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

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 and Stuart C Ray

PLOS ONE, 2023, vol. 18, issue 2, 1-14

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

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0278466 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 78466&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0278466

DOI: 10.1371/journal.pone.0278466

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().