Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study.

(1) Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the "first wave" of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51-69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1-4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae.

Compromised Lung Volume and Hemostatic Abnormalities in COVID-19 Pneumonia: Results from an Observational Study on 510 Consecutive Patients.

BACKGROUND: Hemostatic abnormalities have been described in COVID-19, and pulmonary microthrombosis was consistently found at autopsy with concomitant severe lung damage. METHODS: This is a retrospective observational cross-sectional study including consecutive patients with COVID-19 pneumonia who underwent unenhanced chest CT upon admittance at the emergency room (ER) in one large academic hospital. QCT was used for the calculation of compromised lung volume (%CL). Clinical data were retrieved from patients' files. Laboratory data were obtained upon presentation at the ER. AIM: The aim of this study was to evaluate the correlation between hemostatic abnormalities and lung involvement in patients affected by COVID-19 pneumonia as described using computer-aided quantitative evaluation of chest CT (quantitative CT (QCT)). RESULTS: A total of 510 consecutive patients (68% males), aged 67 years in median, diagnosed with COVID-19 pneumonia, who underwent unenhanced CT scan upon admission to the ER, were included. In all, 115 patients had %CL > 23%; compared to those with %CL < 23%, they showed higher levels of D-dimer, fibrinogen, and CRP, greater platelet count, and longer PT ratio. Via multivariate regression analysis, BMI ≥ 30 kg/m2, D-dimer levels > 500 ng/mL, CRP > 5.0 ng/mL and PT ratio > 1.2 were found to be independent predictors of a %CL > 23% (adjusted odds ratios (95% confidence intervals): 2.1 (1.1-4.0), 3.1 (1.6-5.8), 2.4 (1.3-4.5), and 3.4 (1.4-8.5), respectively). CONCLUSIONS: Hemostatic abnormalities in patients affected by COVID-19 correlate with the severity of lung injury as measured by %CL. Our results underline the pathogenetic role of hemostasis in COVID-19 pneumonia beyond the presence of clinically evident thromboembolic complications.

Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.

Sudden neck swelling with rash as late manifestation of COVID-19: a case report.

BACKGROUND: Although there are reports of otolaryngological symptoms and manifestations of CoronaVirus Disease 19 (COVID-19), there have been no documented cases of sudden neck swelling with rash in patients with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection described in literature. CASE PRESENTATION: We report a case of a sudden neck swelling and rash likely due to late SARS-CoV-2 in a 64-year-old woman. The patient reported COVID-19 symptoms over the previous three weeks. Computed Tomography (CT) revealed a diffuse soft-tissue swelling and edema of subcutaneous tissue, hypodermis, and muscular and deep fascial planes. All the differential diagnoses were ruled out. Both the anamnestic history of the patient's husband who had died of COVID-19 with and the collateral findings of pneumonia and esophageal wall edema suggested the association with COVID-19. This was confirmed by nasopharyngeal swab polymerase chain reaction. The patient was treated with lopinavir/ritonavir, hydroxychloroquine and piperacillin/tazobactam for 7 days. The neck swelling resolved in less than 24 h, while the erythema was still present up to two days later. The patient was discharged after seven days in good clinical condition and with a negative swab. CONCLUSION: Sudden neck swelling with rash may be a coincidental presentation, but, in the pandemic context, it is most likely a direct or indirect complication of COVID-19.

Stroke management during the COVID-19 outbreak: challenges and results of a hub-center in Lombardy, Italy.

