Impact of admission viral load on respiratory outcomes in hospitalized SARS-CoV-2 infected patients with cancer and without cancer: A 2-, 4- and 6-months follow-up prospective study.

BACKGROUND: This prospective follow-up study aimed to determine the temporal changes in respiratory outcomes over 6 months period in patients with and without cancer hospitalized for severe COVID-19 and to determine the associated risk factors based on admission viral load. METHODS: All adult patients hospitalized with a confirmed diagnosis of severe SARS-CoV-2 infection were investigated using rRT-PCR on nasopharyngeal swab specimens. Patients were divided into three arbitrary groups according to their cycle threshold (CT) values obtained at admission as high (CT<25.0), medium (CT between 25.0 and 30.0), and low (CT>30.0) viral load. Patients had pulmonary function tests, chest high-resolution computed tomography (HRCT), and a 6-minute walking time distance measured at each follow-up visit. RESULTS: This follow-up study had a total of 112 participants, of which 75 were cancer-free and 37 had active cancer. Overall, 29.5% had a low viral load, compared to 48.2% who had a high viral load, and 22.3% had a medium viral load. For patients who did not have cancer, the mean age was 57.3 (SD 15.4) and for those who had cancer, it was 62.3 (SD 18.4). Most patients had overall better temporal changes in pulmonary function and tolerance, as well as exercise capacity, even though severe and chronic respiratory abnormalities persisted in a fraction of the patients. In patients without cancer who had a high viral load, we have seen a substantial reduction in diffusion capacity of the lungs for carbon monoxide (DLCO) predicted value with a median of 65 (IQR 63-70) while in patients with cancer, it was 60 (IQR 56-67) at 2 months. At 4 and 6 months, the predicted DLCO values for patients without cancer were 65 (IQR 61-70), whereas the predicted DLCO values for patients with active cancer were 62 (IQR 60-67) and 67 (59-73). Importantly, radiological abnormalities persisted in 22 (29%) non-cancer patients and 16 (43%) cancer patients. Multivariate regression analysis showed an increased odds ratio of impaired HRCT associated with a high viral load of 3.04 (95% CI:1.68-6.14; p < 0.001) for patients without cancer and 5.07 (95% CI: 4.04-10.8; p < 0.0001) for patients with cancer. The CT pneumonia score at hospitalization was 2.25 (95% CI:1.76-3.08; p = 0.041) and 2.85 (95% CI:1.89-5.14; p = 0.031) for non-cancer and cancer patients respectively. CONCLUSIONS: The evidence of persistent pulmonary abnormalities and radiographic changes was found in both patient groups who had high viral load at hospital admission and suggesting that SARS-CoV-2 viral load might serve as a useful indicator to predict the development of respiratory complications in patients with COVID-19.

Lung Ultrasound (LUS) in COVID-19 Pneumonia: Usefulness in Two Atypical Cases.

Clinical experience and scientific articles have shown that patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be paucisymptomatic or asymptomatic at the time of diagnosis. In this paper, we will discuss two paucisymptomatic patients with blood tests suggestive for SARS-CoV-2 infection but with repeated negative nasopharyngeal swabs and without typical features of COVID-19 pneumonia on chest high-resolution computed tomography. In these cases, lung ultrasound helped to raise clinical suspicion of COVID-19 pneumonia and facilitate diagnosis. LEARNING POINTS: During the current COVID-19 pandemic, lung ultrasound (LUS) is being used extensively to evaluate and monitor lung damage in infected patients.Several patients have been described with negative PCR swabs who tested positive for SARS-CoV-2 in bronchoalveolar lavage fluid.Typical signs of interstitial pneumonia on LUS strongly indicate COVID-19 pneumonia, thus suggesting further investigation and invasive tests to confirm the diagnosis.

Spectrum of High Resolution Computed tomography chest findings in PCR positive COVID-19 patients according to duration of infection and CT severity score assessment

Objective: To evaluate the spectrum of HRCT findings of COVID-19 in RT-PCR positive patients according to duration of infection and severity of disease. Methods: This retrospective study was conducted at Radiology department of Lahore General Hospital, Lahore from May to July 2020. Total 40 COVID-19 patients were reviewed for clinical features, HRCT chest findings based on time from symptom onset and CT conduction. Chi-square and fissure exact test were used for measuring association with severity of COVID-19, p value ≤0.05 was reported significant. Mean CT scores were calculated. ROC curve analysis showed threshold values of CT-SS for severe disease. Results: Of total 40 patients with age ranged from 22-83 years, 22(55%) were males and 18(45%) females. The hallmark of COVID-19 was combined GGO and consolidation, GGO alone and consolidation alone in bilateral, sub pleural and posterior distribution. Early stage had normal CT or GGO alone, intermediate and late stage had both GGO and consolidation. Septal lines/bands and crazy paving pattern were prevalent in late stage. Clinically, 24 (60%) were in severe group and 16(40%) in mild group. Severity of COVID-19 was associated with GGO alone (p=0.05), GGO and consolidation (p=0.01), crazy paving (p=0.01) and lung scores (p≤0.05). The threshold values of CT-SS for identifying severe disease by two radiologists were 18.50 and 20.50. Conclusion: HRCT manifestations along with CT-SS aids in predicting disease severity. Staging according to duration of infection is effective in understanding variation in pattern of chest findings in coronavirus disease.

