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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-309465

ABSTRACT

Background: Chest CT is working as a first-line imaging modality for diagnosing Corona Virus Disease 2019 (COVID-19). Whether the CT findings could differentiate between the COVID-19 admitted out of Wuhan and common pneumonia has not been investigated. This study aimed to compare the chest CT features of patients with COVID-19 admitted out of Wuhan, as against the patients with common pneumonia. Methods: : This retrospective study enrolled 37 individuals with COVID-19 from six medical centers out of Wuhan from January 17 th to February 26 th . Another group of 41 patients with acute pneumonia collected from the same timeframe in 2019 were enrolled as the control group. All the patients had high-resolution chest CT (HRCT) scans. Clinical variables were recorded including exposure history, clinical symptoms and laboratory findings. For each HRCT, pulmonary lesions including ground-glass opacification (GGO), consolidation, and evidence of fibrosis were recorded. The Student’s t test or Wilcoxon’s test was used for comparison between COVID-19 and common pneumonia. Spearman correlation was used to evaluate correlations between the pneumonia findings on CT and clinical variables. Results: : A total of 37 patients (M/F:19/18;43.73±16.71 years) in COVID-19 group and 41(M/F:13/28;49.77±15.00 years) in common pneumonia group were evaluated. Patients with COVID-19 demonstrated a typical pattern of bilateral, multi-lobal GGO, sometimes with consolidation and fibrosis, but a mild degree of pneumonia findings than the control group ( P = 0.0024). 23/37 (62.16%) patients with COVID-19 had a preferable subpleural distribution, while the patients with common pneumonia had higher frequency of peribronchovascular pattern (16/41, 39.02%, P =0.0046). The duration between the illness onset and CT were significantly correlated with the severity scores in both groups. Conclusion: Patients with COVID-19 admitted out of Wuhan demonstrated a milder pulmonary change and a preferable subpleural pattern on HRCT when comparing with the patients with common pneumonia.

2.
Travel Med Infect Dis ; 39: 101950, 2021.
Article in English | MEDLINE | ID: covidwho-966342

ABSTRACT

BACKGROUND: To investigate and compare the clinical and imaging features among family members infected with COVID-19. METHODS: We retrospectively collected a total of 34 COVID-19 cases (15 male, 19 female, aged 48 ± 16 years, ranging from 10 to 81 years) from 13 families from January 17, 2020 through February 15, 2020. Patients were divided into two groups: Group 1 - part of the family members (first-generation) who had exposure history and others (second-generation) infected through them, and Group 2 - patients from the same family having identical exposure history. We collected clinical symptoms, laboratory findings, and high-resolution computed tomography (HRCT) features for each patient. Comparison tests were performed between the first- and second-generation patients in Group 1. RESULTS: In total there were 21 patients in Group 1 and 20 patients in Group 2. For Group 1, first-generation patients had significantly higher white blood cell count (6.5 × 109/L (interquartile range (IQR): 4.9-9.2 × 109/L) vs 4.5 × 109/L (IQR: 3.7-5.3 × 109/L); P = 0.0265), higher neutrophil count (4.9 × 109/L (IQR: 3.6-7.3 × 109/L) vs 2.9 × 109/L (IQR: 2.1-3.3 × 109/L); P = 0.0111), and higher severity scores on HRCT (3.9 ± 2.4 vs 2.0 ± 1.3, P = 0.0362) than the second-generation patients. Associated underlying diseases (odds ratio, 8.0, 95% confidence interval: 3.4-18.7, P = 0.0013) were significantly correlated with radiologic severity scores in second-generation patients. CONCLUSION: Analysis of the family cluster cases suggests that COVID-19 had no age or sex predominance. Secondarily infected patients in a family tended to develop milder illness, but this was not true for those with existing comorbidities.


Subject(s)
COVID-19/pathology , Family , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/transmission , Child , China/epidemiology , Female , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Young Adult
3.
SSRN; 2020.
Preprint | SSRN | ID: ppcovidwho-789

ABSTRACT

Background: The predictors for critical illness of severe acute respiratory syndrome (SARS) and coronavirus disease 2019 (COVID-19) have not been identified. We

4.
Nat Med ; 26(8): 1224-1228, 2020 08.
Article in English | MEDLINE | ID: covidwho-291852

ABSTRACT

For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , Thorax/diagnostic imaging , Tomography, X-Ray Computed , Adult , Artificial Intelligence , Betacoronavirus/genetics , Betacoronavirus/pathogenicity , COVID-19 , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/genetics , Coronavirus Infections/virology , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/genetics , Pneumonia, Viral/virology , Real-Time Polymerase Chain Reaction , SARS-CoV-2 , Thorax/pathology , Thorax/virology
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