Extracellular vesicle features are associated with COVID-19 severity.

COVID-19 is heterogeneous; therefore, it is crucial to identify early biomarkers for adverse outcomes. Extracellular vesicles (EV) are involved in the pathophysiology of COVID-19 and have both negative and positive effects. The objective of this study was to identify the potential role of EV in the prognostic stratification of COVID-19 patients. A total of 146 patients with severe or critical COVID-19 were enrolled. Demographic and comorbidity characteristics were collected, together with routine haematology, blood chemistry and lymphocyte subpopulation data. Flow cytometric characterization of the dimensional and antigenic properties of COVID-19 patients' plasma EVs was conducted. Elastic net logistic regression with cross-validation was employed to identify the best model for classifying critically ill patients. Features of smaller EVs (i.e. the fraction of EVs smaller than 200 nm expressing either cluster of differentiation [CD] 31, CD 140b or CD 42b), albuminemia and the percentage of monocytes expressing human leukocyte antigen DR (HLA-DR) were associated with a better outcome. Conversely, the proportion of larger EVs expressing N-cadherin, CD 34, CD 56, CD31 or CD 45, interleukin 6, red cell width distribution (RDW), N-terminal pro-brain natriuretic peptide (NT-proBNP), age, procalcitonin, Charlson Comorbidity Index and pro-adrenomedullin were associated with disease severity. Therefore, the simultaneous assessment of EV dimensions and their antigenic properties complements laboratory workup and helps in patient stratification.

Identification of COVID-19 patients at risk of hospital admission and mortality: a European multicentre retrospective analysis of mid-regional pro-adrenomedullin.

BACKGROUND: Mid-Regional pro-Adrenomedullin (MR-proADM) is an inflammatory biomarker that improves the prognostic assessment of patients with sepsis, septic shock and organ failure. Previous studies of MR-proADM have primarily focussed on bacterial infections. A limited number of small and monocentric studies have examined MR-proADM as a prognostic factor in patients infected with SARS-CoV-2, however there is need for multicenter validation. An evaluation of its utility in predicting need for hospitalisation in viral infections was also performed. METHODS: An observational retrospective analysis of 1861 patients, with SARS-CoV-2 confirmed by RT-qPCR, from 10 hospitals across Europe was performed. Biomarkers, taken upon presentation to Emergency Departments (ED), clinical scores, patient demographics and outcomes were collected. Multiclass random forest classifier models were generated as well as calculation of area under the curve analysis. The primary endpoint was hospital admission with and without death. RESULTS: Patients suitable for safe discharge from Emergency Departments could be identified through an MR-proADM value of ≤ 1.02 nmol/L in combination with a CRP (C-Reactive Protein) of ≤ 20.2 mg/L and age ≤ 64, or in combination with a SOFA (Sequential Organ Failure Assessment) score < 2 if MR-proADM was ≤ 0.83 nmol/L regardless of age. Those at an increased risk of mortality could be identified upon presentation to secondary care with an MR-proADM value of > 0.85 nmol/L, in combination with a SOFA score ≥ 2 and LDH > 720 U/L, or in combination with a CRP > 29.26 mg/L and age ≤ 64, when MR-proADM was > 1.02 nmol/L. CONCLUSIONS: This international study suggests that for patients presenting to the ED with confirmed SARS-CoV-2 infection, MR-proADM in combination with age and CRP or with the patient's SOFA score could identify patients at low risk where outpatient treatment may be safe.

Effect of heparin treatment on pulmonary embolism and in-hospital death in unvaccinated COVID-19 patients without overt deep vein thrombosis.

