A Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Prediction Model From Standard Laboratory Tests. (12th August 2020)

• Record Type: Journal Article
• Authors: Bayat, Vafa; Phelps, Steven; Ryono, Russell; Lee, Chong; Parekh, Hemal; Mewton, Joel; Sedghi, Farshid; Etminani, Payam; Holodniy, Mark
• Is Part Of: Clinical infectious diseases. Volume 73:Number 9(2021)
• Page Start: e2901
• Page End: e2907
• Publication Date: 2020-08-12
• Subjects: machine learning -- human coronavirus -- polymerase chain reaction -- viral pneumonia; Communicable diseases -- Periodicals; 616.905
• DOI: 10.1093/cid/ciaa1175
• Languages: English
• ISSNs: 1058-4838

Abstract

Abstract: Background: With the limited availability of testing for the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests. Methods: We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients. Results: In a cohort of 75 991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July 2020, 7335 of whom were positive by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]). Conclusions: Although molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing. Abstract : Using machine learning on a large data set of Veterans Affairs (VA) patients, we explore the possibility of predicting, using standard laboratory tests, whether or not a patient is infected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus.