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

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.

Early introductions and community transmission of SARS-CoV-2 variant B.1.1.7 in the United States (preprint)

The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2500 COVID-19 cases associated with this variant have been detected in the US since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.

SARS-CoV-2 Viral Load Predicts Mortality in Patients with and Without Cancer Who are Hospitalized with Coronavirus Disease 2019 (preprint)

Patients with cancer may be at increased risk of severe coronavirus disease 2019 (COVID-19), but the role of viral load on this risk is unknown. We measured SARS-CoV-2 viral load using cycle threshold (CT) values from reverse transcription-polymerase chain reaction assays applied to nasopharyngeal swab specimens in 100 patients with cancer and 2914 without cancer admitted to three New York City hospitals. Overall, the in-hospital mortality rate was 39.5% among patients with a high viral load (CT<25), 25.6% among patients with a medium viral load (CT 25-30), and 15.7% among patients with a low viral load (CT>30; P<0.001). Similar findings were observed in patients with cancer (high, 45.0% mortality; medium, 29.2%; low, 13.9%; P=0.003). Patients with hematologic malignancies had higher median viral loads (CT=25.0) than patients without cancer (CT=29.2; P=0.0039). SARS-CoV-2 viral load results may offer vital prognostic information for patients with and without cancer who are hospitalied with COVID-19.Funding: This work was partially supported by the National Centerfor Advancing Translational Science [UL1 TR002384 to Julianne Imperato-McGinley] at the National Institutes of Health.Conflict of Interest: L.F.W. reports receiving consulting fees from Roche Molecular Systems, Inc. M.M.S. receives grant support from Amgen, Inc. All other authors report no potential conflicts of interest.Ethical Approval Statement: The study was approved by the Institutional Review Board (#20-03021681) at Weill Cornell Medicine with a waiver of informed consent