A Machine Learning Model Incorporating Laboratory Blood Tests Discriminates Between SARS-CoV-2 and Influenza Infections at Emergency Department Visit (preprint)

ABSTRACT

Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus are contagious respiratory pathogens with similar symptoms but require different treatment and management strategies. This study investigated whether laboratory blood tests can discriminate between SARS-CoV-2 and influenza infections at emergency department (ED) presentation. Methods 723 influenza A/B positive (2018/1/1 to 2020/3/15) and 1,281 SARS-CoV-2 positive (2020/3/11 to 2020/6/30) ED patients were retrospectively analyzed. Laboratory test results completed within 48 hours prior to reporting of virus RT-PCR results, as well as patient demographics were included to train and validate a random forest (RF) model. The dataset was randomly divided into training (2/3) and testing (1/3) sets with the same SARS-CoV-2/influenza ratio. The Shapley Additive Explanations technique was employed to visualize the impact of each laboratory test on the differentiation. Results The RF model incorporating results from 15 laboratory tests and demographic characteristics discriminated SARS-CoV-2 and influenza infections, with an area under the ROC curve value 0.90 in the independent testing set. The overall agreement with the RT-PCR results was 83% (95% CI 80-86%). The test with the greatest impact on the differentiation was serum total calcium level. Further, the model achieved an AUC of 0.82 in a new dataset including 519 SARS-CoV-2 ED patients (2020/12/1 to 2021/2/28) and the previous 723 influenza positive patients. Serum calcium level remained the most impactful feature on the differentiation. Conclusion We identified characteristic laboratory test profiles differentiating SARS-CoV-2 and influenza infections, which may be useful for the preparedness of overlapping COVID-19 resurgence and future seasonal influenza.