Time series analysis of the demand for COVID-19 related chest imaging during the first wave of the SARS-CoV-2 pandemic: An explorative study.

OBJECTIVES: The aim of this study was to investigate possible patterns of demand for chest imaging during the first wave of the SARS-CoV-2 pandemic and derive a decision aid for the allocation of resources in future pandemic challenges. MATERIALS AND METHODS: Time data of requests for patients with suspected or confirmed coronavirus disease 2019 (COVID-19) lung disease were analyzed between February 27th and May 27th 2020. A multinomial logistic regression model was used to evaluate differences in the number of requests between 3 time intervals (I1: 6am - 2pm, I2: 2pm - 10pm, I3: 10pm - 6am). A cosinor model was applied to investigate the demand per hour. Requests per day were compared to the number of regional COVID-19 cases. RESULTS: 551 COVID-19 related chest imagings (32.8% outpatients, 67.2% in-patients) of 243 patients were conducted (33.3% female, 66.7% male, mean age 60 ± 17 years). Most exams for outpatients were required during I2 (I1 vs. I2: odds ratio (OR) = 0.73, 95% confidence interval (CI) 0.62-0.86, p = 0.01; I2 vs. I3: OR = 1.24, 95% CI 1.04-1.48, p = 0.03) with an acrophase at 7:29 pm. Requests for in-patients decreased from I1 to I3 (I1 vs. I2: OR = 1.24, 95% CI 1.09-1.41, p = 0.01; I2 vs. I3: OR = 1.16, 95% CI 1.05-1.28, p = 0.01) with an acrophase at 12:51 pm. The number of requests per day for outpatients developed similarly to regional cases while demand for in-patients increased later and persisted longer. CONCLUSIONS: The demand for COVID-19 related chest imaging displayed distinct distribution patterns depending on the sector of patient care and point of time during the SARS-CoV-2 pandemic. These patterns should be considered in the allocation of resources in future pandemic challenges with similar disease characteristics.

Bronchoalveolar lavage affects thorax computed tomography of healthy and SARS-CoV-2 infected rhesus macaques (Macaca mulatta).

Medical imaging as method to assess the longitudinal process of a SARS-CoV-2 infection in non-human primates is commonly used in research settings. Bronchoalveolar lavage (BAL) is regularly used to determine the local virus production and immune effects of SARS-CoV-2 in the lower respiratory tract. However, the potential interference of those two diagnostic modalities is unknown in non-human primates. The current study investigated the effect and duration of BAL on computed tomography (CT) in both healthy and experimentally SARS-CoV-2-infected female rhesus macaques (Macaca mulatta). In addition, the effect of subsequent BALs was reviewed. Thorax CTs and BALs were obtained from four healthy animals and 11 experimentally SARS-CoV-2-infected animals. From all animals, CTs were obtained just before BAL, and 24 hours post-BAL. Additionally, from the healthy animals, CTs immediately after, and four hours post-BAL were obtained. Thorax CTs were evaluated for alterations in lung density, measured in Hounsfield units, and a visual semi-quantitative scoring system. An increase in the lung density was observed on the immediately post-BAL CT but resolved within 24 hours in the healthy animals. In the infected animals, a significant difference in both the lung density and CT score was still found 24 hours after BAL. Furthermore, the differences between time points in CT score were increased for the second BAL. These results indicate that the effect of BAL on infected lungs is not resolved within the first 24 hours. Therefore, it is important to acknowledge the interference between BAL and CT in rhesus macaques.

Brief psychotic disorder in COVID-19 patient with no history of mental illness.

