Compressive ultrasound can predict early pulmonary embolism onset in COVID patients.

PURPOSE: To evaluate the usefulness of compressive ultrasound (CUS) for the diagnosis of deep vein thrombosis (DVT) in patients with SARS-CoV-2-related infection. METHODS: 112 hospitalized patients with confirmed SARS-CoV-2 infection were retrospectively enrolled. CUS was performed within 2 days of admission and consisted in the assessment of the proximal and distal deep venous systems. Lack of compressibility, or direct identification of an endoluminal thrombus, were the criteria used for the diagnosis of DVT. Pulmonary embolism (PE) events were investigated at computed tomography pulmonary angiography (CTPA) within 5 days of follow-up. Logistic binary regression was computed to determine which clinical and radiological parameters were independently associated with PE onset. RESULTS: Overall, the incidence of DVT in our cohort was about 43%. The most common district involved was the left lower limb (68.7%) in comparison with the right one (58.3%) while the upper limbs were less frequently involved (4.2% the right one and 2.1% the left one, respectively). On both sides, the distal tract of the popliteal vein was the most common involved (50% right side and 45.8% left side). The presence of DVT in the distal tract of the right popliteal vein (OR = 2.444 95%CIs 1.084-16.624, p = 0.038), in the distal tract of the left popliteal vein (OR = 4.201 95%CIs 1.484-11.885, p = 0.007), and D-dimer values (OR = 2.122 95%CIs 1.030-5.495, p = 0.003) were independently associated with the onset on PE within 5 days. CONCLUSIONS: CUS should be considered a useful tool to discriminate which category of patients can develop PE within 5 days from admission.

Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia.

OBJECTIVES: To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes. METHODS: All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated. RESULTS: A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO2 (r = 0.176), HCO3- (r = 0.284), and PaO2/FiO2 (P/F) values (r = - 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = -0.225), CRP (r = 0.306), PaCO2 (r = 0.227), pH (r = 0.162), HCO3- (r = 0.394), and P/F (r = - 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05). CONCLUSION: The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation. KEY POINTS: • Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia. • All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count. • All lung volumes correlate with patient's outcome, in particular concerning invasive ventilation.

Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia.

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy.

BACKGROUND: We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. METHODS: We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. RESULTS: At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2. CONCLUSIONS: This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

Acute pulmonary embolism in hospitalized patients with SARS-CoV-2-related pneumonia: multicentric experience from Italian endemic area.

PURPOSE: To analyze pulmonary embolism (PE) on chest computed tomography pulmonary angiography (CTPA) in hospitalized patients affected by SARS-CoV-2, according to the severity of lung disease based both on temporal CT features changes and on CT-severity lung involvement (CT-severity score), along with the support of clinical and laboratory findings. METHODS: We retrospectively enrolled a total of 170 patients with confirmed SARS-CoV-2 infection who underwent CTPA examination for PE suspicion. Pulmonary arteries diameters, right ventricle/left ventricle (RV/LV) ratio, presence, absence, and distribution of PE, pulmonary artery obstructive index (PAO index), and lobe involvement were recorded. All CT scans were reviewed to assess temporal CT changes and the COVID CT-severity score. RESULTS: A total of 76 out of 170 patients (44.7%) developed PE without having any major risk factors for venous thromboembolism. The most severe pulmonary arteries involvement, expressed in terms of PAO Index, occurred in those patients with markedly elevated D-dimer and C-reactive protein (CRP) values and those patients with an advanced temporal stage of lung disease. The majority PE-positive patients were hospitalized in non-intensive wards. PE-positive patients showed a slightly higher hospitalization time in comparison with PE-negative ones. In the three months of study, overall 85.9% of patients were discharged while 14.1% died, of whom 13 PE-positive (54.2%). CONCLUSIONS: Patients hospitalized for SARS-CoV-2 infection present a higher cumulative incidence of PE compared to the general population of hospitalized patients, regardless of the severity of lung inflammation or the temporal stage of the disease.

Chest CT in the emergency department for suspected COVID-19 pneumonia.

