https://orcid.org/0000-0002-0612-0658
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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 between December 1, 2019 and February 29, 2020, from Zhongnan Hospital of Wuhan University (ZHWU) and Wuhan No.1 Hospital (WNH) located in Wuhan, China, were included for analysis. A machine learning-based decision model was developed using the XGBoost algorithms.
Results
Data of 357 COVID-19 and 1893 influenza patients from ZHWU were split into a training and a testing set in the ratio 7:3, while the dataset from WNH (308 COVID-19 and 312 influenza patients) was preserved for an external test. Model-based decision tree selected age, serum high-sensitivity C-reactive protein and circulating monocytes as meaningful indicators for classifying COVID-19 and influenza cases. In the training, testing and external sets, the model achieved good performance in identifying COVID-19 from influenza cases with a corresponding area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI 0.93, 0.96), 0.93 (95% CI 0.90, 0.96), and 0.84 (95% CI: 0.81, 0.87), respectively.
Conclusion
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 with massive co-occurrences of COVID-19 and influenza cases while limited resources for laboratory testing of specific pathogens.
Abbreviation
AUC, area under the receiver operating characteristic curve; COVID-19, the novel coronavirus disease 2019; hsCRP, high-sensitivity C-reactive protein; NPV, negative predictive value; PPV, positive predictive value; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; WNH, Wuhan No.1 Hospital; ZHWU, Zhongnan Hospital of Wuhan University.
Data Sharing Statement
Additional information is available on request from the corresponding author ([email protected]).
Ethic Approval
This study was performed in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee, Zhongnan Hospital of Wuhan University (Clinical Ethical Approval No. 2,020,020). The ethics committee waived written informed consent because of the urgent need of data collection on COVID-19. All the data were deidentified to protect patient privacy.
Acknowledgments
The authors would like to thank Dr. Gilles Hejblum (Sorbonne Université, INSERM, Institut Pierre Louis d′Épidémiologie et de Santé Publique, F75012, Paris, France. Email: [email protected]) for his critical comments and review of our manuscript. We also thank Drs. Chengwei Li and Fangjian Yuan for their assistance in data extraction from electric medical record system and Drs. Jie Mao and Yumei Yang for their assistance in model development. In addition, the authors would like to thank Drs. Weijia Xing, Guoyong Ding, Legao Chen, Jun Zhang, Cheng Jiang, Haoli Ma and Zhigang Zhao for their kind assisstance in manuscript preparation.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. Xianlong Zhou and Zhichao Wang should be considered as co-first authors.
Disclosure
The authors report no conflicts of interest related to this work.