One-year pulmonary impairment after severe COVID-19: a prospective, multicenter follow-up study.

BACKGROUND: Long-term pulmonary sequelae following hospitalization for SARS-CoV-2 pneumonia is largely unclear. The aim of this study was to identify and characterise pulmonary sequelae caused by SARS-CoV-2 pneumonia at 12-month from discharge. METHODS: In this multicentre, prospective, observational 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 12 months from discharge. Pulmonary function tests and diffusion capacity for carbon monoxide (DLCO), 6 min walking test, high resolution CT (HRCT) scan, and modified Medical Research Council (mMRC) dyspnea scale were collected. RESULTS: Out of 287 patients hospitalized with SARS-CoV-2 pneumonia and followed up at 1 year, DLCO impairment, mainly of mild entity and improved with respect to the 6-month follow-up, was observed more frequently in the "oxygen only" and "IMV" group (53% and 49% of patients, respectively), compared to 29% in the "CPAP" group. Abnormalities at chest HRCT were found in 46%, 65% and 80% of cases in the "oxygen only", "CPAP" and "IMV" group, respectively. Non-fibrotic interstitial lung abnormalities, in particular reticulations and ground-glass attenuation, were the main finding, while honeycombing was found only in 1% of cases. Older patients and those requiring IMV were at higher risk of developing radiological pulmonary sequelae. Dyspnea evaluated through mMRC scale was reported by 35% of patients with no differences between groups, compared to 29% at 6-month follow-up. CONCLUSION: DLCO alteration and non-fibrotic interstitial lung abnormalities are common after 1 year from hospitalization due to SARS-CoV-2 pneumonia, particularly in older patients requiring higher ventilatory support. Studies with longer follow-ups are needed.

Comparison of chest HRCT severity score in PCR positive and PCR negative clinically suspected COVID-19 Patients.

Background: The limitations and false-negative results of Real-time Polymerase chain reaction (RT PCR) in diagnosing COVID-19 infection demand the need for imaging modalities such as chest HRCT to improve the diagnostic accuracy and assess the severity of the infection. Objectives: The study aimed to compare the chest HRCT severity scores in RT-PCR positive and negative cases of COVID-19. Methods: This cross-sectional study included 50 clinically suspected COVID-19 patients. Chest HRCT and PCR testing of all 50 patients were done and the chest HRCT severity scores for each lung and bronchopulmonary segments were compared in patients with positive and negative PCR results. Chi-square and Mann Whitney U test were used to assess differences among study variables. Results: Chest HRCT severity score was more in PCR negative patients than in those with PCR positive results. However, the difference was not significant (p=0.11). There was a significant association in severity scores of the anterior basal segment of the left lung (p=0.022) and posterior segment upper lobe of right lung (p=0.035) with PCR results. This association was insignificant for other bronchopulmonary segments (p>0.05). Conclusion: CR negativity does not rule out infection in clinically suspected COVID-19 patients. The use of chest HRCT helps to determine the extent of lung damage in clinically suspected patients irrespective of PCR results. Guidelines that consider clinical symptoms, chest HRCT severity score and PCR results for a confirmed diagnosis of COVID-19 in suspected patients are needed.

Lung ultrasound correlates with radiographic severity and pattern in COVID-19 pneumonia: a preliminary study.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) was outbreaking in late 2019 and a proportion of patients developed to pneumonia. Although chest CT is a pivotal diagnostic tool for COVID-19 pneumonia, CT is expensive and also radiological burden for patients. There is urgent to investigate the role of lung ultrasound (LUS) in diagnosing and monitoring COVID-19 pneumonia. METHODS: A total of 8 patients with confirmed cases of COVID-19 pneumonia in Shantou Central Hospital from January 2020 to February 2020 were retrospectively studied. All participants underwent chest HRCT and LUS examination; both were independently performed within 1 day of the other. The radiological patterns were reviewed by 2 radiologists who were blind to the clinical information. A senior ultrasound physician, blind to HRCT results and clinical data, performed bedside LUS in the isolation ward. The CT score was used (a semi-quantitative scoring system) to assess radiographic severity and extent. A B-lines score denoting the extent and severity of sonographic lesion was calculated by summing the number of B-lines on 18 scanning sites. RESULTS: B-lines (100%), pleural irregularities (25%), consolidation (25%), and pleural effusion (25%) were the main findings of LUS examination. Interstitial abnormalities, ground-glass opacities (GGO), consolidations and local or bilateral patchy shadowing were the main findings of HRCT examination. The findings of LUS and HRCT were compared point to point and high consistency was found between the 2 measurements. A significant correlation was also found between the B-lines score and CT score [r=0.96, 95% confidence interval (CI): 0.81 to 0.99, P=0.0001]. CONCLUSIONS: Both LUS patterns and B-lines score are significantly correlated with HRCT findings and score, respectively, supporting its role in assessing COVID-19 pneumonia severity, screening, and following up dynamic changes of pneumonia.