INTRODUCTION: COVID-19 pandemic affects mental health globally. Reports showed the increase of mental illness as a response to the COVID-19 pandemic. However, the correlation between the COVID-19 and mental illness is not fully understood yet. METHODOLOGY: We reported a brief psychotic disorder in a COVID-19 patient with no history of mental illness who was hospitalized in Persahabatan Hospital, Jakarta, Indonesia. RESULTS: Psychotic symptoms appeared five days after COVID-19 onset and laboratory tests showed elevated levels of d-dimer and fibrinogen. CONCLUSIONS: Elevated levels of d-dimer and fibrinogen suggest an ongoing COVID-19-associated coagulopathy that might cause a microdamage in the central nervous system. It might contribute to the manifestation of psychotic symptoms. The correlation between brief psychotic disorder and COVID-19 requires further investigation.

The clinical characteristics of pediatric inpatients with SARS-CoV-2 infection: A meta-analysis and systematic review.

Millions of people were infected with the coronavirus disease 2019 (COVID-19) all over the world. Data on clinical symptoms of pediatric inpatients with COVID-19 infection were unclear. The aim of study was to investigate the clinical features of pediatric inpatients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. PubMed, EMBASE, and the Cochrane Library were searched to seek for studies providing details on pediatric inpatients with SARS-CoV-2 infection which were published from 1st January to 21st April 2020. Studies with more than five pediatric inpatients were included in our meta-analysis.This study was registered in the PROSPERO database (CRD42020183550). As the results shown, fever (46%) and cough (42%) were the main clinical characters of pediatric inpatients with SARS-CoV-2 infection and the other clinical characters, such as diarrhea, vomiting, nasal congestion, and fatigue account for 10% in pediatric inpatients. The proportion of asymptomatic cases was 0.42 (95% confidence interval [CI]: 0.27-0.59) and severe cases was 0.03 (95% CI: 0.01-0.06). For the laboratory result, leukopenia (21%) and lymphocytosis (22%) were the mainly indicators for pediatric inpatients, followed by high aspartate aminotransferase (19%), lymphopenia (16%), high alanine aminotransferase (15%), high C-reactive protein (17%), leukocytosis (13%), high D-dimer (12%) and high creatine kinase-MB (5%). Regard to chest imaging features, unilateral and bilateral accounts for 22% in pediatric inpatients, respectively. In conclusion, compared with adult inpatients with SARS-CoV-2 infection, the pediatric inpatients had mild clinical characters, lab test indicators, and chest imaging features. More clinical studies focus on the pediatric patients with SARS-CoV-2 infection in other countries should be conducted.

Alert for non-respiratory symptoms of coronavirus disease 2019 patients in epidemic period: A case report of familial cluster with three asymptomatic COVID-19 patients.

At present, coronavirus disease 2019 (COVID-19) is rampaging around the world. However, asymptomatic carriers intensified the difficulty of prevention and management. Here we reported the screening, clinical features, and treatment process of a family cluster involving three COVID-19 patients. The discovery of the first asymptomatic carrier in this family cluster depends on the repeated and comprehensive epidemiological investigation by disease control experts. In addition, the combination of multiple detection methods can help clinicians find asymptomatic carriers as early as possible. In conclusion, the prevention and control experience of this family cluster showed that comprehensive rigorous epidemiological investigation and combination of multiple detection methods were of great value for the detection of hidden asymptomatic carriers.

Temporal changes in the chest based on findings from imaging in severe patients with novel coronavirus pneumonia.

The aim of the present study was to observe the temporal changes in the chest based on findings from imaging in severe patients with novel coronavirus pneumonia. A total of 33 severe confirmed cases (20 male patients and 13 female patients) were enrolled in the present study between January 31, 2020 and March 10, 2020. Chest imaging findings and clinical data were collected and analyzed. The median age was 65 years (age range, 25­90 years). As of April 7, 2020, 24 patients were discharged, and 9 patients died. With regards to the clinical manifestations, 28 patients had fever, 17 patients had a cough and 15 patients had shortness of breath. Of these, 29 patients had underlying health conditions. Ground glass opacities, consolidation and interlobular septal thickening were the most common and typical chest computerized tomography (CT) scan abnormalities. A total of 6/33 (18.2%) patients had 1 affected lobe, 6/33 (18.2%) patients had 2 affected lobes, 5/33 (15.2%) patients had 3 affected lobes, 9/33 (27.3%) patients had 4 affected lobes and 7/33 (21.2%) patients had 5 affected lobes in the initial chest CT scan. The mean interval time between two consecutive CT examinations was 4.5 days (range, 3­9 days). Most severe patients exhibited some degree of aggravation based on the CT findings in the 3 weeks from illness onset. After 3 weeks from illness onset, these severe survivors demonstrated improvements in the chest CT findings, which included complete absorption or only a few remaining fibrous stripes. Chest CT manifestations of patients infected with novel coronavirus pneumonia were diverse and varied. Severe patients had imaging features of rapid progression and slow absorption. Monitoring of chest imaging findings is vital to detect any changes in a timely manner.

