Abstract
There is substantial interest in using presenting symptoms to prioritize testing for COVID-19 and establish symptom-based surveillance. However, little is currently known about the specificity of COVID-19 symptoms. To assess the feasibility of symptom-based screening for COVID-19, we used data from tests for common respiratory viruses and SARS-CoV-2 in our health system to measure the ability to correctly classify virus test results based on presenting symptoms. Based on these results, symptom-based screening may not be an effective strategy to identify individuals who should be tested for SARS-CoV-2 infection or to obtain a leading indicator of new COVID-19 cases.
Highlights
There is substantial interest in developing symptom-based screening to prioritize who should be tested for SARS-CoV-2 infection and to establish symptom-based surveillance to provide an early indicator of new COVID-19 cases[1,2,3]
To assess the feasibility of using symptom-based screening to assign a probability of SARS-CoV-2 infection, we first quantified the ability to correctly predict results of tests for common respiratory viruses observed to frequently co-infect patients positive for SARS-CoV-2 at Stanford Health Care[5], using symptoms mentioned in clinical notes at the time of the test order
SARSCoV-2 and the remaining common respiratory viruses were not highly predictable, with average area under the receiver operator curve (AUROC) below 0.70. These results suggest that, for both SARS-CoV-2 and other commonly diagnosed respiratory viral infections, the presenting symptoms at the time of the test order may not provide sufficient information to correctly classify whether a given patient will test positive for that virus
Summary
There is substantial interest in developing symptom-based screening to prioritize who should be tested for SARS-CoV-2 infection and to establish symptom-based surveillance to provide an early indicator of new COVID-19 cases[1,2,3]. It is crucial to determine whether symptom-based screening to prioritize testing is feasible. To assess the feasibility of using symptom-based screening to assign a probability of SARS-CoV-2 infection, we first quantified the ability to correctly predict results of tests for common respiratory viruses observed to frequently co-infect patients positive for SARS-CoV-2 at Stanford Health Care[5], using symptoms mentioned in clinical notes at the time of the test order. After establishing a baseline for the performance of machine learning models to correctly classify common respiratory virus infections[6], we trained a similar model for SARS-CoV-2 test results[7]
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