Analysis of 2019 Novel Coronavirus Infection and Clinical Characteristics of Outpatients: An Epidemiological Study from the Fever Clinic in Wuhan, China

Liu, Wenhua; Wang, Rui; Li, Gang; Wei, Yuanhuan; Li, Xinying; Yue, Huihui; Zhang, Fengqin; Hu, Qinyong; Chu, Jiaojiao; Yuan, Xianglin; Lu, Yanjun; Xia, Liming; Zhang, Wanguang; Miao, Xiaoping; Zhao, Jianping; Wang, Cong Yi; Liu, Li; Zhang, Huilan

doi:10.2139/ssrn.3539646

Cited by 7 publications

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“…While age was found to play an important role in susceptibility and prognosis of COVID-19 patients (1,2,14), this study identified that older patients were more prone to testing positive, and had a shorter window for doing so. This result was consistent with Liu (15). It further revealed that the age effect did not fade as time elapsed.…”

Section: Discussionsupporting

confidence: 89%

Analysis and Prediction of COVID-19 Patients’ False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen: A Retrospective Study

Qiu

et al. 2020

Preprint

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Background: False negative results of SARS-CoV-2 nucleic acid detection pose threats to COVID-19 patients and medical workers alike.Objective: To develop multivariate models to determine clinical characteristics that contribute to false negative results of SARS-CoV-2 nucleic acid detection, and use them to predict false negative results as well as time windows for testing positive. Design: Retrospective Cohort Study (Ethics number of Tongji Hospital Patients: 1,324 outpatients with COVID-19Measurements: Clinical information on CT imaging reports, blood routine tests, and clinic symptoms were collected. A multivariate logistic regression was used to explain and predict false negative testing results of SARS-CoV-2 detection. A multivariate accelerated failure model was used to analyze and predict delayed time windows for testing positive. Results:Of the 1,324 outpatients who diagnosed of COVID-19, 633 patients tested positive in their first SARS-CoV-2 nucleic acid test (47.8%), with a mean age of 51 years (SD=14.9); the rest, which had a mean age of 47 years (SD=15.4), tested negative in the first test. "Ground glass opacity" in a CT imaging report was associated with a lower chance of false negatives (aOR, 0.56), and reduced the length of time window for testing positive by 26%. "Consolidation" was associated with a higher chance of false negatives (aOR, 1.57), and extended the length of time window for testing positive by 44%. In blood routine tests, basophils (aOR, 1.28) and eosinophils (aOR, 1.29) were associated with a higher chance of false negatives, and were found to extend the time window for testing positive by 23% and 41%, respectively. Age and gender also affected the significantly.Limitation: Data were generated in a large single-center study. Conclusion:Testing outcome and positive window of SARS-CoV-2 detection for COVID-19 patients were associated with CT imaging results, blood routine tests, and clinical symptoms. Taking into account relevant information in CT imaging reports, blood routine tests, and clinical symptoms helped reduce a false negative testing outcome. The predictive AFT model, what we believe to be one of the first statistical models for predicting time window of SARS-CoV-2 detection, could help clinicians improve the accuracy and efficiency of the diagnosis, and hence, optimizes the timing of nucleic acid detection and alleviates the shortage of nucleic acid detection kits around the world. Primary Funding Source: None.All rights reserved. No reuse allowed without permission.(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. refinement of this model could be achieved by including novel clinical predictors.