PURPOSE: We describe the reorganization carried out during the COVID-19 outbreak at one of the stroke centers in Italy and report on the clinical features and procedural variables of stroke patients in need of endovascular treatment. METHODS: From 1 March to 10 May 2020, we retrospectively analyzed data from stroke patients in need of urgent neurointerventional treatment. Clinical presentation, demographics, and clinical history were collected along with procedural variables (door-to-needle time, needle-to-mTiCi time). Each patient underwent a nasal swab (polymerase chain reaction test), clinical screening, and chest CT scan to assess the risk of SARS-CoV-2 infection. Technical success, procedural safety (including staff SARS-CoV-2 infection), and clinical outcome at discharge were retrieved. A comparison was made with the same patient population treated between 1 March and 10 May 2019 to highlight possible differences in the characteristics or outcomes of the patients. RESULTS: One hundred thirty-six ischemic stroke patients were admitted to our facility from 1 March to 10 May 2020. Of these, 12 patients (9%) were classified as "high risk" for SARS-CoV-2 infection. Radiological suspicion of COVID-19 was confirmed in all cases by pharyngeal swab. Five SARS-CoV-2 patients (42%) needed endovascular therapy. None of the staff members tested positive for IgG against SARS-CoV-2. Compared to the same period in 2019, an increase in the mean interval from the first symptoms to hospital arrival was observed (p < 0.05). CONCLUSION: Endovascular treatment of stroke presented several challenges during the COVID-19 outbreak. Within the hospital, special pathways can be used to maintain both procedural safety and procedural times.

Risk factors for myocardial injury and death in patients with COVID-19: insights from a cohort study with chest computed tomography.

AIMS: Whether pulmonary artery (PA) dimension and coronary artery calcium (CAC) score, as assessed by chest computed tomography (CT), are associated with myocardial injury in patients with coronavirus disease 2019 (COVID-19) is not known. The aim of this study was to explore the risk factors for myocardial injury and death and to investigate whether myocardial injury has an independent association with all-cause mortality in patients with COVID-19. METHODS AND RESULTS: This is a single-centre cohort study including consecutive patients with laboratory-confirmed COVID-19 undergoing chest CT on admission. Myocardial injury was defined as high-sensitivity troponin I >20 ng/L on admission. A total of 332 patients with a median follow-up of 12 days were included. There were 68 (20.5%) deaths; 123 (37%) patients had myocardial injury. PA diameter was higher in patients with myocardial injury compared with patients without myocardial injury [29.0 (25th-75th percentile, 27-32) mm vs. 27.7 (25-30) mm, P < 0.001). PA diameter was independently associated with an increased risk of myocardial injury [adjusted odds ratio 1.10, 95% confidence interval (CI) 1.02-1.19, P = 0.01] and death [adjusted hazard ratio (HR) 1.09, 95% CI 1.02-1.17, P = 0.01]. Compared with patients without myocardial injury, patients with myocardial injury had a lower prevalence of a CAC score of zero (25% vs. 55%, P < 0.001); however, the CAC score did not emerge as a predictor of myocardial injury by multivariable logistic regression. Myocardial injury was independently associated with an increased risk of death by multivariable Cox regression (adjusted HR 2.25, 95% CI 1.27-3.96, P = 0.005). Older age, lower estimated glomerular filtration rate, and lower PaO2/FiO2 ratio on admission were other independent predictors for both myocardial injury and death. CONCLUSIONS: An increased PA diameter, as assessed by chest CT, is an independent risk factor for myocardial injury and mortality in patients with COVID-19. Myocardial injury is independently associated with an approximately two-fold increased risk of death.

Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation.

OBJECTIVE: Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19. METHODS: We performed a single-centre retrospective study on COVID-19 patients hospitalised from January 25, 2020, to April 28, 2020, who received CT at admission prompted by respiratory symptoms such as dyspnea or desaturation. QCT was performed using a semi-automated method (3D Slicer). Lungs were divided by Hounsfield unit intervals. Compromised lung (%CL) volume was the sum of poorly and non-aerated volumes (- 500, 100 HU). We collected patient's clinical data including oxygenation support throughout hospitalisation. RESULTS: Two hundred twenty-two patients (163 males, median age 66, IQR 54-6) were included; 75% received oxygenation support (20% intubation rate). Compromised lung volume was the most accurate outcome predictor (logistic regression, p < 0.001). %CL values in the 6-23% range increased risk of oxygenation support; values above 23% were at risk for intubation. %CL showed a negative correlation with PaO2/FiO2 ratio (p < 0.001) and was a risk factor for in-hospital mortality (p < 0.001). CONCLUSIONS: QCT provides new metrics of COVID-19. The compromised lung volume is accurate in predicting the need for oxygenation support and intubation and is a significant risk factor for in-hospital death. QCT may serve as a tool for the triaging process of COVID-19. KEY POINTS: • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the - 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6-23% range prompt oxygenation therapy; values above 23% increase the need for intubation.