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Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning

Author

Listed:
  • Kai-Wen K Yang
  • Chloé F Paris
  • Kevin T Gorman
  • Ilia Rattsev
  • Rebecca H Yoo
  • Yijia Chen
  • Jacob M Desman
  • Tony Y Wei
  • Joseph L Greenstein
  • Casey Overby Taylor
  • Stuart C Ray

Abstract

There have been over 621 million cases of COVID-19 worldwide with over 6.5 million deaths. Despite the high secondary attack rate of COVID-19 in shared households, some exposed individuals do not contract the virus. In addition, little is known about whether the occurrence of COVID-19 resistance differs among people by health characteristics as stored in the electronic health records (EHR). In this retrospective analysis, we develop a statistical model to predict COVID-19 resistance in 8,536 individuals with prior COVID-19 exposure using demographics, diagnostic codes, outpatient medication orders, and count of Elixhauser comorbidities in EHR data from the COVID-19 Precision Medicine Platform Registry. Cluster analyses identified 5 patterns of diagnostic codes that distinguished resistant from non-resistant patients in our study population. In addition, our models showed modest performance in predicting COVID-19 resistance (best performing model AUROC = 0.61). Monte Carlo simulations conducted indicated that the AUROC results are statistically significant (p

Suggested Citation

  • Kai-Wen K Yang & Chloé F Paris & Kevin T Gorman & Ilia Rattsev & Rebecca H Yoo & Yijia Chen & Jacob M Desman & Tony Y Wei & Joseph L Greenstein & Casey Overby Taylor & Stuart C Ray, 2023. "Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-14, February.
    • Handle: RePEc:plo:pone00:0278466
    DOI: 10.1371/journal.pone.0278466
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