Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results With Routine Blood Tests

Thomas Tschoellitsch, MD; Martin Dünser, MD; Carl Böck, MSc; Karin Schwarzbauer, MSc; Jens Meier, MD

Disclosures

Lab Med. 2021;52(2):146-149. 

In This Article

Abstract and Introduction

Abstract

Objective: The diagnosis of COVID-19 is based on the detection of SARS-CoV-2 in respiratory secretions, blood, or stool. Currently, reverse transcription polymerase chain reaction (RT-PCR) is the most commonly used method to test for SARS-CoV-2.

Methods: In this retrospective cohort analysis, we evaluated whether machine learning could exclude SARS-CoV-2 infection using routinely available laboratory values. A Random Forests algorithm with 28 unique features was trained to predict the RT-PCR results.

Results: Out of 12,848 patients undergoing SARS-CoV-2 testing, routine blood tests were simultaneously performed in 1357 patients. The machine learning model could predict SARS-CoV-2 test results with an accuracy of 86% and an area under the receiver operating characteristic curve of 0.74.

Conclusion: Machine learning methods can reliably predict a negative SARS-CoV-2 RT-PCR test result using standard blood tests.

Introduction

The diagnosis of COVID-19 is based on the detection of SARS-CoV-2 in respiratory secretions, blood, or stool.[1,2] Currently, reverse-transcription polymerase chain reaction (RT-PCR) is the most commonly used method to test for SARS-CoV-2.[3] Key limitations of this technique are its restricted availability and time requirement, often leaving clinicians unaware of the patient's virus status for 12 hours or longer.

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