Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning

Abstract Background Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, how...

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Bibliographic Details
Published in:	Clinical Chemistry 2020-11, Vol.66 (11), p.1396-1404
Main Authors:	Yang, He S, Hou, Yu, Vasovic, Ljiljana V, Steel, Peter A D, Chadburn, Amy, Racine-Brzostek, Sabrina E, Velu, Priya, Cushing, Melissa M, Loda, Massimo, Kaushal, Rainu, Zhao, Zhen, Wang, Fei
Format:	Article
Language:	eng
Subjects:	Adult Aged Blood Blood tests Clinical Laboratory Techniques Control Coronavirus Infections - diagnosis COVID-19 COVID-19 Testing Decision trees Epidemics Female Health aspects Health risks Hematologic Tests Humans Infections Laboratories Laboratory tests Learning algorithms Machine Learning Male Medical examination Medical screening Methods Middle Aged Models, Theoretical Pandemics Patients Pneumonia, Viral - diagnosis Polymerase chain reaction Retrospective Studies Reverse Transcriptase Polymerase Chain Reaction ROC Curve Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 Tat protein United States Viral diseases Young Adult
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Summary:	Abstract Background Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. Method We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual’s SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. Results The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. Conclusion This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.
ISSN:	0009-9147 1530-8561