The spider web sign in Covid-19 pneumonia: An interesting case studied to resolution with computed tomography.

Since the widespread of acute respiratory syndrome infection caused by Coronavirus-19, unenhanced computed tomography (CT) was considered a useful imaging tool commonly used in early diagnosis and monitoring of patients with complicated Covid-19 pneumonia. Many typical imaging features of this disease were described such as bilateral multilobar ground-glass opacity (GGO) with a prevalent peripheral or posterior distribution, mainly in the lower lobes, and sometimes consolidative opacities superimposed on GGO. As less common findings were mentioned septal thickening, bronchiectasis, pleural thickening, and subpleural involvement. Here we describe the case of a patient, with Covid-19 pneumonia, that had the spider web sign, a triangular or angular GGO in the subpleural lung, documented at CT.

High resolution computed tomography for the diagnosis of 2019 novel coronavirus (2019-nCoV) pneumonia: a study from multiple medical centers in western China.

BACKGROUND: To evaluate the role of high-resolution computed tomography (HRCT) in the diagnosis of 2019 novel coronavirus (2019-nCoV) pneumonia and to provide experience in the early detection and diagnosis of 2019-nCoV pneumonia. METHODS: Seventy-two patients confirmed to be infected with 2019-nCoV from multiple medical centers in western China were retrospectively analyzed, including epidemiologic characteristics, clinical manifestations, laboratory findings and HRCT chest features. RESULTS: All patients had lung parenchymal abnormalities on HRCT scans, which were mostly multifocal in both lungs and asymmetric in all patients, and were mostly in the peripheral or subpleural lung regions in 52 patients (72.22%), in the central lung regions in 16 patients (22.22%), and in both lungs with "white lung" manifestations in 4 patients (5.56%). Subpleural multifocal consolidation was a predominant abnormality in 38 patients (52.78%). Ground-glass opacity was seen in 34 patients (47.22%). Interlobular septal thickening was found in 18 patients, 8 of whom had only generally mild thickening with no zonal predominance. Reticulation was seen in 8 patients (11.11%), and was mild and randomly distributed. In addition, both lungs of 28 patients had 2 or 3 CT imaging features. Out of these 72 patients, 36 were diagnosed as early stage, 32 patients as progressive stage, and 4 patient as severe stage pneumonia. Moreover, the diagnostic accuracy of HRCT features combined with epidemiological history was not significantly different from the detection of viral nucleic acid (all P >0.05). CONCLUSIONS: The HRCT features of 2019-nCoV pneumonia are characteristic to a certain degree, which when combined with epidemiological history yield high clinical value in the early detection and diagnosis of 2019-nCoV pneumonia.

Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study.

BACKGROUND: To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT). METHODS: The Ethic Committee of our institution approved the protocol of this study and waived the requirement for patient informed consent. Two hundreds and ninety-five patients were enrolled in this study (healthy person: 149; COVID-19 patients: 146), which were divided into three separate non-overlapping cohorts (training set, n=135, healthy person, n=69, patients, n=66; validation set, n=20, healthy person, n=10, patients, n=10; test set, n=140, healthy person, n=70, patients, n=70). The DenseNet was trained and tested to classify the images as having manifestation of COVID-19 or as healthy. A radiologist also blindly evaluated all the test images and rechecked the misdiagnosed cases by DenseNet. Receiver operating characteristic curves (ROC) and areas under the curve (AUCs) were used to assess the model performance. The sensitivity, specificity and accuracy of DenseNet model and radiologist were also calculated. RESULTS: The DenseNet algorithm model yielded an AUC of 0.99 (95% CI: 0.958-1.0) in the validation set and 0.98 (95% CI: 0.972-0.995) in the test set. The threshold value was selected as 0.8, while for validation and test sets, the accuracies were 95% and 92%, the sensitivities were 100% and 97%, the specificities were 90% and 87%, and the F1 values were 95% and 93%, respectively. The sensitivity of radiologist was 94%, the specificity was 96%, while the accuracy was 95%. CONCLUSIONS: Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists' evaluation) and radiologists' workload.