PURPOSE: In overwhelmed emergency departments (EDs) facing COVID-19 outbreak, a swift diagnosis is imperative. CT role was widely debated for its limited specificity. Here we report the diagnostic role of CT in two EDs in Lombardy, epicenter of Italian outbreak. MATERIAL AND METHODS: Admitting chest CT from 142 consecutive patients with suspected COVID-19 were retrospectively analyzed. CT scans were classified in "highly likely," "likely," and "unlikely" COVID-19 pneumonia according to the presence of typical, indeterminate, and atypical findings, or "negative" in the absence of findings, or "alternative diagnosis" when a different diagnosis was found. Nasopharyngeal swab results, turnaround time, and time to positive results were collected. CT diagnostic performances were assessed considering RT-PCR as reference standard. RESULTS: Most of cases (96/142, 68%) were classified as "highly likely" COVID-19 pneumonia. Ten (7%) and seven (5%) patients were classified as "likely" and "unlikely" COVID-19 pneumonia, respectively. In 21 (15%) patients a differential diagnosis was provided, including typical pneumonia, pulmonary edema, neoplasia, and pulmonary embolism. CT was negative in 8/142 (6%) patients. Mean turnaround time for the first COVID-19 RT-PCR was 30 ± 13 h. CT diagnostic accuracy in respect of the first test swab was 79% and increased to 91.5% after repeated swabs and/or BAL, for 18 false-negative first swab. CT performance was good with 76% specificity, 99% sensitivity, 90% positive predictive value and 97% negative predictive value. CONCLUSION: Chest CT was useful to streamline patients' triage while waiting for RT-PCR in the ED, supporting the clinical suspicion of COVID-19 or providing alternative diagnosis.

Chest X-ray features of SARS-CoV-2 in the emergency department: a multicenter experience from northern Italian hospitals.

OBJECTIVES: To evaluate the imaging features of routine admission chest X-ray in patients referred for novel Coronavirus 2019 infection. METHODS: All patients referred to the emergency departments, RT-PCR positive for SARS-CoV-2 infection were evaluated. Demographic and clinical data were recorded. Two radiologists (8 and 15 years of experience) reviewed all the X-ray images and evaluated the following findings: interstitial opacities, alveolar opacities (AO), AO associated with consolidation, consolidation and/or pleural effusion. We stratified patients in groups according to the time interval between symptoms onset (cut-off 5 days) and X-ray imaging and according to age (cut-off 60 years old). Computed tomography was performed in case of a discrepancy between clinical symptoms, laboratory and X-ray findings, and/or suspicion of complications. RESULTS: A total of 468 patients were tested positive for SARS-CoV-2. Lung lesions primarily manifested as interstitial opacities (71.7%) and AO opacities (60.5%), more frequently bilateral (64.5%) and with a peripheral predominance (62.5%). Patients admitted to the emergency radiology department after 5 days from symptoms onset, more frequently had interstitial and AO opacities, in comparison to those admitted within 5 days, and lung lesions were more frequently bilateral and peripheral. Older patients more frequently presented interstitial and AO opacities in comparison to younger ones. Sixty-eight patients underwent CT that principally showed the presence of ground-glass opacities and consolidations. CONCLUSIONS: The most common X-ray pattern is multifocal and peripheral, associated with interstitial and alveolar opacities. Chest X-ray, compared to CT, can be considered a reliable diagnostic tool, especially in the Emergency setting.

Diagnostic impact of bedside chest X-ray features of 2019 novel coronavirus in the routine admission at the emergency department: case series from Lombardy region.

PURPOSE: To evaluate the diagnostic accuracy and the imaging features of routine admission chest X-ray in patients suspected for novel Coronavirus 2019 (SARS-CoV-2) infection. METHOD: We retrospectively evaluated clinical and X-ray features in all patients referred to the emergency department for suspected SARS-CoV-2 infection between March 1st and March 13th. A single radiologist with more than 15 years of experience in chest-imaging evaluated the presence and extent of alveolar opacities, reticulations, and/or pleural effusion. The percentage of lung involvement (range <25 % to 75-100 %) was also calculated. We stratified patients in groups according to the time interval between symptoms onset and X-ray imaging (≤ 5 and > 5 days) and according to age (≤ 50 and > 50 years old). RESULTS: A total of 518 patients were enrolled. Overall 314 patients had negative and 204 had positive RT-PCR results. Lung lesions in patients with SARS-Cov2 pneumonia primarily manifested as alveolar and interstitial opacities and were mainly bilateral (60.8 %). Lung abnormalities were more frequent and more severe by symptom duration and by increasing age. The sensitivity and specificity of chest X-ray at admission in the overall cohort were 57 % (95 % CI = 47-67) and 89 % (83-94), respectively. Sensitivity was higher for patients with symptom onset > 5 days compared to ≤ 5 days (76 % [62-87] vs 37 % [24-52]) and in patients > 50 years old compared to ≤ 50 years (59 % [48-69] vs 47 % [23-72]), at the expense of a slightly lower specificity (68 % [45-86] and 82 % [73-89], respectively). CONCLUSIONS: Overall chest X-ray sensitivity for SARS-CoV-2 pneumonia was 57 %. Sensitivity was higher when symptoms had started more than 5 days before, at the expense of lesser specificity, while slightly higher in older patients in comparison to younger ones.