BACKGROUND: Pulmonary embolism (PE) without overt deep vein thrombosis (DVT) was common in hospitalized coronavirus-induced disease (COVID)-19 patients and represented a diagnostic, prognostic, and therapeutic challenge. The aim of this study was to analyze the prognostic role of PE on mortality and the preventive effect of heparin on PE and mortality in unvaccinated COVID-19 patients without overt DVT. METHODS: Data from 401 unvaccinated patients (age 68 ± 13 years, 33% females) consecutively admitted to the intensive care unit or the medical ward were included in a retrospective longitudinal study. PE was documented by computed tomography scan and DVT by compressive venous ultrasound. The effect of PE diagnosis and any heparin use on in-hospital death (primary outcome) was analyzed by a classical survival model. The preventive effect of heparin on either PE diagnosis or in-hospital death (secondary outcome) was analyzed by a multi-state model after having reclassified patients who started heparin after PE diagnosis as not treated. RESULTS: Median follow-up time was 8 days (range 1-40 days). PE cumulative incidence and in-hospital mortality were 27% and 20%, respectively. PE was predicted by increased D-dimer levels and COVID-19 severity. Independent predictors of in-hospital death were age (hazards ratio (HR) 1.05, 95% confidence interval (CI) 1.03-1.08, p < 0.001), body mass index (HR 0.93, 95% CI 0.89-0.98, p = 0.004), COVID-19 severity (severe versus mild/moderate HR 3.67, 95% CI 1.30-10.4, p = 0.014, critical versus mild/moderate HR 12.1, 95% CI 4.57-32.2, p < 0.001), active neoplasia (HR 2.58, 95% CI 1.48-4.50, p < 0.001), chronic obstructive pulmonary disease (HR 2.47; 95% CI 1.15-5.27, p = 0.020), respiratory rate (HR 1.06, 95% CI 1.02-1.11, p = 0.008), heart rate (HR 1.03, 95% CI 1.01-1.04, p < 0.001), and any heparin treatment (HR 0.35, 95% CI 0.18-0.67, p = 0.001). In the multi-state model, preventive heparin at prophylactic or intermediate/therapeutic dose, compared with no treatment, reduced PE risk and in-hospital death, but it did not influence mortality of patients with a PE diagnosis. CONCLUSIONS: PE was common during the first waves pandemic in unvaccinated patients, but it was not a negative prognostic factor for in-hospital death. Heparin treatment at any dose prevented mortality independently of PE diagnosis, D-dimer levels, and disease severity.

Clinical characteristics and outcome of 125 polymicrobial bloodstream infections in hematological patients: an 11-year epidemiologic survey.

BACKGROUND: Polymicrobial bloodstream infections (pBSI) occurring in hematological patients are still poorly understood, and specific information are very limited. OBJECTIVES AND METHODS: In this epidemiologic survey, we describe clinical characteristics and outcome of 125 consecutive pBSI occurred in oncohematological patients. Polymicrobial bloodstream infections (pBSI) were defined with the isolation of 2 or more bacteria from blood culture specimens obtained within 72 h. RESULTS: Over an 11-year period, we documented 500 bacterial bloodstream infections (BSI) in 4542 hospital admissions and 25% (125) of these were pBSI. Most common underlying hematological disease was acute myeloid leukemia and 89% of patients had severe neutropenia. Fifty pBSI (40%) occurred in patients undergoing a stem cell transplantation (SCT), mostly within 30 days from transplant (42/50-84%). Principal bacterial association was Gram-positive plus Gram-negative (57%). Resolution rate of pBSI was 82%, without differences between SCT and non-SCT cases. pBSI-related mortality was 15% (6% in SCT cases). Septic shock occurred in 16% of cases and septic shock-related mortality was 65% (75% in SCT cases and 63% in non-SCT cases; p = 0.6). Multidrug-resistant (MDR) bacteria were involved in 22% of pBSI and the MDR-pBSI-related mortality was significantly higher in SCT patients (p = 0.007). CONCLUSIONS: This observational study highlights that pBSI is not a rare bloodstream infectious complication in oncohematological patients. pBSI-related mortality is lower than 20%, but, if septic shock occurs, mortality reaches 65%. MDR bacteria were involved in 22% of cases and pBSI-MDR-related mortality was significantly higher in SCT patients.

Cytokines from Bench to Bedside: A Retrospective Study Identifies a Definite Panel of Biomarkers to Early Assess the Risk of Negative Outcome in COVID-19 Patients.

