Lung recruitment and hyperinflation with higher positive end-expiratory pressure in COVID-19: a computed tomography study (preprint)

Background A higher (> 10 cmH2O) positive end-expiratory pressure (PEEP) is commonly used in patients with moderate to severe hypoxemia due to the novel coronavirus disease (COVID-19). However, compliance more commonly decreases when PEEP is increased from 10 to 15 cmH2O, as for lung hyperinflation. In this study, we directly measured lung recruitment and hyperinflation induced by increasing PEEP from 10 to 15 cmH2O in mechanically ventilated patients with COVID-19.Methods Twenty mechanically ventilated patients with COVID-19 underwent a lung computed tomography (CT) at 10 and 15 cmH2O of airway pressure. Gas exchange and compliance were then measured with 10 and 15 cmH2O of PEEP. Recruitment was computed as the decrease of the non-aerated lung volume (density above − 100 HU) and hyperinflation as the increase of the over-aerated lung volume (density below − 900 HU). If recruitment was larger than hyperinflation, the net morphological response was “recruitment”; otherwise, it was “hyperinflation”.Results With 10 cmH2O of PEEP, the median (Q1-Q3) arterial tension to the inspiratory fraction of oxygen (PaO2:FiO2) was 146 (107–197) mmHg. The net morphological response to increasing PEEP was recruitment in nine (45%) patients and hyperinflation in eleven (55%). Oxygenation improved in twelve (60%) patients, but compliance in only three (15%). Compliance with 10 cmH2O of PEEP ≤ 45 cmH2O/ml predicted net recruitment in response to increasing PEEP with a positive predictive value of 0.89 (95%-confidence intervals: 0.49-1.00) and a negative predictive value of 0.91 (0.59-1.00).Conclusions The morphological response to a higher PEEP is largely variable in patients with COVID-19, ranging from net recruitment (in half of the patients) to net hyperinflation (in the other half). Baseline compliance may help to predict the individual response to increasing PEEP.

Lung Response to Prone Positioning in Mechanically-ventilated Patients With COVID-19 (preprint)

Background: Prone positioning improves survival in moderate-to-severe acute respiratory distress syndrome (ARDS) unrelated to the novel coronavirus disease (COVID-2019). This benefit is probably mediated by a decrease in alveolar collapse and hyperinflation and a more homogeneous distribution of lung aeration, with fewer harms of mechanical ventilation. Herein we aimed to verify whether prone positioning causes analogue changes in lung aeration in COVID-2019. A positive result would support prone positioning even in this other population. MethodsFifteen mechanically-ventilated patients with COVID-19 underwent a lung computed tomography in the supine and prone position within three days of endotracheal intubation and with a constant positive end-expiratory pressure (PEEP). Using quantitative analysis, we measured the volume of the non-aerated, poorly-aerated, well-aerated, and over-aerated compartments and the gas-to-tissue ratio of the ten vertical levels of the lung. In addition, we expressed the heterogeneity of lung aeration with the standardized median absolute deviation of the ten vertical gas-to-tissue ratios, with lower values indicating less heterogeneity. ResultsBy the time of the study, PEEP was 12 (10-14) cmH 2 O and the PaO 2 FiO 2 107 (84-173) mmHg in the supine position. With prone positioning, the volume of the non-aerated compartment decreased by 82 (26-147) ml, of the poorly-aerated compartment increased by 82 (53-174) ml, of the normally-aerated compartment did not significantly change, and of the over-aerated compartment decreased by 28 (11-186) ml. In eight (53%) patients, the volume of the over-aerated compartment decreased more than the volume of the non-aerated compartment. The gas-to-tissue ratio of the ten vertical levels of the lung decreased by 0.34 (0.25-0.49) ml/g per level in the supine position and by 0.03 (-0.11-0.14) ml/g in the prone position (p<0.001). The standardized median absolute deviation of the gas-to-tissue ratios of those ten levels decreased in all patients, from 0.55 (0.50-0.71) to 0.20 (0.14-0.27) (p<0.001).ConclusionsIn fifteen patients with COVID-19, prone positioning decreased alveolar collapse and hyperinflation, and homogenized lung aeration. A similar response has been observed in other ARDS, where prone positioning improves outcome. Therefore, our data provide a pathophysiological rationale to support prone positioning even in COVID-19.

