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2.
ssrn; 2020.
Preprint in English | PREPRINT-SSRN | ID: ppzbmed-10.2139.ssrn.3695178

ABSTRACT

Background: Considering the current situation of the novel coronavirus disease (COVID-19) epidemic control, it is highly likely that COVID-19 and influenza may coincide during the approaching winter season. However, there is no available tool that can rapidly and precisely distinguish between these two diseases in the absence of laboratory evidence of specific pathogens. Methods: Laboratory confirmed COVID-19 and influenza patients from Zhongnan Hospital of Wuhan University (ZHWU) and Wuhan No.1 Hospital (WNH) between December 1, 2019 and February 29, 2020, were included for analysis. A machine learning-based decision model was developed using the XGBoost algorithms. The specificity, sensitivity, positive and negative predictive values (PPV/NPV), accuracy and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the model performance. Findings: The data from 357 COVID-19 and 1893 influenza patients from ZHWU were divided into a training and a testing set in the ratio 7:3. The external test used the data of 308 COVID-19 and 312 influenza patients from WNH. In the training and testing sets, the model achieved good performance in identifying COVID-19 from influenza with an accuracy of 0.968 (AUC, 0.943 (95%CI 0.925, 0.962)) and 0.960 (AUC, 0.928 (95%CI 0.897, 0.959)), respectively. Our decision tree suggested that older age (>16 years), higher hsCRP (>14.2 mg/L) and lower monocyte (≤0.68×109/L) drive the prediction towards COVID-19. In addition, the external test determined a COVID-19 prediction accuracy of 0.839 (AUC, 0.839 (95%CI: 0.811, 0.868). Interpretation: Machine learning provides a tool that can rapidly and accurately distinguish between COVID-19 and influenza cases. This finding would be particularly useful in regions having massive COVID-19 and influenza cases while limited resources for laboratory test of specific pathogens. Funding: National Natural Science Foundation of China (81900097) and the Emergency Response Project of Hubei Science and Technology Department (2020FCA002, 2020FCA023).Declaration of Interests: None reported.Ethics Approval Statement: This study was approved by the Medical Ethics Committee, Zhongnan Hospital of Wuhan University (Clinical Ethical Approval No. 2020020).


Subject(s)
COVID-19
3.
researchsquare; 2020.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-29357.v1

ABSTRACT

Understanding the epidemiological and clinical characteristics of fatal cases infected with SARS-CoV-2 is import to develop appropriate preventable intervention programs in hospitals. Demographic data, clinical symptoms, clinical course, co-morbidities, laboratory findings, CT scans, treatments and complications of 162 fatal cases were retrieved from electric medical records in 5 hospitals of Wuhan, China. The median age was 69.5 years old (IQR: 63.0-77.25; range: 29-96). 112 (69.1%) cases were men. Hypertension (45.1%) was the most common co-morbidity, but 59 (36.4%) cases had no co-morbidity. At admission, 131 (81.9%) cases were assessed as severe or critical. However, 39 (18.1%) were assessed as moderate. Moderate cases had a higher prevalence of hypertension and chronic lung disease comparing with severe or critical cases (P<0.05, respectively). 126 (77.8%) and 132 (81.5%) cases received antiviral treatment and glucocorticoids, respectively. 116 (71.6%) cases were admitted to ICU and 137 (85.1%) cases received mechanical ventilation. Respiratory failure or acute respiratory distress syndrome (93.2%) was the most common complication. The young cases of COVID-19, without co-morbidity and in a moderate condition at admission could develop fatal outcome. We need to be more cautious in case management of COVID-19 for preventing the fatal outcomes.


Subject(s)
Lung Diseases , Respiratory Distress Syndrome , Hypertension , COVID-19 , Respiratory Insufficiency
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