The main aim of this study was to identify the most relevant cytokines which, when assessed in the earliest stages from hospital admission, may help to select COVID-19 patients with worse prognosis. A retrospective observational study was conducted in 415 COVID-19 patients (272 males; mean age 68 ± 14 years) hospitalized between May 2020 and March 2021. Within the first 72 h from hospital admission, patients were tested for a large panel of biomarkers, including C-reactive protein (CRP), Mid-regional proadrenomedullin (MR-proADM), Interferon-γ, interleukin 6 (IL-6), IL-1ß, IL-8, IL-10, soluble IL2-receptor-α (sIL2Rα), IP10 and TNFα. Extensive statistical analyses were performed (correlations, t-tests, ranking tests and tree modeling). The mortality rate was 65/415 (15.7%) and a negative outcome (death and/or orotracheal intubation) affected 98/415 (23.6%) of cases. Univariate tests showed the majority of biomarkers increased in severe patients, but ranking tests helped to select the best variables to put on decisional tree modeling which identified IL-6 as the first dichotomic marker with a cut-off of 114 pg/mL. Then, a good synergy was found between IL-10, MR-proADM, sIL2Rα, IP10 and CRP in increasing the predictive value in classifying patients at risk or not for a negative outcome. In conclusion, beside IL-6, a panel of other cytokines representing the degree of immunoparalysis and the anti-inflammatory response (IP10, sIL2Rα and IL-10) showed synergic role when combined to biomarkers of systemic inflammation and endothelial dysfunction (CRP, MR-proADM) and may also better explain disease pathogenesis and suggests targeted intervention.

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery.

The persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a "non-severe" and 80 had a "severe" outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases. The main clinical and hematochemical data associated with disease outcome were grouped with serological data to form a dataset for the supervised machine learning techniques. We identified nine proteins (i.e., CD200R1, MCP1, MCP3, IL6, LTBP2, MATN3, TRANCE, α2-MRAP, and KIT) that contributed to the correct classification of COVID-19 disease severity when combined with relative neutrophil and lymphocyte counts. By analyzing PEA, clinical and hematochemical data with statistical methods that were able to handle many variables in the presence of a relatively small sample size, we identified nine potential serum biomarkers of a "severe" outcome. Most of these were confirmed by literature data. Importantly, we found three biomarkers associated with central nervous system pathologies and protective factors, which were downregulated in the most severe cases.

Recognition in Emergency Department of Septic Patients at Higher Risk of Death: Beware of Patients without Fever.

Background and Objectives: Chances of surviving sepsis increase markedly upon prompt diagnosis and treatment. As most sepsis cases initially show-up in the Emergency Department (ED), early recognition of a septic patient has a pivotal role in sepsis management, despite the lack of precise guidelines. The aim of this study was to identify the most accurate predictors of in-hospital mortality outcome in septic patients admitted to the ED. Materials and Methods: We compared 651 patients admitted to ED for sepsis (cases) with 363 controls (non-septic patients). A Bayesian mean multivariate logistic regression model was performed in order to identify the most accurate predictors of in-hospital mortality outcomes in septic patients. Results: Septic shock and positive qSOFA were identified as risk factors for in-hospital mortality among septic patients admitted to the ED. Hyperthermia was a protective factor for in-hospital mortality. Conclusions: Physicians should bear in mind that fever is not a criterium for defining sepsis; according to our results, absence of fever upon presentation might be indicative of greater severity and diagnosis of sepsis should not be delayed.

Personalized machine learning approach to predict candidemia in medical wards.

