Clinical and CT imaging features of 2019 novel coronavirus disease (COVID-19)

Zhu, Ying; Z, Gao; Liu, Yang Li; Xu, Dong Roman; Guan, Tian; Li, Zi Ping; Kuang, Jian; Li, Xiang Min; Yang, You You; Feng, Shi Ting

doi:10.1016/j.jinf.2020.03.033

Cited by 73 publications

(44 citation statements)

References 9 publications

(1 reference statement)

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“…Lymphopenia, including T cells, B cells, and NK cells and an increase in NLR were common among patients with COVID-19, and were more pronounced in the severe patients. These results are in accordance with other studies [12,13,14]) and the ndings of limited autopsies and biopsies, which reported markedly shrunken spleens and a signi cant reduction in lymphocytes (Chinese Clinical Guidance for COVID-19 Pneumonia Diagnosis and Treatment, 7th edition). Immune cells are important effectors of a host's immune system, and play crucial roles in anti-viral infections [15].…”

Section: Discussionsupporting

confidence: 93%

Dynamic Changes in the Immune Response Correlate with Disease Severity and Outcomes During Infection with SARS-CoV-2

Zheng

Chen

Huang

et al. 2020

Preprint

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Background:Little is known about the dynamic changes in the patient immune responses to SARS-CoV-2, and how different responses are correlated with disease severity and outcomes.Method:A total of 74 patients with confirmed COVID-19 were included from January 29th to February 15th 2020. Clinical characteristics and dynamic changes in immune response were analyzed and compared between severe and non-severe patients. ResultsOf the 74 patients, 17 suffered from severe disease and 57 from non-severe disease. Patients with severe disease tended be older (65.29 ± 12.33 years vs. 45.37 ± 18.66 years), and had a greater degree of underlying disease (41.18% vs. 24.56%) , lower lymphocytes counts (0.69 ± 0.36 × 10⁹ vs. 1.46 ± 0.75 × 10⁹) , higher neutrophil-lymphocyte-ratios (NLRs; 3.76 (3.15–5.51) vs. 2.07 (1.48–2.93)) and lower eosinophil counts (0.01 ± 0.01 × 10⁹ vs. 0.05 ± 0.07 × 10⁹), than that in non-severe patients. The number of immune cell subtypes, including helper T cells, suppressor T cells, B cells, and natural killer cells was significantly decreased in severe cases compared to that in non-severe cases (335.47 vs. 666.46/mL; 158 vs. 334/mL; 95 vs. 210/mL; 52 vs. 122/mL, respectively). As the condition of the patients improved, the number of neutrophils decreased significantly in the severe patients. All patients who recovered exhibited a gradual and persistent increase in eosinophil counts and lymphocyte counts, including helper T cells, suppressor T cells, and natural killer cells. In addition, the levels of most of inflammatory cytokines, including IFN-γ, IL-2, IL-4, IL-6, IL-10, IL-17A, and TNF–α generally decreased as the patients gradually recovered.ConclusionsCollectively, our study provides novel information on the kinetics of the immune responses to COVID-19. Furthermore, our study indicates that both innate and adaptive immune responses correlate with better clinical outcomes.

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Section: Discussionsupporting

confidence: 93%

Dynamic Changes in the Immune Response Correlate with Disease Severity and Outcomes During Infection with SARS-CoV-2

Zheng

Chen

Huang

et al. 2020

Preprint

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“…CT findings of viral pneumonia is listed as one of the three clinical evidences (the other two are respiratory symptoms and blood test) to identify suspected cases in the guideline of Chinese Health Commission (CHC) (8). Typical CT signs of COVID-19 infection include ground-glass opacities (GGO), GGO with lung consolidation, bronchial dilation, bilateral involvement, and peripheral distribution (9)(10)(11). These abnormal CT findings play an important role in assisting the diagnosis of COVID-19 patients.…”

