Diaphragm dysfunction after severe COVID-19: An ultrasound study.

Background: SARS-CoV-2 infection can impair diaphragm function at the acute phase but the frequency of diaphragm dysfunction after recovery from COVID-19 remains unknown. Materials and methods: This study was carried out on patients reporting persistent respiratory symptoms 3-4 months after severe COVID-19 pneumonia. The included patients were selected from a medical consultation designed to screen for recovery after acute infection. Respiratory function was assessed by a pulmonary function test, and diaphragm function was studied by ultrasonography. Results: In total, 132 patients (85M, 47W) were recruited from the medical consultation. During the acute phase of the infection, the severity of the clinical status led to ICU admission for 58 patients (44%). Diaphragm dysfunction (DD) was detected by ultrasonography in 13 patients, two of whom suffered from hemidiaphragm paralysis. Patients with DD had more frequently muscle pain complaints and had a higher frequency of prior cardiothoracic or upper abdominal surgery than patients with normal diaphragm function. Pulmonary function testing revealed a significant decrease in lung volumes and DLCO and the dyspnea scores (mMRC and Borg10 scores) were significantly increased in patients with DD. Improvement in respiratory function was recorded in seven out of nine patients assessed 6 months after the first ultrasound examination. Conclusion: Assessment of diaphragm function by ultrasonography after severe COVID-19 pneumonia revealed signs of dysfunction in 10% of our population. In some cases, ultrasound examination probably discovered an un-recognized pre-existing DD. COVID-19 nonetheless contributed to impairment of diaphragm function. Prolonged respiratory physiotherapy led to improvement in respiratory function in most patients. Clinical trial registration: [www.cnil.fr], identifier [#PADS20-207].

COVID-19 Lung Damage in a Preterm Newborn

The new coronavirus disease 2019 (COVID-19) was declared responsible for a global pandemic by the World Health Organization (WHO) on March 11, 2020. It created, since its emergence, a special concern in neonates given their immature immune system and their frequent contact with caregivers. Monochorionic very preterm female neonate (31(5/7) weeks of gestation) with growth retardation had at 3 weeks of age severe lung damage following an acute respiratory failure caused by horizontally transmitted severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. Before this acute episode, she was without respiratory support apart from 7 days of positive airway pressure following a respiratory distress syndrome. Both of her parents, her twin sister, and her referent nurse tested positive for SARS-CoV-2. Chest radiographs showed bilateral alveolar infiltration. Chest computed tomography (CT) scan detected multiple ground-glass opacifications in both lungs along with ventilation disorders and bronchial wall thickening. Neonate paraclinical tests showed a negative C-reactive protein and hypogammaglobulinemia. Mechanical ventilation was needed for 1 week along with sedation, pharmaceutically induced paralysis, and inhaled nitric oxide. Severe bronchopulmonary dysplasia revealed by oxygen dependency at 36 weeks of gestation was treated with steroids and noninvasive respiratory support. The neonate was discharged with free oxygen at home. Her twin sister, also SARS-CoV-2-positive, only needed noninvasive ventilation for 19 days. Severe forms of COVID-19 in premature neonates, with long-term respiratory sequelae, are possible. Considering the lack of data in this age group, more studies are needed to investigate the risk factors associated with severe symptoms, the effects of specific treatments, and the long-term prognosis of the disease.

Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT

Objectives 1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification. Methods This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization>10 days, intensive care unit hospitalization or oxygen therapy. Results The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08;0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90;p<0.0001). Conclusions A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.

Early combination therapy with hydroxychloroquine and azithromycin reduces mortality in 10,429 COVID-19 outpatients.

We evaluated the age-specific mortality of unselected adult outpatients infected with SARS-CoV-2 treated early in a dedicated COVID-19 day hospital and we assessed whether the use of hydroxychloroquine (HCQ) + azithromycin (AZ) was associated with improved survival in this cohort. A retrospective monocentric cohort study was conducted in the day hospital of our center from March to December 2020 in adults with PCR-proven infection who were treated as outpatients with a standardized protocol. The primary endpoint was 6-week mortality, and secondary endpoints were transfer to the intensive care unit and hospitalization rate. Among 10,429 patients (median age, 45 [IQR 32-57] years; 5597 [53.7%] women), 16 died (0.15%). The infection fatality rate was 0.06% among the 8315 patients treated with HCQ+AZ. No deaths occurred among the 8414 patients younger than 60 years. Older age and male sex were associated with a higher risk of death, ICU transfer, and hospitalization. Treatment with HCQ+AZ (0.17 [0.06-0.48]) was associated with a lower risk of death, independently of age, sex and epidemic period. Meta-analysis evidenced consistency with 4 previous outpatient studies (32,124 patients-Odds ratio 0.31 [0.20-0.47], I2 = 0%). Early ambulatory treatment of COVID-19 with HCQ+AZ as a standard of care is associated with very low mortality, and HCQ+AZ improve COVID-19 survival compared to other regimens.

