Symptoms Predicting SARS-CoV-2 Test Results in Resident Physicians and Fellows in New York City

Tania P. Chen, Meizhen Yao, Vishal Midya, Betty Kolod, Rabeea F. Khan, Adeyemi Oduwole, Bernard Camins, I. Michael Leitman, Ismail Nabeel, Kristin Oliver, Damaskini Valvi

Research output: Contribution to journalArticlepeer-review

Abstract

Accurate prediction of SARS-CoV-2 infection based on symptoms can be a cost-efficient tool for remote screening in healthcare settings with limited SARS-CoV-2 testing capacity. We used a machine learning approach to determine self-reported symptoms that best predict a positive SARS-CoV-2 test result in physician trainees from a large healthcare system in New York. We used survey data on symptoms history and SARS-CoV-2 testing results collected retrospectively from 328 physician trainees in the Mount Sinai Health System, over the period 1 February 2020 to 31 July 2020. Prospective data on symptoms reported prior to SARS-CoV-2 test results were available from the employee health service COVID-19 registry for 186 trainees and analyzed to confirm absence of recall bias. We estimated the associations between symptoms and IgG antibody and/or reverse transcriptase polymerase chain reaction test results using Bayesian generalized linear mixed effect regression models adjusted for confounders. We identified symptoms predicting a positive SARS-CoV-2 test result using extreme gradient boosting (XGBoost). Cough, chills, fever, fatigue, myalgia, headache, shortness of breath, diarrhea, nausea/vomiting, loss of smell, loss of taste, malaise and runny nose were associated with a positive SARS-CoV-2 test result. Loss of taste, myalgia, loss of smell, cough and fever were identified as key predictors for a positive SARS-CoV-2 test result in the XGBoost model. Inclusion of sociodemographic and occupational risk factors in the model improved prediction only slightly (from AUC = 0.822 to AUC = 0.838). Loss of taste, myalgia, loss of smell, cough and fever are key predictors for symptom-based screening of SARS-CoV-2 infection in healthcare settings with remote screening and/or limited testing capacity.

Original languageEnglish
Pages (from-to)671-681
Number of pages11
JournalCOVID
Volume3
Issue number5
DOIs
StatePublished - May 2023

Keywords

  • COVID-19
  • SARS-CoV-2
  • healthcare workers
  • medical residents
  • physician trainees
  • screening

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