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning.

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models' predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.

Calcification of the thoracic aorta on low-dose chest CT predicts severe COVID-19.

BACKGROUND: Cardiovascular comorbidity anticipates poor prognosis of SARS-CoV-2 disease (COVID-19) and correlates with the systemic atherosclerotic transformation of the arterial vessels. The amount of aortic wall calcification (AWC) can be estimated on low-dose chest CT. We suggest quantification of AWC on the low-dose chest CT, which is initially performed for the diagnosis of COVID-19, to screen for patients at risk of severe COVID-19. METHODS: Seventy consecutive patients (46 in center 1, 24 in center 2) with parallel low-dose chest CT and positive RT-PCR for SARS-CoV-2 were included in our multi-center, multi-vendor study. The outcome was rated moderate (no hospitalization, hospitalization) and severe (ICU, tracheal intubation, death), the latter implying a requirement for intensive care treatment. The amount of AWC was quantified with the CT vendor's software. RESULTS: Of 70 included patients, 38 developed a moderate, and 32 a severe COVID-19. The average volume of AWC was significantly higher throughout the subgroup with severe COVID-19, when compared to moderate cases (771.7 mm3 (Q1 = 49.8 mm3, Q3 = 3065.5 mm3) vs. 0 mm3 (Q1 = 0 mm3, Q3 = 57.3 mm3)). Within multivariate regression analysis, including AWC, patient age and sex, as well as a cardiovascular comorbidity score, the volume of AWC was the only significant regressor for severe COVID-19 (p = 0.004). For AWC > 3000 mm3, the logistic regression predicts risk for a severe progression of 0.78. If there are no visually detectable AWC risk for severe progression is 0.13, only. CONCLUSION: AWC seems to be an independent biomarker for the prediction of severe progression and intensive care treatment of COVID-19 already at the time of patient admission to the hospital; verification in a larger multi-center, multi-vendor study is desired.

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection.

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

Chest Computed Tomography Scoring in Patients With Novel Coronavirus-infected Pneumonia: Correlation With Clinical and Laboratory Features and Disease Outcome.

BACKGROUND/AIM: This study investigated the correlation of chest computed tomography (CT), findings, graded using two different scoring methods, with clinical and laboratory features and disease outcome, including a novel clinical predictive score, in patients with novel coronavirus-infected pneumonia (NCIP). PATIENTS AND METHODS: In this retrospective, observational study, CT scan of 92 NCIP patients admitted to Policlinico Tor Vergata, were analyzed using a quantitative, computed-based and a semiquantitative, radiologist-assessed scoring system. Correlations of the two radiological scores with clinical and laboratory features, the CALL score, and their association with a composite adverse outcome were assessed. RESULTS: The two scores correlated significantly with each other (ρ=0.637, p<0.0001) and were independently associated with age, LDH, estimated glomerular filtration rate, diabetes, and with the composite outcome, which occurred in 24 patients. CONCLUSION: In NCIP patients, two different radiological scores correlated with each other and with several clinical, laboratory features, and the CALL score. The quantitative score was a better independent predictor of the composite adverse outcome than the semiquantitative score.

Clinical characteristics and longitudinal chest CT features of healthcare workers hospitalized with coronavirus disease 2019 (COVID-19).

