Long-term physical impairments in survivors of COVID-19-associated ARDS compared to classic ARDS. A Two-Center Study (preprint)

We carried out a prospective and retrospective case series study to compare physical outcome performance with an in-person evaluation of 248 COVID-related ARDS (CARDS) patients and 48 classic ARDS patients. At 6 months, patients with classic ARDS compared to CARDS had lower MRCss, handgrip dynamometry, and 6 Minutes Walk Test. Fatigue was more frequently reported by patients with classic ARDS. At 12 months, patients in both groups partially regained physical performances, and the differences in measured variables between classic ARDS and CARDS remained constant over time. Reasons for these differences are likely multifactorial and require further investigations.

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.

Six-month pulmonary impairment after severe COVID-19: a prospective, multicenter follow-up study (preprint)

Background and objective. Long-term pulmonary sequelae following SARS-CoV-2 pneumonia are not yet confirmed, however preliminary observations suggests a possible relevant clinical, functional and radiological impairment. The aim of this study was to identify and characterise pulmonary sequelae caused by SARS-CoV-2 pneumonia at 6-month follow-up. Methods. In this multicenter, prospective, observational cohort study, patients hospitalised for SARS-CoV-2 pneumonia and without prior diagnosis of structural lung diseases were stratified by maximum ventilatory support (oxygen only, continuous positive airway pressure (CPAP) and invasive mechanical ventilation (IMV)) and followed up at 6 months from discharge. Pulmonary function tests and diffusion capacity for carbon monoxide (DLCO), 6 minutes walking test, chest X-ray, physical exam and modified Medical Research Council (mMRC) dyspnoea score were collected. Results. Between March and June 2020, 312 patients were enrolled (83, 27% women; median [IQR] age 61.1 [53.4,69.3] years). The parameters that showed the highest rate of impairment were DLCO and chest-X-ray, in 46% and 25% of patients, respectively. However, only a minority of patients reported dyspnoea (31%), defined as mMRC [≥] 1, or showed a restrictive ventilatory defects (9%). In the logistic regression model, having asthma as comorbidity was associated with DLCO impairment at follow-up, while prophylactic heparin administration during hospitalisation appeared as a protective factor. Need for invasive ventilatory support during hospitalisation was associated with chest imaging abnormalities. Conclusion. DLCO and radiological assessment appear to be the most sensitive tools to monitor patients with COVID-19 during follow-up. Future studies with longer follow-up are warranted to better understand pulmonary sequelae.

Six-month Pulmonary Impairment After Severe COVID-19: a Prospective, Multicenter Follow-up Study (preprint)

Background: Long-term pulmonary sequelae following SARS-CoV-2 pneumonia are not yet confirmed, however preliminary observations suggests a possible relevant clinical, functional and radiological impairment. The aim of this study was to identify and characterise pulmonary sequelae caused by SARS-CoV-2 pneumonia at 6-month follow-up. Methods: . In this multicenter, prospective, observational cohort study, patients hospitalised for SARS-CoV-2 pneumonia and without prior diagnosis of structural lung diseases were stratified by maximum ventilatory support (“oxygen only”, “continuous positive airway pressure (CPAP)” and “invasive mechanical ventilation (IMV)”) and followed up at 6 months from discharge. Pulmonary function tests and diffusion capacity for carbon monoxide (DLCO), 6 minutes walking test, chest X-ray, physical exam and modified Medical Research Council (mMRC) dyspnoea score were collected. Results: . Between March and June 2020, 312 patients were enrolled (83, 27% women; median [IQR] age 61.1 [53.4,69.3] years). The parameters that showed the highest rate of impairment were DLCO and chest-X-ray, in 46% and 25% of patients, respectively. However, only a minority of patients reported dyspnoea (31%), defined as mMRC ≥ 1, or showed a restrictive ventilatory defects (9%). In the logistic regression model, having asthma as comorbidity was associated with DLCO impairment at follow-up, while prophylactic heparin administration during hospitalisation appeared as a protective factor. Need for invasive ventilatory support during hospitalisation was associated with chest imaging abnormalities. Conclusions: . DLCO and radiological assessment appear to be the most sensitive tools to monitor patients with COVID-19 during follow-up. Future studies with longer follow-up are warranted to better understand pulmonary sequelae. ClinicalTrials.gov Identifier: NCT04435327

Helmet CPAP to treat hypoxic pneumonia outside the ICU: an observational study during the COVID-19 outbreak (preprint)

Background: Respiratory failure due to COVID-19 pneumonia is associated with high mortality and may overwhelm health care systems, due to the surge patients requiring advanced respiratory support. Shortage of intensive care unit (ICU) beds required many patients to be treated outside the ICU despite severe gas exchange impairment. Helmet is as effective interface to provide Continuous Positive Airway Pressure (CPAP) non-invasively. We report data about the usefulness of helmet CPAP during pandemic, either as an effective treatment, a bridge to intubation or a rescue therapy for patients with care limitations (DNI). Methods: In this observational study we collected data regarding patients failing standard oxygen therapy (i.e. non-rebreathing mask) due to COVID-19 pneumonia treated with a free flow helmet CPAP system. Patients’ data were recorded before, at initiation of CPAP treatment and once a day, thereafter. CPAP failure was defined as a composite outcome of intubation or death. Results: A total of 306 patients were included; 42% were deemed as DNI. Helmet CPAP treatment was successful in 69% of the full-treatment and 28% of the DNI patients ( P< 0.001). With helmet CPAP, PaO 2 /FiO 2 ratio doubled from about 100 to 200 mmHg ( P< 0.001); respiratory rate decreased from 28 [22-32] to 24 [20-29] breaths per minute, P <0.001). C-Reactive Protein, time to oxygen mask failure, age, PaO 2 /FiO 2 during CPAP, number of comorbidities were independently associated with CPAP failure. Helmet CPAP was maintained for 6 [3-9] days, almost continuously during the first two days. None of the full treatment patients died before intubation in the wards. Conclusions: : Helmet CPAP treatment is feasible for several days outside the ICU, despite persistent impairment in gas exchange. It was used, without escalating to intubation, in the majority of full treatment patients after standard oxygen therapy failed. DNI patients could benefit from helmet CPAP as rescue therapy to improve survival. Trial Registration: NCT04424992

CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network (preprint)

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of $0.495 \pm 0.309$ mm and Dice coefficient of $0.985 \pm 0.011$. Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.