A PTX3/LDH/CRP signature correlates with lung injury CTs scan severity and disease progression in paucisymptomatic COVID-19 (preprint)

BackgroundQuantitative CT (QCT) analysis is an invaluable diagnostic tool to assess lung injury and predict prognosis of patients affected by COVID-19 pneumonia. PTX3 was recently described as one of the most reliable serological predictors of clinical deterioration and short-term mortality. The present study was designed to evaluate a correlation between serological biomarkers of inflammation and lung injury measured by QCT. MethodsThis retrospective monocentric study analysed a cohort of patients diagnosed with COVID-19 and admitted because of respiratory failure, or significant radiological involvement on chest CT scan. All patients, males and non-pregnant females older than 18 years, underwent chest CT scan and laboratory testing at admission. Exclusion criteria were defined by concurrent acute pathological processes and ongoing specific treatments which could interfere with immune activity. The cohort was stratified based on severity in mild and severe forms. Compromised lung at QCT was then correlated to serological biomarkers representative of SARS-CoV-2. We further developed a multivariable logistic model to predict CT data and clinical deterioration based on a specific molecular signature. Internal cross-validation led to evaluate discrimination, calibration, and clinical utility of the tool that was provided by a score to simplify its application. Findings592 patients were recruited between March 19th and December 1st, 2020. Applying exclusion criteria which consider confounders, the cohort resulted in 366 individuals characterized by 177 mild and 189 severe forms. In our predictive model, blood levels of PTX3, CRP and LDH were found to correlate with QCT values in mild COVID-19 disease. A signature of these three biomarkers had a high predictive accuracy in detecting compromised lungs as assessed by QCT. The score was elaborated and resulted representative of lung CT damage leading to clinical deterioration and oxygen need in mild disease. InterpretationThe LDH, PTX3, CRP blood signature can serve as a strong correlate of compromised lung in COVID-19, possibly integrating cellular damage, systemic inflammation, myeloid and endothelial cell activation. FundingThis work was supported by a philanthropic donation by Dolce & Gabbana fashion house (to A.M., C.G.) and by a grant from Italian Ministry of Health for COVID-19 (to A.M. and C.G.). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSBesides nasopharyngeal swab and serological test, chest CT scan represents one of the most useful tools to confirm COVID-19 diagnosis; moreover, QCT has been demonstrated to foresee oxygen need as well as deterioration of health status. Several clinical and serological parameters have been studied alone or combined in scores to be applied as prognostic tools of SARS-CoV-2 pneumonia; however, no one has yet reached the everyday practice. Recently, our group has investigated the expression and clinical significance of PTX3 in COVID-19 demonstrating the correlation with short-term mortality independently of confounders. The result was confirmed by other studies in different settings increasing evidence of PTX3 as a strong biomarker of severity; noteworthy, a recent report analysed proteomic data with a machine learning approach identifying age with PTX3 or SARS-CoV-2 RNAemia as the best binary signatures associated to 28-days mortality. Added value of this studyThe present study was designed to investigate associations between markers of damage and the CT extension of SARS-CoV-2 pneumonia in order to provide a biological footprint of radiological results in paucisymptomatic patients. QCT data were considered in a binary form identifying a threshold relevant for clinical deterioration, as already proved by literature. Our findings demonstrate a significant correlation with three peripheral blood proteins (PTX3, LDH and CRP) which result representative of COVID-19 severity. The study presents a predictive model of radiological lung involvement which performs with a high level of accuracy (cvAUC of 0{middle dot}794{+/-}0{middle dot}107; CI 95%: 0{middle dot}74-0{middle dot}87) and a simple score was provided to simplify the interpretation of the three biomarkers. Besides additional finding on PTX3 role in SARS-CoV2 pathology, its prognostic value was confirmed by data on clinical deterioration; indeed, paucisymptomatic subjects showed a 11{middle dot}9% deaths. The model offers the possibility to quickly assess patients resulted positive for SARS-CoV-2 and estimate people at risk of deterioration despite normal clinical and blood gases analysis, with potential to identify those who need better clinical monitoring and interventions. Implications of all the available evidencePredicting the extension, severity, and clinical deterioration in COVID-19 patients its pivotal to allocate enough resources in emergency and to avoid health system burden. Despite the urgent clinical need of biomarkers, SARS-CoV-2 pneumonia still lacks something able to provide an easy measure of its severity. Some multiparametric scores have been proposed for severe COVID-19 and rely on deep assessment of patients status (clinical, serological, and radiological data). Our model represents an unprecedented effort to provide a tool which could predict CT pneumonia extension, oxygen requirement and clinical deterioration in mild COVID-19. Based on the measurement of three proteins on peripheral blood, this score could improve early assessment of asymptomatic patients tested positive by SARS-CoV2 specifically in first level hospitals as well in developing countries.

