PLoS ONE (Jan 2023)

A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms

  • Nusrat J. Epsi,
  • John H. Powers,
  • David A. Lindholm,
  • Katrin Mende,
  • Allison Malloy,
  • Anuradha Ganesan,
  • Nikhil Huprikar,
  • Tahaniyat Lalani,
  • Alfred Smith,
  • Rupal M. Mody,
  • Milissa U. Jones,
  • Samantha E. Bazan,
  • Rhonda E. Colombo,
  • Christopher J. Colombo,
  • Evan C. Ewers,
  • Derek T. Larson,
  • Catherine M. Berjohn,
  • Carlos J. Maldonado,
  • Paul W. Blair,
  • Josh Chenoweth,
  • David L. Saunders,
  • Jeffrey Livezey,
  • Ryan C. Maves,
  • Margaret Sanchez Edwards,
  • Julia S. Rozman,
  • Mark P. Simons,
  • David R. Tribble,
  • Brian K. Agan,
  • Timothy H. Burgess,
  • Simon D. Pollett,
  • for the EPICC COVID-19 Cohort Study Group

Journal volume & issue
Vol. 18, no. 2

Abstract

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Background Accurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflammatory profiles. Methods 1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative symptom scores (FLU-PRO Plus) were included in this analysis. We employed machine-learning approaches to identify symptom clusters and compared risk of hospitalization, long-term symptoms, as well as peak CRP and IL-6 concentrations. Results We identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 (“Nasal cluster”) is highly correlated with reporting runny/stuffy nose and sneezing, cluster 2 (“Sensory cluster”) is highly correlated with loss of smell or taste, and cluster 3 (“Respiratory/Systemic cluster”) is highly correlated with the respiratory (cough, trouble breathing, among others) and systemic (body aches, chills, among others) domain symptoms. Participants in the Respiratory/Systemic cluster were twice as likely as those in the Nasal cluster to have been hospitalized, and 1.5 times as likely to report that they had not returned-to-activities, which remained significant after controlling for confounding covariates (P Conclusions We identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.