PURPOSE: Candidemia is a highly lethal infection; several scores have been developed to assist the diagnosis process and recently different models have been proposed. Aim of this work was to assess predictive performance of a Random Forest (RF) algorithm for early detection of candidemia in the internal medical wards (IMWs). METHODS: A set of 42 potential predictors was acquired in a sample of 295 patients (male: 142, age: 72 ± 15 years; candidemia: 157/295; bacteremia: 138/295). Using tenfold cross-validation, a RF algorithm was compared with a classic stepwise multivariable logistic regression model; discriminative performance was assessed by C-statistics, sensitivity and specificity, while calibration was evaluated by Hosmer-Lemeshow test. RESULTS: The best tuned RF algorithm demonstrated excellent discrimination (C-statistics = 0.874 ± 0.003, sensitivity = 84.24% ± 0.67%, specificity = 91% ± 2.63%) and calibration (Hosmer-Lemeshow statistics = 12.779 ± 1.369, p = 0.120), markedly greater than the ones guaranteed by the classic stepwise logistic regression (C-statistics = 0.829 ± 0.011, sensitivity = 80.21% ± 1.67%, specificity = 84.81% ± 2.68%; Hosmer-Lemeshow statistics = 38.182 ± 15.983, p < 0.001). In addition, RF suggests a major role of in-hospital antibiotic treatment with microbioma highly impacting antimicrobials (MHIA) that are found as a fundamental risk of candidemia, further enhanced by TPN. When in-hospital MHIA therapy is not performed, PICC is the dominant risk factor for candidemia, again enhanced by TPN. When PICC is not used and MHIA therapy is not performed, the risk of candidemia is minimum, slightly increased by in-hospital antibiotic therapy. CONCLUSION: RF accurately estimates the risk of candidemia in patients admitted to IMWs. Machine learning technique might help to identify patients at high risk of candidemia, reduce the delay in empirical treatment and improve appropriateness in antifungal prescription.

AmpC ß-lactamase-producing Enterobacterales: what a clinician should know.

BACKGROUND: Enterobacterales are among the most common causes of bacterial infections in the community and among hospitalized patients, and multidrug-resistant (MDR) strains have emerged as a major threat to human health. Resistance to third-generation cephalosporins is typical of MDRs, being mainly due to the production of extended spectrum ß-lactamases or AmpC-type ß-lactamases. OBJECTIVE: The objective of this paper is to review the epidemiological impact, diagnostic issues and treatment options with AmpC producers. FINDINGS: AmpC enzymes encoded by resident chromosomal genes (cAmpCs) are produced by some species (e.g., Enterobacter spp., Citrobacter freundii, Serratia marcescens), while plasmid-encoded AmpCs (pAmpCs) can be encountered also in species that normally do not produce cAmpCs (e.g., Salmonella enterica, Proteus mirabilis, Klebsiella pneumoniae and Klebsiella oxytoca) or produce them at negligible levels (e.g., Escherichia coli). Production of AmpCs can be either inducible or constitutive, resulting in different resistance phenotypes. Strains producing cAmpCs in an inducible manner (e.g., Enterobacter spp.) usually appear susceptible to third-generation cephalosporins, which are poor inducers, but can easily yield mutants constitutively producing the enzyme which are resistant to these drugs (which are good substrates), resulting in treatment failures. pAmpCs are usually constitutively expressed. Production of pAmpCs is common in community-acquired infections, while cAmpC producers are mainly involved in healthcare-associated infections. CONCLUSIONS: To date, there is no conclusive evidence about the most appropriate treatment for AmpC-producing Enterobacterales. Carbapenems are often the preferred option, especially for severe infections in which adequate source control is not achieved, but cefepime is also supported by substantial clinical evidences as an effective carbapenem-sparing option.

Neonatal pertussis diagnosis: low procalcitonin level and high lymphocyte count are able to discriminate pertussis from bacterial and viral infections.

Pertussis is quite frequent and severe among infants; therefore, rapid diagnosis and timely targeted therapy are essential. Although a molecular test for etiological diagnosis is now available, it may not be available everywhere, and therefore adjunctive diagnostic tests are still useful for presumptive diagnosis. We describe the use of procalcitonin (PCT) and lymphocyte count to discriminate among pertussis, bacterial and viral infections. Fourteen infants per group were studied. The decision tree, built considering all available variables, showed a major role of PCT in predicting the different groups. A PCT value equal to or greater than 0.75 ng/ml selected for bacterial infections. A PCT value lower than 0.75 ng/ml and a lymphocyte count equal to or greater than 10,400/mm3 selected the subjects with pertussis, while a lymphocyte count lower than 10,400/mm3 selected for viral etiology. PCT should be used in the diagnosis of infants suspected of having pertussis.

A prediction rule for early recognition of patients with candidemia in Internal Medicine: results from an Italian, multicentric, case-control study.