New susceptibility loci for severe COVID-19 by detailed GWAS analysis in European populations (preprint)

Due to the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), deepening the host genetic contribution to severe COVID-19 may further improve our understanding about underlying disease mechanisms. Here, we describe an extended GWAS meta-analysis of 3,260 COVID-19 patients with respiratory failure and 12,483 population controls from Italy, Spain, Norway and Germany, as well as hypothesis-driven targeted analysis of the human leukocyte antigen (HLA) region and chromosome Y haplotypes. We include detailed stratified analyses based on age, sex and disease severity. In addition to already established risk loci, our data identify and replicate two genome-wide significant loci at 17q21.31 and 19q13.33 associated with severe COVID-19 with respiratory failure. These associations implicate a highly pleiotropic ~0.9-Mb 17q21.31 inversion polymorphism, which affects lung function and immune and blood cell counts, and the NAPSA gene, involved in lung surfactant protein production, in COVID-19 pathogenesis.

Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak (preprint)

Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subsequently spread globally. COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making, considering that baseline comorbidities, age, and patient conditions at admission have been associated to poorer outcomes. Supervised machine learning techniques are increasingly diffuse in clinical medicine and can predict mortality and test associations reaching high predictive performance. We assessed performances of a machine learning approach to predict mortality in COVID-19 patients admitted to ICU using data from the Lombardy ICU Network.Methods: this is a secondary analysis of prospectively collected data from Lombardy ICU network. To predict survival at 7-,14- and 28 days we built two different models; model A included patient demographics, medications before admission and comorbidities, while model B also included the data of the first day since ICU admission. 10-fold cross validation was repeated 2500 times, to ensure optimal hyperparameter choice. The only constrain imposed to model optimization was the choice of logistic regression as final layer to increase clinical interpretability. Different imputation and over-sampling techniques were employed in model training.Results 1503 patients were included, with 766 deaths (51%). Exploratory analysis and Kaplan-Meier curves demonstrated mortality association with age and gender. Model A and B reached the greatest predictive performance at 28 days (AUC 0.77 and 0.79), with lower performance at 14 days (AUC 0.72 and 0.74) and 7 days (AUC 0.68 and 0.71). Male gender, age and number of comorbidities were strongly associated with mortality in both models. Among comorbidities, chronic kidney disease and chronic obstructive pulmonary disease demonstrated association. Mode of ventilatory assistance at ICU admission and Fraction of Inspired oxygen were associated with mortality in model B.Conclusions Supervised machine learning models demonstrated good performance in prediction of 28-day mortality. 7-days and 14-days predictions demonstrated lower performance. Machine learning techniques may be useful in emergency phases to reach higher predictive performance with reduced human supervision using complex data.

Prone Position in Intubated, Mechanically Ventilated Patients with Coronavirus Disease-19. (preprint)

Background: Limited data are available on the use of prone position in intubated, invasively ventilated patients with Coronavirus disease-19 (COVID-19). Aim of this study is to investigate the use and effect of prone position in this population during the first 2020 pandemic wave.Methods: Retrospective, multicentre, national cohort study conducted between February 24 and June 14, 2020 in 24 Italian Intensive Care Units (ICU) on adult patients needing invasive mechanical ventilation for respiratory failure caused by COVID-19.Clinical data were collected on the day of ICU admission. Information regarding the use of prone position were collected daily. Follow-up for patient outcomes was performed on July 15, 2020. The respiratory effects of the first prone position were studied in a subset of 78 patients. Patients were classified as Responders if the PaO2/FiO2 ratio increased ≥ 20 mmHg during prone position. Results:  Of 1057 included patients, mild, moderate and severe ARDS was present in 15, 50 and 35% of patients, respectively and had a resulting mortality of 25, 33 and 41%. Prone position was applied in 61% of the patients. Patients placed prone had a more severe disease and died significantly more (45% vs 33%, p<0.001). Overall, prone position induced a significant increase in PaO2/FiO2 ratio, while no change in respiratory system compliance was observed. Seventy-eight % of patients were Responders to prone position. Non-Responders had a more severe respiratory failure and died more often in the ICU (65% vs. 38%, p=0.047).Conclusions: During the COVID-19 pandemic, prone position has been widely adopted to treat mechanically ventilated patients with respiratory failure. The majority of patients improved their oxygenation during prone position, most likely due to a better ventilation perfusion matching.Trial registration: clinicaltrials.gov  number: NCT04388670