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Section: Discussionsupporting

confidence: 89%

Analysis and Prediction of COVID-19 Patients’ False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen: A Retrospective Study

Qiu

et al. 2020

Preprint

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“…[14] , [15] , [16] , [17] , [18] , [19] , [20] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] , [65] , [66] , [67] , [68] , [69] , [70] , [71] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] , [81] , [82] , [83] , [84] , [85] ,…”

Section: Uncited Referencesunclassified

Chest CT findings of coronavirus disease 2019 (COVID-19): A comprehensive meta-analysis of 9907 confirmed patients

et al. 2021

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We performed a systematic review and meta-analysis of the prevalence of chest CT findings in patients with confirmed COVID-19 infection. Methods: Systematic review of the literature was performed using PubMed, Scopus, Embase, and Google Scholar to retrieve original studies on chest CT findings of patients with confirmed COVID-19, available up to 10 May 2020. Data on frequency and distribution of chest CT findings were extracted from eligible studies, pooled and meta-analyzed using random-effects model to calculate the prevalence of chest CT findings. Results: Overall, 103 studies (pooled population: 9907 confirmed COVID-19 patients) were meta-analyzed. The most common CT findings were ground-glass opacities (GGOs) (77.18%, 95%CI = 72.23-81.47), reticulations (46.24%, 95%CI = 38.51-54.14), and air bronchogram (41.61%, 95%CI = 32.78-51.01). Pleural thickening (33.35%, 95%CI = 21.89-47.18) and bronchial wall thickening (15.48%, 95%CI = 8.54-26.43) were major atypical and airway findings. Lesions were predominantly distributed bilaterally (75.72%, 95%CI = 70.79-80.06) and peripherally (65.64%, 95%CI = 58.21-72.36), while 8.20% (95%CI = 6.30-10.61) of patients had no abnormal findings and pre-existing lung diseases were present in 6.01% (95%CI = 4.37-8.23). Conclusions: The most common CT findings in COVID-19 are GGOs with/without consolidation, reticulations, and air bronchogram, which often involve both lungs with peripheral distribution. However, COVID-19 might present with atypical manifestations or no abnormal findings in chest CT, which deserve clinicians' notice.

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“…WHO (World Health Organization) reports that it began in Wuhan city, located in Hubei Province of China, first reported on 21 January [14]. COVID-19 categorizes in three distinctions concerning it's infected host's severity of disease [15] [16]. To date, the statistics of its reported cases shows more than 80 percent of infected had a mild case of disease, while around 14 percent of infected experienced a severe one, suffering from breathlessness and pneumonia.…”

Section: Basic Statisticsmentioning

confidence: 99%

Understanding Epidemic Data and Statistics: A Case Study of COVID-19

Dehkordi

Alizadeh

Derakhshan

et al. 2020

Preprint

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The 2019-Novel-Coronavirus (COVID-19) has affected 181 countries and out of about 1197405 confirmed cases (By April 5). Understanding the transmission dynamics of the infection in each country which affected on a daily basis and evaluating the effectiveness of control policies is critical for our further actions. To date, the statistics of COVID-19 reported cases show more than 80 percent of infected had a mild case of disease, while around 14 percent of infected experienced a severe one and about 5 percent are categorized as critical disease victims. Today's report (2020-04-05; daily updates in the prepared website) shows the confirmed cases of COVID-19 in the US, Spain, Italy, and Germany are 308850, 126168, 124632, and 96092; respectively. Calculating the total Case Fatality Rate (CFR) of Italy (2020-04-04), about 13.3% of confirmed cases passed away. Compared to South Korea's rate of 1.8% (7 times lower than Italy) and China's 4% (69% lower than Italy), the CFR of Italy is too high. There are some effective policies that yield significant changes in the trend of cases. The lockdown policy in China, Italy, and Spain (the effect observed after some days), Shutdown of all non-essential companies in Hubei (the effect observed after 5 days), combined policy in South Korea, and reducing working hours in Iran.

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Analysis of 2019 Novel Coronavirus Infection and Clinical Characteristics of Outpatients: An Epidemiological Study from the Fever Clinic in Wuhan, China

Cited by 7 publications

References 0 publications

Analysis and Prediction of COVID-19 Patients’ False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen: A Retrospective Study

Analysis and Prediction of COVID-19 Patients’ False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen: A Retrospective Study

Chest CT findings of coronavirus disease 2019 (COVID-19): A comprehensive meta-analysis of 9907 confirmed patients

Understanding Epidemic Data and Statistics: A Case Study of COVID-19

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