Rationale: The clinical data and corresponding dynamic CT findings were investigated in detail to describe the clinical and imaging profiles of COVID-19 pneumonia disease progression. Methods: Forty HCWs with COVID-19 were included in this study and 30 enrolled for imaging assessment. Disease was divided into four stages based on time from onset: stage 1 (1-6 days), stage 2 (7-13 days), stage 3 (14-22 days), and stage 4 (> 22 days). Clinical wand imaging data were analyzed retrospectively. Results: The cohort included 33 female and 7 male cases, with a median age of 40 years. Six had underlying comorbidities. More than half of the cases were nurses (22, 55%). Each stage included 39, 37, 34 and 32 CTs, respectively. Bilateral lesions, multifocal lesions and lesions with GGO pattern occurred in both lower lobes at all stages. The crazy-paving pattern (20, 54%), air bronchogram (13, 35%), and pleural effusion (2, 5%) were the most common CT features in stage 2. Consolidation score peaked in stage 2 whereas total lesions score peaked in stage 3. Conclusions: COVID-19 pneumonia in HCWs has a potential predilection for younger female workers. Stage 2 of COVID-19 pneumonia may be the key period for controlling progression of the disease, and consolidation scores may be an objective reflection of the severity of lung involvement.

The clinical sensitivity of a single SARS-CoV-2 upper respiratory tract RT-PCR test for diagnosing COVID-19 using convalescent antibody as a comparator.

The clinical false negative rate of reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 on a single upper respiratory tract sample was calculated using convalescent antibody testing as a comparator. The sensitivity in symptomatic individuals was 86.2% (25/29). Of the missed cases, one (3.5%) was detected by repeat RT-PCR, one by CT thorax and two (7.1%) by convalescent antibody. The clinical false negative rate of a single RT-PCR on an upper respiratory tract sample of 14% in symptomatic patients is reassuring when compared to early reports. This report supports a strategy of combining repeat swabbing, use of acute and convalescent antibody testing and CT thorax for COVID-19 diagnosis.

Chest CT Findings in Patients With Coronavirus Disease 2019 and Its Relationship With Clinical Features.

OBJECTIVES: The aim of this study was to investigate the chest computed tomography (CT) findings in patients with confirmed coronavirus disease 2019 (COVID-19) and to evaluate its relationship with clinical features. MATERIALS AND METHODS: Study sample consisted of 80 patients diagnosed as COVID-19 from January to February 2020. The chest CT images and clinical data were reviewed, and the relationship between them was analyzed. RESULTS: Totally, 80 patients diagnosed with COVID-19 were included. With regards to the clinical manifestations, 58 (73%) of the 80 patients had cough, and 61 (76%) of the 80 patients had high temperature levels. The most frequent CT abnormalities observed were ground glass opacity (73/80 cases, 91%), consolidation (50/80 cases, 63%), and interlobular septal thickening (47/80, 59%). Most of the lesions were multiple, with an average of 12 ± 6 lung segments involved. The most common involved lung segments were the dorsal segment of the right lower lobe (69/80, 86%), the posterior basal segment of the right lower lobe (68/80, 85%), the lateral basal segment of the right lower lobe (64/80, 80%), the dorsal segment of the left lower lobe (61/80, 76%), and the posterior basal segment of the left lower lobe (65/80, 81%). The average pulmonary inflammation index value was (34% ± 20%) for all the patients. Correlation analysis showed that the pulmonary inflammation index value was significantly correlated with the values of lymphocyte count, monocyte count, C-reactive protein, procalcitonin, days from illness onset, and body temperature (P < 0.05). CONCLUSIONS: The common chest CT findings of COVID-19 are multiple ground glass opacity, consolidation, and interlobular septal thickening in both lungs, which are mostly distributed under the pleura. There are significant correlations between the degree of pulmonary inflammation and the main clinical symptoms and laboratory results. Computed tomography plays an important role in the diagnosis and evaluation of this emerging global health emergency.

