A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results

Rohan P. Joshi, Vikas Pejaver, Noah E. Hammarlund, Heungsup Sung, Seong Kyu Lee, Al'ona Furmanchuk, Hye Young Lee, Gregory Scott, Saurabh Gombar, Nigam Shah, Sam Shen, Anna Nassiri, Daniel Schneider, Faraz S. Ahmad, David Liebovitz, Abel Kho, Sean Mooney, Benjamin A. Pinsky, Niaz Banaei*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Background: Testing for COVID-19 remains limited in the United States and across the world. Poor allocation of limited testing resources leads to misutilization of health system resources, which complementary rapid testing tools could ameliorate. Objective: To predict SARS-CoV-2 PCR positivity based on complete blood count components and patient sex. Study design: A retrospective case-control design for collection of data and a logistic regression prediction model was used. Participants were emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing. 33 confirmed SARS-CoV-2 PCR positive and 357 negative patients at Stanford Health Care were used for model training. Validation cohorts consisted of emergency department patients > 18 years old who had concurrent complete blood counts and SARS-CoV-2 PCR testing in Northern California (41 PCR positive, 495 PCR negative), Seattle, Washington (40 PCR positive, 306 PCR negative), Chicago, Illinois (245 PCR positive, 1015 PCR negative), and South Korea (9 PCR positive, 236 PCR negative). Results: A decision support tool that utilizes components of complete blood count and patient sex for prediction of SARS-CoV-2 PCR positivity demonstrated a C-statistic of 78 %, an optimized sensitivity of 93 %, and generalizability to other emergency department populations. By restricting PCR testing to predicted positive patients in a hypothetical scenario of 1000 patients requiring testing but testing resources limited to 60 % of patients, this tool would allow a 33 % increase in properly allocated resources. Conclusions: A prediction tool based on complete blood count results can better allocate SARS-CoV-2 testing and other health care resources such as personal protective equipment during a pandemic surge.

Original languageEnglish (US)
Article number104502
JournalJournal of Clinical Virology
Volume129
DOIs
StatePublished - Aug 2020

Keywords

  • COVID-19
  • Machine learning
  • Prediction tool
  • Rapid testing
  • SARS-CoV-2

ASJC Scopus subject areas

  • Virology
  • Infectious Diseases

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