PURPOSE: Increasing prevalence of candidemia in Internal Medicine wards (IMWs) has been reported in recent years, but risk factors for candida bloodstream infection in patients admitted to IMW may differ from those known in other settings. The aim of this study was to identify risk factors and define a prediction rule for the early recognition of the risk of candidemia in IMW inpatients. METHODS: This was a multicentric, retrospective, observational case-control study on non-neutropenic patients with candidemia admitted to IMWs of four large Italian Hospitals. Each eligible patient with candidemia (case) was matched to a control with bacteremia. Stepwise logistic regression analyses were performed. RESULTS: Overall, 300 patients (150 cases and 150 controls) were enrolled. The following factors were associated with an increased risk of candidemia and weighted to build a score: total parenteral nutrition (OR 2.45, p = 0.008; 1 point); central venous catheter (OR 2.19, p = 0.031; 1 point); peripherally inserted central catheter (OR 5.63, p < 0.0001; 3 points), antibiotic treatment prior (OR 2.06; p = 0.059; 1 point) and during hospitalization (OR2.38, p = 0.033; 1 point); neurological disability (OR 2.25, p = 0.01; 1 point); and previous hospitalization within 3 months (OR 1.56, p = 0.163; 1 point). At ROC curve analysis, a final score ≥ 4 showed 84% sensitivity, 76% specificity, and 80% accuracy in predicting the risk of candidemia. CONCLUSIONS: The proposed scoring system showed to be a simple and highly performing tool in distinguishing bloodstream infections due to Candida and bacteria in patients admitted to IMW. The proposed rule might help to reduce delay in empirical treatment and improve appropriateness in antifungal prescription in septic patients.

Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction.

Clinical databases typically include, for each patient, many heterogeneous features, for example blood exams, the clinical history before the onset of the disease, the evolution of the symptoms, the results of imaging exams, and many others. We here propose to exploit a recently developed statistical approach, the Information Imbalance, to compare different subsets of patient features and automatically select the set of features that is maximally informative for a given clinical purpose, especially in minority classes. We adapt the Information Imbalance approach to work in a clinical framework, where patient features are often categorical and are generally available only for a fraction of the patients. We apply this algorithm to a data set of ∼ 1300 patients treated for COVID-19 in Udine hospital before October 2021. Using this approach, we find combinations of features which, if used in combination, are maximally informative of the clinical fate and of the severity of the disease. The optimal number of features, which is determined automatically, turns out to be between 10 and 15. These features can be measured at admission. The approach can be used also if the features are available only for a fraction of the patients, does not require imputation and, importantly, is able to automatically select features with small inter-feature correlation. Clinical insights deriving from this study are also discussed.

Psychological factors associated with vaccination hesitancy: an observational study of patients hospitalized for COVID-19 in a later phase of the pandemic in Italy.

Introduction: Vaccination against SARS-CoV-2 has been used to reduce the severity of COVID-19 disease and the incidence of new cases. However, a significant proportion of people have shown vaccination hesitancy. Methods: This study explored psychological factors related to vaccination hesitancy in a sample of Italian COVID-19 patients (N = 54), hospitalized during 2021, after vaccines had been made available and while the vaccination campaign was on-going. Consecutive patients, aged 18 or older, admitted to the hospital with a diagnosis of COVID-19 were assessed with a set of standardized measures. Results: In our sample, 48.1% was not vaccinated and 7.4% died within 6months after hospitalization, with a preponderance of deaths among non-vaccinated patients. Non-vaccinated participants had higher resilience scores at the CD-RISC-10 scale than vaccinated ones (33.6 ± 5.50 vs 28.6 ± 6.61; t40.2=+ 2.94, p = 0.005). No statistically significant differences were found between the two groups for any other measures. Discussion: Higher levels of resilience among non-vaccinated patients may reflect greater identity worth and self-esteem, in turn resulting in a decrease in vaccination likelihood. This finding may have important public health implications, as it indicates that specific psychological aspects, such as resilience, may result in vaccination hesitancy, with implications for hospitalization rates, and thus healthcare costs, as well as loss of lives.

The role of asymmetric dimethylarginine (ADMA) in COVID-19: association with respiratory failure and predictive role for outcome.