A SARS-CoV-2 familial cluster infection reveals asymptomatic transmission to children.

Information on SARS-CoV-2 asymptomatic infection and infectivity in children is limited. In this study, we aimed to report the epidemiological and clinical characteristics of a familial cluster infection including children with SARS-CoV-2. On February 1, 2020, two children(case 1 and case 2), an 8-year-old girl and a 9-year-old boy, were admitted to the isolation ward in Xiangyang Central Hospital, Hubei province, China, with the diagnosis of COVID-19. Before admission, they had been staying at home with their father and never contacted with any confirmed patients except their mother (case 3) who returned from Wuhan on January 22. Both case 1 and case 2 got mild symptoms. Case 3 did not develop any symptoms until February 6, 2020, with an asymptomatic period of 15 days. She was transferred to ICU and administered multiple treatment according to the disease progression and chest CT manifestations. Her nucleic acid test turned positive until Feb 21, 2020, 15 days after symptoms onset, 30 days after her return from Wuhan. Our data showed that patients with SARS-CoV-2 may have the ability to transmit during their asymptomatic period even with the negative of viral nucleic acid in pharyngeal swabs.

Clinical characteristics and risk assessment of newborns born to mothers with COVID-19.

BACKGROUND: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is causing an outbreak of pneumonia in Wuhan, Hubei Province, China, and other international areas. OBJECTIVE: Here, we report the clinical characteristics of the newborns delivered by SARS-CoV-2 infected pregnant women. METHODS: We prospectively collected and analyzed the clinical features, laboratory data and outcomes of 7 newborns delivered by SARS-CoV-2 infected pregnant women in Zhongnan Hospital of Wuhan University during January 20 to January 29, 2020. RESULTS: 4 of the 7 newborns were late preterm with gestational age between 36 weeks and 37 weeks, and the other 3 were full-term infants. The average birth weight was 2096 ± 660 g. All newborns were born without asphyxia. 2 premature infants performed mild grunting after birth, but relieved rapidly with non-invasive continuous positive airway pressure (nCPAP) ventilation. 3 cases had chest X-ray, 1 was normal and 2 who were supported by nCPAP presented mild neonatal respiratory distress syndrome (NRDS). Samples of pharyngeal swab in 6 cases, amniotic fluid and umbilical cord blood in 4 cases were tested by qRT-PCR, and there was no positive result of SARS-CoV-2 nucleic acid in all cases. CONCLUSIONS: The current data show that the infection of SARS-CoV-2 in late pregnant women does not cause adverse outcomes in their newborns, however, it is necessary to separate newborns from mothers immediately to avoid the potential threats.

Predictors for imaging progression on chest CT from coronavirus disease 2019 (COVID-19) patients.

OBJECTIVE: This study aimed to investigate the potential parameters associated with imaging progression on chest CT from coronavirus disease 19 (COVID-19) patients. RESULTS: The average age of 273 COVID-19 patients enrolled with imaging progression were older than those without imaging progression (p = 0.006). The white blood cells, platelets, neutrophils and acid glycoprotein were all decreased in imaging progression patients (all p < 0.05), and monocytes were increased (p = 0.025). The parameters including homocysteine, urea, creatinine and serum cystatin C were significantly higher in imaging progression patients (all p < 0.05), while eGFR decreased (p < 0.001). Monocyte-lymphocyte ratio (MLR) was significantly higher in imaging progression patients compared to that in imaging progression-free ones (p < 0.001). Logistic models revealed that age, MLR, homocysteine and period from onset to admission were factors for predicting imaging progression on chest CT at first week from COVID-19 patients (all p < 0.05). CONCLUSION: Age, MLR, homocysteine and period from onset to admission could predict imaging progression on chest CT from COVID-19 patients. METHODS: The primary outcome was imaging progression on chest CT. Baseline parameters were collected at the first day of admission. Imaging manifestations on chest CT were followed-up at (6±1) days.