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A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms.
Epsi, Nusrat J; Powers, John H; Lindholm, David A; Mende, Katrin; Malloy, Allison; Ganesan, Anuradha; Huprikar, Nikhil; Lalani, Tahaniyat; Smith, Alfred; Mody, Rupal M; Jones, Milissa U; Bazan, Samantha E; Colombo, Rhonda E; Colombo, Christopher J; Ewers, Evan C; Larson, Derek T; Berjohn, Catherine M; Maldonado, Carlos J; Blair, Paul W; Chenoweth, Josh; Saunders, David L; Livezey, Jeffrey; Maves, Ryan C; Sanchez Edwards, Margaret; Rozman, Julia S; Simons, Mark P; Tribble, David R; Agan, Brian K; Burgess, Timothy H; Pollett, Simon D.
  • Epsi NJ; Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America.
  • Powers JH; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, United States of America.
  • Lindholm DA; Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of America.
  • Mende K; Molecular Biology Laboratory, Brooke Army Medical Center, San Antonio, Texas, United States of America.
  • Malloy A; Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America.
  • Ganesan A; Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America.
  • Huprikar N; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, United States of America.
  • Lalani T; Molecular Biology Laboratory, Brooke Army Medical Center, San Antonio, Texas, United States of America.
  • Smith A; Department of Pediatrics, Walter Reed National Military Medical Center, Bethesda, Maryland, United States of America.
  • Mody RM; Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America.
  • Jones MU; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, United States of America.
  • Bazan SE; Infectious Disease Clinic, Walter Reed National Military Medical Center, Bethesda, Maryland, United States of America.
  • Colombo RE; Infectious Disease Clinic, Walter Reed National Military Medical Center, Bethesda, Maryland, United States of America.
  • Colombo CJ; Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America.
  • Ewers EC; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, United States of America.
  • Larson DT; Infectious Disease Clinical Research Program, Naval Medical Center Portsmouth, Portsmouth, Virginia, United States of America.
  • Berjohn CM; Infectious Disease Clinical Research Program, Naval Medical Center Portsmouth, Portsmouth, Virginia, United States of America.
  • Maldonado CJ; Infectious Disease Clinic, William Beaumont Army Medical Center, El Paso, Texas, United States of America.
  • Blair PW; Pediatric Infectious Diseases, Tripler Army Medical Center, Honolulu, Hawaii, United States of America.
  • Chenoweth J; Family Nurse Practitioner and Women's Health Nurse Practitioner Program, Carl R. Darnall Army Medical Center, Fort Hood, Texas, United States of America.
  • Saunders DL; Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America.
  • Livezey J; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, United States of America.
  • Maves RC; Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America.
  • Sanchez Edwards M; Infectious Disease Clinic, Madigan Army Medical Center, Tacoma, Washington, United States of America.
  • Rozman JS; Infectious Disease Clinic, Madigan Army Medical Center, Tacoma, Washington, United States of America.
  • Simons MP; Internal Medicine, Fort Belvoir Community Hospital, Fort Belvoir, Virginia, United States of America.
  • Tribble DR; Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America.
  • Agan BK; Internal Medicine, Fort Belvoir Community Hospital, Fort Belvoir, Virginia, United States of America.
  • Burgess TH; Infectious Disease Clinic, Naval Medical Center San Diego, San Diego, California, United States of America.
  • Pollett SD; Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America.
PLoS One ; 18(2): e0281272, 2023.
Article in English | MEDLINE | ID: covidwho-2229770
ABSTRACT

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 < 0.01). Respiratory/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset (P = 0.03). We observed higher peak CRP and IL-6 in the Respiratory/Systemic cluster (P < 0.01).

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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2023 Document Type: Article Affiliation country: Journal.pone.0281272

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2023 Document Type: Article Affiliation country: Journal.pone.0281272