We aimed to assess the potential role of Asymmetric dimethylarginine (ADMA) in conditioning respiratory function and pulmonary vasoregulation during Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) infection. Within 72 h from admission, samples from 90 COVID-19 patients were assessed for ADMA, SDMA, L-arginine concentrations. In addition to classical statistics, patients were also clustered by a machine learning approach according to similar features. Multivariable analysis showed that C-reactive protein (OR 1.012), serum ADMA (OR 4.652), white blood cells (OR = 1.118) and SOFA (OR = 1.495) were significantly associated with negative outcomes. Machine learning-based clustering showed three distinct clusters: (1) patients with low severity not requiring invasive mechanical ventilation (IMV), (2) patients with moderate severity and respiratory failure whilst not requiring IMV, and (3) patients with highest severity requiring IMV. Serum ADMA concentration was significantly associated with disease severity and need for IMV although less pulmonary vasodilation was observed by CT scan. High serum levels of ADMA are indicative of high disease severity and requirement of mechanical ventilation. Serum ADMA at the time of hospital admission may therefore help to identify COVID-19 patients at high risk of deterioration and negative outcome.

Barotrauma during Noninvasive Respiratory Support in COVID-19 Pneumonia Outside ICU: The Ancillary COVIMIX-2 Study.

BACKGROUND: Noninvasive respiratory support (NIRS) has been extensively used during the COVID-19 surge for patients with acute respiratory failure. However, little data are available about barotrauma during NIRS in patients treated outside the intensive care unit (ICU) setting. METHODS: COVIMIX-2 was an ancillary analysis of the previous COVIMIX study, a large multicenter observational work investigating the frequencies of barotrauma (i.e., pneumothorax and pneumomediastinum) in adult patients with COVID-19 interstitial pneumonia. Only patients treated with NIRS outside the ICU were considered. Baseline characteristics, clinical and radiological disease severity, type of ventilatory support used, blood tests and mortality were recorded. RESULTS: In all, 179 patients were included, 60 of them with barotrauma. They were older and had lower BMI than controls (p < 0.001 and p = 0.045, respectively). Cases had higher respiratory rates and lower PaO2/FiO2 (p = 0.009 and p < 0.001). The frequency of barotrauma was 0.3% [0.1-1.3%], with older age being a risk factor for barotrauma (OR 1.06, p = 0.015). Alveolar-arterial gradient (A-a) DO2 was protective against barotrauma (OR 0.92 [0.87-0.99], p = 0.026). Barotrauma required active treatment, with drainage, in only a minority of cases. The type of NIRS was not explicitly related to the development of barotrauma. Still, an escalation of respiratory support from conventional oxygen therapy, high flow nasal cannula to noninvasive respiratory mask was predictive for in-hospital death (OR 15.51, p = 0.001). CONCLUSIONS: COVIMIX-2 showed a low frequency for barotrauma, around 0.3%. The type of NIRS used seems not to increase this risk. Patients with barotrauma were older, with more severe systemic disease, and showed increased mortality.

Potential role of IgM-enriched immunoglobulin as adjuvant treatment in severe SARS-CoV-2 infection.

BACKGROUND: Severe COVID-19 patients are characterized by a dysregulated host response to an infection, with uncontrolled pro- and anti- inflammatory pathway activation. Consistent proportion of patients require admission in intensive care units and are at risk of progression to severe forms of disease. These patients are generally admitted during later stages of the disease, when effective antiviral and monoclonal antibody are not indicated. We aimed to assess the potential role of IgM-enriched intra venous immunoglobulins (IGAM) preparations in this setting. METHODS: This retrospective, observational case-controlled study was conducted at a single-center University Hospital of Udine in the Friuli Venezia Giulia Region of Italy. Patients referring to the center between March 2020 and April 2021 was included. During the study period, patient who received Pentaglobin® IGAM treatment (N.=56), administered as compassionate use, was compared with a control group (N.=169) to assess, by propensity score analysis, clinical outcome. RESULTS: Untreated controls required, respect to patient treated with IGAM therapy, longer time to hospitalization with no significant differences in death and orotracheal intubation requirement. Significant differences in the two cohort were in: SOFA was higher in treated, while D-dimer and P/F ratio was better in the treatment cohort. Multivariate logistic regression analysis performed on the "matched sample," obtained by a weighting propensity score approach, identify, as significant protective factor for death outcome, the Pentaglobin® treatment (0.820 [0.698-0.963], P=0.016) and low C-reactive protein (1.001 [1.000-1.002], P=0.031) value while the delay of onset hospitalization is associate with a worst outcome (0.983 [0.967-0.999], P=0.041). CONCLUSIONS: The present study offers a significant insight concerning the use of IgM-enriched immunoglobulin preparations in patients with SARS-CoV-2 severe infection and also could identifying the specific immunological and biochemical profile of the patient who can more benefit from this therapeutic option.

