Prediction of Long COVID Based on Severity of Initial COVID-19 Infection: Differences in predictive feature sets between hospitalized versus non-hospitalized index infections
Socia et al.,
Prediction of Long COVID Based on Severity of Initial COVID-19 Infection: Differences in predictive feature..,
medRxiv, doi:10.1101/2023.01.16.23284634 (Preprint)
N3C retrospective identifying plant hardiness zone as a predictive variable for long COVID. Authors note that this may be due to sunlight/climate affecting the risk of long COVID, and plan more detailed analysis in future work.
Socia et al., 18 Jan 2023, preprint, 5 authors.
Contact:
robert.cockrell@med.uvm.edu.
Abstract: medRxiv preprint doi: https://doi.org/10.1101/2023.01.16.23284634; this version posted January 20, 2023. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
Prediction of Long COVID Based on Severity of Initial COVID-19 Infection:
Differences in predictive feature sets between hospitalized versus non-hospitalized
index infections
______________________________________________________________________________
Damien Socia1, Dale Larie1, Sol Feuerwerker1, Gary An1 and Chase Cockrell1*
1. Department of Surgery, University of Vermont Larner College of Medicine
Keywords: COVID, Long COVID, Machine Learning, SARS-COV2, Plant Hardiness Zones
Abstract:
Long COVID is recognized as a significant consequence of SARS-COV2 infection. While the
pathogenesis of Long COVID is still a subject of extensive investigation, there is considerable
potential benefit in being able to predict which patients will develop Long COVID. We
hypothesize that there would be distinct differences in the prediction of Long COVID based on
the severity of the index infection, and use whether the index infection required hospitalization or
not as a proxy for developing predictive models. We divide a large population of COVID patients
drawn from the United States National Institutes of Health (NIH) National COVID Cohort
Collaborative (N3C) Data Enclave Repository into two cohorts based on the severity of their initial
COVID-19 illness and correspondingly trained two machine learning models: the Long COVID
after Severe Disease Model (LCaSDM) and the Long COVID after Mild Disease Model
(LCaMDM). The resulting models performed well on internal validation/testing, with a F1 score
of 0.94 for the LCaSDM and 0.82 for the LCaMDM. There were distinct differences in the top 10
features used by each model, possibly reflecting the differences in type and amount of
pathophysiological data between the hospitalized and non-hospitalized patients and/or reflecting
different pathophysiological trajectories in the development of Long COVID. Of particular interest
was the importance of Plant Hardiness Zone in the feature set for the LCaMDM, which may point
to a role of climate and/or sunlight in the progression to Long COVID. Future work will involve a
more detailed investigation of the potential role of climate and sunlight, as well as refinement of
the predictive models as Long COVID becomes increasingly parsed into distinct clinical
phenotypes.
*To Whom Correspondence should be addressed:
Robert.cockrell@med.uvm.edu
1
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
medRxiv preprint doi: https://doi.org/10.1101/2023.01.16.23284634; this version posted January 20, 2023. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
1.0 Introduction:
The development of long COVID carries significant morbidity for patients and a large financial
burden to health systems. The diagnostic criteria for long COVID are quite broad; the diagnosis
can incorporate numerous organ systems and the severity can range from mild to debilitating. This
makes..
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