{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T11:07:15Z","timestamp":1792062435437,"version":"4.4.0"},"reference-count":51,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"funder":[{"name":"Defense Health Program","award":["HU00012020067"],"award-info":[{"award-number":["HU00012020067"]}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["HU00011920111"],"award-info":[{"award-number":["HU00011920111"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["Y1-AI-5072"],"award-info":[{"award-number":["Y1-AI-5072"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 (\u201cNasal cluster\u201d) is highly correlated with reporting runny\/stuffy nose and sneezing, cluster 2 (\u201cSensory cluster\u201d) is highly correlated with loss of smell or taste, and cluster 3 (\u201cRespiratory\/Systemic cluster\u201d) 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 (\n                      <jats:italic>P<\/jats:italic>\n                      &lt; 0.01). Respiratory\/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.03). We observed higher peak CRP and IL-6 in the Respiratory\/Systemic cluster (\n                      <jats:italic>P<\/jats:italic>\n                      &lt; 0.01).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0281272","type":"journal-article","created":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T13:28:23Z","timestamp":1675949303000},"page":"e0281272","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":19,"title":["A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5363-075X","authenticated-orcid":true,"given":"Nusrat J.","family":"Epsi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John 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