Macropahge expression and prognostic significance of the long pentraxin PTX3 in COVID-19 (preprint)

PTX3 is an essential component of humoral innate immunity, involved in resistance to selected pathogens and in the regulation of inflammation. PTX3 plasma levels are associated with poor outcome in systemic inflammatory conditions and vascular pathology. The present study was designed to assess expression and significance of PTX3 in COVID-19. By bioinformatics analysis of public databases PTX3 expression was detected in lung respiratory cell lines exposed to SARS-CoV-2. By analysis at single cell level of COVID-19 circulating mononuclear cells, we found that PTX3 was selectively expressed by monocytes among circulating leukocytes. Moreover, in lung bronchoalveolar lavage fluid, single cell analysis revealed selective expression of PTX3 in neutrophils and macrophages, which play a major role in the pathogenesis of the disease. By immunohistochemistry, PTX3 was expressed by lung myelomocytic cells, type 2 pneumocytes and vascular endothelial cells. PTX3 plasma levels were determined by ELISA in 96 consecutive patients with a laboratory-confirmed diagnosis of COVID-19. Higher PTX3 plasma levels were observed in 52 (54.2%) patients admitted in ICU (median 21.0ng/mL, IQT 15.5-46.3 vs 12.4ng/mL IQT 6.1-20.2 in ward patients; p=0.0017) and in 22 (23%) patients died by 28 days (39.8ng/mL, IQT 20.2-75.7 vs 15.7ng/mL, IQT 8.2-21.6 in survivors; p=0.0001). After determining an optimal PTX3 cut-off for the primary outcome, the Kaplan-Meier curve showed an increased mortality in patients with PTX3>22.25ng/mL (Log-rank tests p<0.0001). In Cox regression model, PTX3>22.25ng/mL showed an adjusted Hazard Ratio (aHR) of 7.6 (95%CI2.45-23.76) in predicting mortality. Performing a multivariate logistic regression including all inflammatory markers (PTX3, ferritin, D-Dimer, IL-6, and CRP), PTX3 was the only marker significantly associated with death (aHR 1.13;95%CI1.02-1.24; p=0.021). The results reported here suggest that circulating and lung myelomonocytic cells are a major source of PTX3 and that PTX3 plasma levels can serve as a strong prognostic indicator of short-term mortality in COVID-19.

The ABO blood group locus and a chromosome 3 gene cluster associate with SARS-CoV-2 respiratory failure in an Italian-Spanish genome-wide association analysis (preprint)

Background. Respiratory failure is a key feature of severe Covid-19 and a critical driver of mortality, but for reasons poorly defined affects less than 10% of SARS-CoV-2 infected patients. Methods. We included 1,980 patients with Covid-19 respiratory failure at seven centers in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe (Milan, Monza, Madrid, San Sebastian and Barcelona) for a genome-wide association analysis. After quality control and exclusion of population outliers, 835 patients and 1,255 population-derived controls from Italy, and 775 patients and 950 controls from Spain were included in the final analysis. In total we analyzed 8,582,968 single-nucleotide polymorphisms (SNPs) and conducted a meta-analysis of both case-control panels. Results. We detected cross-replicating associations with rs11385942 at chromosome 3p21.31 and rs657152 at 9q34, which were genome-wide significant (P<5x10-8) in the meta-analysis of both study panels, odds ratio [OR], 1.77; 95% confidence interval [CI], 1.48 to 2.11; P=1.14x10-10 and OR 1.32 (95% CI, 1.20 to 1.47; P=4.95x10-8), respectively. Among six genes at 3p21.31, SLC6A20 encodes a known interaction partner with angiotensin converting enzyme 2 (ACE2). The association signal at 9q34 was located at the ABO blood group locus and a blood-group-specific analysis showed higher risk for A-positive individuals (OR=1.45, 95% CI, 1.20 to 1.75, P=1.48x10-4) and a protective effect for blood group O (OR=0.65, 95% CI, 0.53 to 0.79, P=1.06x10-5). Conclusions. We herein report the first robust genetic susceptibility loci for the development of respiratory failure in Covid-19. Identified variants may help guide targeted exploration of severe Covid-19 pathophysiology.