Low Dose Chest CT and Lung Ultrasound for the Diagnosis and Management of COVID-19.

BACKGROUND: The COVID-19 pandemic has provided an opportunity to use low- and non-radiating chest imaging techniques on a large scale in the context of an infectious disease, which has never been done before. Previously, low-dose techniques were rarely used for infectious diseases, despite the recognised danger of ionising radiation. METHOD: To evaluate the role of low-dose computed tomography (LDCT) and lung ultrasound (LUS) in managing COVID-19 pneumonia, we performed a review of the literature including our cases. RESULTS: Chest LDCT is now performed routinely when diagnosing and assessing the severity of COVID-19, allowing patients to be rapidly triaged. The extent of lung involvement assessed by LDCT is accurate in terms of predicting poor clinical outcomes in COVID-19-infected patients. Infectious disease specialists are less familiar with LUS, but this technique is also of great interest for a rapid diagnosis of patients with COVID-19 and is effective at assessing patient prognosis. CONCLUSIONS: COVID-19 is currently accelerating the transition to low-dose and "no-dose" imaging techniques to explore infectious pneumonia and their long-term consequences.

Prevalence and risk factors for lung involvement on low-dose chest CT (LDCT) in a paucisymptomatic population of 247 patients affected by COVID-19.

BACKGROUND: Low-dose chest CT (LDCT) showed high sensitivity and ability to quantify lung involvement of COVID-19 pneumopathy. The aim of this study was to describe the prevalence and risk factors for lung involvement in 247 patients with a visual score and assess the prevalence of incidental findings. METHODS: For 12 days in March 2020, 250 patients with RT-PCR positive tests and who underwent LDCT were prospectively included. Clinical and imaging findings were recorded. The extent of lung involvement was quantified using a score ranging from 0 to 40. A logistic regression model was used to explore factors associated with a score ≥ 10. RESULTS: A total of 247 patients were analyzed; 138 (54%) showed lung involvement. The mean score was 4.5 ± 6.5, and the mean score for patients with lung involvement was 8.1 ± 6.8 [1-31]. The mean age was 43 ± 15 years, with 121 males (48%) and 17 asymptomatic patients (7%). Multivariate analysis showed that age > 54 years (odds ratio 4.4[2.0-9.6] p < 0.001) and diabetes (4.7[1.0-22.1] p = 0.049) were risk factors for a score ≥ 10. Multivariate analysis including symptoms showed that only age > 54 years (4.1[1.7-10.0] p = 0.002) was a risk factor for a score ≥ 10. Rhinitis (0.3[0.1-0.7] p = 0.005) and anosmia (0.3[0.1-0.9] p = 0.043) were protective against lung involvement. Incidental imaging findings were found in 19% of patients, with a need for follow-up in 0.6%. CONCLUSION: The prevalence of lung involvement was 54% in a predominantly paucisymptomatic population. Age ≥ 55 years and diabetes were risk factors for significant parenchymal lung involvement. Rhinitis and anosmia were protective against LDCT abnormalities.

Low-dose chest CT for diagnosing and assessing the extent of lung involvement of SARS-CoV-2 pneumonia using a semi quantitative score.

OBJECTIVES: The purpose is to assess the ability of low-dose CT (LDCT) to determine lung involvement in SARS-CoV-2 pneumonia and to describe a COVID19-LDCT severity score. MATERIALS AND METHODS: Patients with SARS-CoV-2 infection confirmed by RT-PCR were retrospectively analysed. Clinical data, the National Early Warning Score (NEWS) and imaging features were recorded. Lung features included ground-glass opacities (GGO), areas of consolidation and crazy paving patterns. The COVID19-LDCT score was calculated by summing the score of each segment from 0 (no involvement) to 10 (severe impairment). Univariate analysis was performed to explore predictive factor of high COVID19-LDCT score. The nonparametric Mann-Whitney test was used to compare groups and a Spearman correlation used with p<0.05 for significance. RESULTS: Eighty patients with positive RT-PCR were analysed. The mean age was 55 years ± 16, with 42 males (53%). The most frequent symptoms were fever (60/80, 75%) and cough (59/80, 74%), the mean NEWS was 1.7±2.3. All LDCT could be analysed and 23/80 (28%) were normal. The major imaging finding was GGOs in 56 cases (67%). The COVID19-LDCT score (mean value = 19±29) was correlated with NEWS (r = 0.48, p<0.0001). No symptoms were risk factor to have pulmonary involvement. Univariate analysis shown that dyspnea, high respiratory rate, hypertension and diabetes are associated to a COVID19-LDCT score superior to 50. CONCLUSIONS: COVID19-LDCT score did correlate with NEWS. It was significantly different in the clinical low-risk and high-risk groups. Further work is needed to validate the COVID19-LDCT score against patient prognosis.