Evidence map›Paper›PMID 36812214›Full record

ArticlePloS one2023

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 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Kai-Wen K YangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.ORCID 0000-0002-3915-4280
Chloé F ParisDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
Kevin T GormanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
Ilia RattsevDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
Rebecca H YooDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.ORCID 0000-0001-8161-8934
Yijia ChenDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
Jacob M DesmanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
Tony Y WeiDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.ORCID 0000-0001-6849-5650
Joseph L GreensteinDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
Casey Overby TaylorDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
Stuart C RayDepartment of Medicine, Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, United States of America.ORCID 0000-0002-1051-7260

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 < 0.001) for the testing set. We hope to validate the features found to be associated with resistance/non-resistance through more advanced association studies.

Indexed as

COVID-19Electronic Health RecordsHumansMachine LearningRetrospective StudiesSARS-CoV-2

Identifiers

PMID36812214
PMCPMC9946212

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.