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. 2022 Nov;31(8):1390-1398.
doi: 10.1177/10547738221125632. Epub 2022 Sep 24.

COVID Symptoms, Symptom Clusters, and Predictors for Becoming a Long-Hauler Looking for Clarity in the Haze of the Pandemic

Yong Huang  1 , Melissa D Pinto  2 , Jessica L Borelli  3 , Milad Asgari Mehrabadi  1 , Heather L Abrahim  2 , Nikil Dutt  1 , Natalie Lambert  4 , Erika L Nurmi  5 , Rana Chakraborty  6 , Amir M Rahmani  1   2 , Charles A Downs  7
Affiliations

COVID Symptoms, Symptom Clusters, and Predictors for Becoming a Long-Hauler Looking for Clarity in the Haze of the Pandemic

Yong Huang et al. Clin Nurs Res. 2022 Nov.

Abstract

Post-acute sequelae of SARS-CoV-2 (PASC) is defined as persistent symptoms after apparent recovery from acute COVID-19 infection, also known as COVID-19 long-haul. We performed a retrospective review of electronic health records (EHR) from the University of California COvid Research Data Set (UC CORDS), a de-identified EHR of PCR-confirmed SARS-CoV-2-positive patients in California. The purposes were to (1) describe the prevalence of PASC, (2) describe COVID-19 symptoms and symptom clusters, and (3) identify risk factors for PASC. Data were subjected to non-negative matrix factorization to identify symptom clusters, and a predictive model of PASC was developed. PASC prevalence was 11% (277/2,153), and of these patients, 66% (183/277) were considered asymptomatic at days 0-30. Five PASC symptom clusters emerged and specific symptoms at days 0-30 were associated with PASC. Women were more likely than men to develop PASC, with all age groups and ethnicities represented. PASC is a public health priority.

Keywords: COVID-19; electronic health record; long-COVID; machine learning.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Flowchart depicting how records were screened for inclusion in the study.
Figure 2.
Figure 2.
Symptoms prevalence among SARS-CoV-2 infected community dwellers at days 0–30 and 180+ days. Bar graphs showing prevalence of symptoms reported at days 0–30 and 180+ days. Symptoms with very low prevalence are omitted in this graph.
Figure 3.
Figure 3.
Symptom clusters among SARS-CoV-2-infected community dwellers at days (a) 0–30 and (b) 180+ days. NMF determined symptom clusters depicted in bar graphs with symptom ranking within each cluster, graph demonstrating optimal k means clustering, and graph demonstrating symptom network analysis showing relationship between each reported symptom. Each symptom is denoted as a node, with darker lines connecting symptoms indicating stronger relationships. NMF = non-negative matrix factorization.
Figure 4.
Figure 4.
Key features during days 0–30 and their potential as indicators for developing prolonged COVID-19 symptoms or being a long-hauler. Bar graph showing factors that positively or negatively affect the probability of developing persistent symptoms among COVID+ community dwellers.
Figure 5.
Figure 5.
Presence of key indicators at days 0–30 predict inclusion into specific symptom clusters reported at 180+ days. Heat map demonstrating magnitude of association between key predictors reported at days 0–30 and assignment to a cluster; darker coloring indicates greater positive magnitude of association.
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References

    1. Akaike H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. 10.1109/TAC.1974.1100705 - DOI
    1. Bergquist S. H., Partin C., Roberts D. L., O’Keefe J. B., Tong E. J., Zreloff J., Jarrett T. L., Moore M. A. (2020). Non-hospitalized adults with COVID-19 differ noticeably from hospitalized adults in their demographic, clinical, and social characteristics. SN Comprehensive Clinical Medicine, 2(9), 1349–1357. 10.1007/s42399-020-00453-3 - DOI - PMC - PubMed
    1. Carfì A., Bernabei R., Landi F.Gemelli Against COVID-19 Post-Acute Care Study Group. (2020). Persistent symptoms in patients after acute COVID-19. Jama, 324(6), 603–605. 10.1001/jama.2020.12603 - DOI - PMC - PubMed
    1. Carvalho-Schneider C., Laurent E., Lemaignen A., Beaufils E., Bourbao-Tournois C., Laribi S., Flament T., Ferreira-Maldent N., Bruyère F., Stefic K., Gaudy-Graffin C., Grammatico-Guillon L., Bernard L. (2020). Follow-up of adults with noncritical COVID-19 two months after symptom onset. Clinical Microbiology and Infection : The Official Publication of the European Society of Clinical Microbiology and Infectious Diseases, 27(2), 258–263. 10.1016/j.cmi.2020.09.052 [pii] - DOI - PMC - PubMed
    1. Centers for Disease Control. (2021). COVID-19 laboratory confirmed hospitalizations. https://gis.cdc.gov/grasp/covidnet/COVID19_5.html

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