Ischemic strokes in COVID-19: risk factors, obesity paradox, and distinction between trigger and causal association.

Background and purpose: Stroke has been described as a COVID-19 complication. However, its occurrence rate, risk factors, and causal relationships are still not well established. Methods: We describe the characteristics of confirmed COVID-19-related strokes among all cases of COVID-19 hospitalized in our health network, from November 1, 2020 to April 30, 2021. Risk factor analysis has been conducted for ischemic stroke (IS), which represents 92% of all confirmed cases of Covid-19-related strokes, and a "causal attribution to infection" classification is provided. Results: In all, 62/4105 hospitalized COVID-19 patients had an acute stroke (1.51%). Severe COVID-19 (OR 2.27-CI 1.06-4.77; p = 0.032), atrial fibrillation (OR 3.65-CI 1.63-7.98; p = 0.001), and ischemic heart disease (OR 4.590-CI 1.714-12.137; p = 0.002) proved to be independent risk factors for IS, while obesity was a protective factor (OR 0.90-CI 0.82-0.97; p = 0.012). COVID-19 had a causal role in 32.1% of IS cases, was a relevant cofactor in 28.6% of cases of IS, and was a possible trigger in 39.3% of events. Conclusion: Our stroke occurrence rate is consistent with other population-based reports (range 0.34-2.7%). Prespecified peculiar clinical and radiological features allow the distinction between "IS caused by COVID-19" and "IS triggered by COVID-19." Clinical history of vascular diseases and risk factors is crucial in determining the risk of IS in patients with COVID-19. However, the protective effect of a BMI > 30 kg/m2 seems to suggest an obesity paradox.

Ventilatory associated barotrauma in COVID-19 patients: A multicenter observational case control study (COVI-MIX-study).

BACKGROUND: The risk of barotrauma associated with different types of ventilatory support is unclear in COVID-19 patients. The primary aim of this study was to evaluate the effect of the different respiratory support strategies on barotrauma occurrence; we also sought to determine the frequency of barotrauma and the clinical characteristics of the patients who experienced this complication. METHODS: This multicentre retrospective case-control study from 1 March 2020 to 28 February 2021 included COVID-19 patients who experienced barotrauma during hospital stay. They were matched with controls in a 1:1 ratio for the same admission period in the same ward of treatment. Univariable and multivariable logistic regression (OR) were performed to explore which factors were associated with barotrauma and in-hospital death. RESULTS: We included 200 cases and 200 controls. Invasive mechanical ventilation was used in 39.3% of patients in the barotrauma group, and in 20.1% of controls (p<0.001). Receiving non-invasive ventilation (C-PAP/PSV) instead of conventional oxygen therapy (COT) increased the risk of barotrauma (OR 5.04, 95% CI 2.30 - 11.08, p<0.001), similarly for invasive mechanical ventilation (OR 6.24, 95% CI 2.86-13.60, p<0.001). High Flow Nasal Oxygen (HFNO), compared with COT, did not significantly increase the risk of barotrauma. Barotrauma frequency occurred in 1.00% [95% CI 0.88-1.16] of patients; these were older (p=0.022) and more frequently immunosuppressed (p=0.013). Barotrauma was shown to be an independent risk for death (OR 5.32, 95% CI 2.82-10.03, p<0.001). CONCLUSIONS: C-PAP/PSV compared with COT or HFNO increased the risk of barotrauma; otherwise HFNO did not. Barotrauma was recorded in 1.00% of patients, affecting mainly patients with more severe COVID-19 disease. Barotrauma was independently associated with mortality. TRIAL REGISTRATION: this case-control study was prospectively registered in clinicaltrial.gov as NCT04897152 (on 21 May 2021).