Section: Introductionmentioning

confidence: 99%

CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients

Cai

Xue

et al. 2020

Academic Radiology

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This study was to investigate the CT quantification of COVID-19 pneumonia and its impacts on the assessment of disease severity and the prediction of clinical outcomes in the management of COVID-19 patients. Materials Methods: Ninety-nine COVID-19 patients who were confirmed by positive nucleic acid test (NAT) of RT-PCR and hospitalized from January 19, 2020 to February 19, 2020 were collected for this retrospective study. All patients underwent arterial blood gas test, routine blood test, chest CT examination, and physical examination on admission. In addition, follow-up clinical data including the disease severity, clinical treatment, and clinical outcomes were collected for each patient. Lung volume, lesion volume, nonlesion lung volume (NLLV) (lung volume À lesion volume), and fraction of nonlesion lung volume (%NLLV) (nonlesion lung volume / lung volume) were quantified in CT images by using two U-Net models trained for segmentation of lung and COVID-19 lesions in CT images. Furthermore, we calculated 20 histogram textures for lesions volume and NLLV, respectively. To investigate the validity of CT quantification in the management of COVID-19, we built random forest (RF) models for the purpose of classification and regression to assess the disease severity (Moderate, Severe, and Critical) and to predict the need and length of ICU stay, the duration of oxygen inhalation, hospitalization, sputum NAT-positive, and patient prognosis. The performance of RF classifiers was evaluated using the area under the receiver operating characteristic curves (AUC) and that of RF regressors using the root-mean-square error. Results: Patients were classified into three groups of disease severity: moderate (n = 25), severe (n = 47) and critical (n = 27), according to the clinical staging. Of which, a total of 32 patients, 1 (1/25) moderate, 6 (6/47) severe, and 25 critical (25/27), respectively, were admitted to ICU. The median values of ICU stay were 0, 0, and 12 days, the duration of oxygen inhalation 10, 15, and 28 days, the hospitalization 12, 16, and 28 days, and the sputum NAT-positive 8, 9, and 13 days, in three severity groups, respectively. The clinical outcomes were complete recovery (n = 3), partial recovery with residual pulmonary damage (n = 80), prolonged recovery (n = 15), and death (n = 1). The %NLLV in three severity groups were 92.18 § 9.89%, 82.94 § 16.49%, and 66.19 § 24.15% with p value <0.05 among each two groups. The AUCs of RF classifiers using hybrid models were 0.927 and 0.929 in classification of moderate vs (severe + critical), and severe vs critical, respectively, which were significantly higher than either radiomics models or clinical models (p < 0.05). The root-mean-square errors of RF regressors were 0.88 weeks for prediction of duration of hospitalization (mean: 2.60 § 1.01 weeks), 0.92 weeks for duration of oxygen inhalation (mean: 2.44 § 1.08 weeks), 0.90 weeks for duration of sputum NAT-positive (mean: 1.59 § 0.98 weeks), and 0.69 weeks for stay of ICU (mean: 1.32 § 0.67 weeks), respectively....

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“…At the same time, COVID-19 is also spread all over the world, such as Korea [20,21], Italy [22,23], Japan [24,25], the United States [26] and Iran, etc. As a new infectious disease, the clinical features and prognosis of COVID-19 is not completely clear yet [27][28][29]. The clinical characteristics, the proportion of severe patients and the mortality of patients with COVID-19 are different according to different individual publications.…”

Section: Discussionmentioning

confidence: 99%

A Meta-analysis of Clinical Characteristics and Mortality COVID-19 Pneumonia

Jiang

Lou

et al. 2020

Preprint

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Objective To investigate the Corona Virus Disease 2019(COVID-19) clinical characteristics and mortality risk by pooling the open published data.Methods Studies relevant to COVID-19 published in Pubmed, China Wanfang database, ChinaXiv and medRxiv were systematic screened by using the text word of “COVID-19”, 2019-nCoV, “SARS-CoV-2”, “NCP”. The mortality and clinical characteristic of the COVID-19 cases such as male/female ratio, mechanical ventilation ratio and top clinical symptom rate of the COVID-19 cases were pooled.ResultsTen clinical studies relevant to COVID-19 were identified by electronic searching the related databases. The combined mortality was 0.03(95%CI: 0.01-0.04) for COVID-19 cases by random effect model. The pooled female ratio of the COVID-19 cases from 10 published data was 0.41(95%CI:0.37-0.46). The pooled invasive and non-invasive ventilation ratio were 0.03(95%CI:0.01-0.05) and 0.06(95%CI:0.02-0.09) respectively for patients with COVID-19 pneumonia. The pooled clinical symptom rate of fever, cough, headache and fatigue were 0.80(95%CI:0.60-1.01), 0.12(95%CI:0.08-0.17), 0.68(95%CI:0.57-0.73) and 0.51(95%CI:0.36-0.67) respectively under random effect model.Conclusion According to the present published data, male was more cline to susceptible to COVID-19 compared to female. The fever, cough and fatigue were the most common symptom of COVID-19 cases. About 10% of patients received invasive or noninvasive mechanical ventilation with the overall crude mortality of 3%.

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Clinical and CT imaging features of 2019 novel coronavirus disease (COVID-19)

Cited by 73 publications

References 9 publications

Dynamic Changes in the Immune Response Correlate with Disease Severity and Outcomes During Infection with SARS-CoV-2

Dynamic Changes in the Immune Response Correlate with Disease Severity and Outcomes During Infection with SARS-CoV-2

CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients

A Meta-analysis of Clinical Characteristics and Mortality COVID-19 Pneumonia

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