference (ICRC2013)

Compilation of CTA contributions to the proceedings of the 33rd International Cosmic Ray Conference (ICRC2013), which took place in 2-9 July, 2013, in Rio de Janeiro, BrazilComment: Index of CTA conference proceedings at the ICRC2013, Rio de Janeiro (Brazil). v1: placeholder with no arXiv links yet, to be replaced once individual contributions have been all submitted. v2: final with arXiv links to all CTA contributions and full author lis

CTA Contributions to the 34th International Cosmic Ray Conference (ICRC2015)

List of contributions from the CTA Consortium presented at the 34th International Cosmic Ray Conference, 30 July - 6 August 2015, The Hague, The Netherlands.Comment: Index of CTA conference proceedings at the ICRC2015, The Hague (The Netherlands). v1: placeholder with no arXiv links yet, to be replaced once individual contributions have been all submitted; v2: final with arXiv links to all CTA contributions and full author lis

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use