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PLoS One ; 18(2): e0281272, 2023.
Article in English | MEDLINE | ID: covidwho-2229770


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.

COVID-19 , Humans , COVID-19/epidemiology , SARS-CoV-2 , Post-Acute COVID-19 Syndrome , Interleukin-6 , Phenotype , Hospitalization , Machine Learning
JAMA Netw Open ; 6(1): e2251360, 2023 01 03.
Article in English | MEDLINE | ID: covidwho-2172252


Importance: Understanding the factors associated with post-COVID conditions is important for prevention. Objective: To identify characteristics associated with persistent post-COVID-19 symptoms and to describe post-COVID-19 medical encounters. Design, Setting, and Participants: This cohort study used data from the Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases With Pandemic Potential (EPICC) study implemented in the US military health system (MHS); MHS beneficiaries aged 18 years or older who tested positive for SARS-CoV-2 from February 28, 2020, through December 31, 2021, were analyzed, with 1-year follow-up. Exposures: SARS-CoV-2 infection. Main Outcomes and Measures: The outcomes analyzed included survey-reported symptoms through 6 months after SARS-CoV-2 infection and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis categories reported in medical records 6 months following SARS-CoV-2 infection vs 3 months before infection. Results: More than half of the 1832 participants in these analyses were aged 18 to 44 years (1226 [66.9%]; mean [SD] age, 40.5 [13.7] years), were male (1118 [61.0%]), were unvaccinated at the time of their infection (1413 [77.1%]), and had no comorbidities (1290 [70.4%]). A total of 728 participants (39.7%) had illness that lasted 28 days or longer (28-89 days: 364 [19.9%]; ≥90 days: 364 [19.9%]). Participants who were unvaccinated prior to infection (risk ratio [RR], 1.39; 95% CI, 1.04-1.85), reported moderate (RR, 1.80; 95% CI, 1.47-2.22) or severe (RR, 2.25; 95% CI, 1.80-2.81) initial illnesses, had more hospitalized days (RR per each day of hospitalization, 1.02; 95% CI, 1.00-1.03), and had a Charlson Comorbidity Index score of 5 or greater (RR, 1.55; 95% CI, 1.01-2.37) were more likely to report 28 or more days of symptoms. Among unvaccinated participants, postinfection vaccination was associated with a 41% lower risk of reporting symptoms at 6 months (RR, 0.59; 95% CI, 0.40-0.89). Participants had higher risk of pulmonary (RR, 2.00; 95% CI, 1.40-2.84), diabetes (RR, 1.46; 95% CI, 1.00-2.13), neurological (RR, 1.29; 95% CI, 1.02-1.64), and mental health-related medical encounters (RR, 1.28; 95% CI, 1.01-1.62) at 6 months after symptom onset than at baseline (before SARS-CoV-2 infection). Conclusions and Relevance: In this cohort study, more severe acute illness, a higher Charlson Comorbidity Index score, and being unvaccinated were associated with a higher risk of reporting COVID-19 symptoms lasting 28 days or more. Participants with COVID-19 were more likely to seek medical care for diabetes, pulmonary, neurological, and mental health-related illness for at least 6 months after onset compared with their pre-COVID baseline health care use patterns. These findings may inform the risk-benefit ratio of COVID-19 vaccination policy.

COVID-19 , Humans , Male , Adult , Female , COVID-19/epidemiology , COVID-19/prevention & control , SARS-CoV-2 , COVID-19 Vaccines , Cohort Studies , Post-Acute COVID-19 Syndrome
Cell Host Microbe ; 2022 Oct 21.
Article in English | MEDLINE | ID: covidwho-2118002


The rapid emergence of SARS-CoV-2 variants challenges vaccination strategies. Here, we collected 201 serum samples from persons with a single infection or multiple vaccine exposures, or both. We measured their neutralization titers against 15 natural variants and 7 variants with engineered spike mutations and analyzed antigenic diversity. Antigenic maps of primary infection sera showed that Omicron sublineages BA.2, BA.4/BA.5, and BA.2.12.1 are distinct from BA.1 and more similar to Beta/Gamma/Mu variants. Three mRNA COVID-19 vaccinations increased neutralization of BA.1 more than BA.4/BA.5 or BA.2.12.1. BA.1 post-vaccination infection elicited higher neutralization titers to all variants than three vaccinations alone, although with less neutralization to BA.2.12.1 and BA.4/BA.5. Those with BA.1 infection after two or three vaccinations had similar neutralization titer magnitude and antigenic recognition. Accounting for antigenic differences among variants when interpreting neutralization titers can aid the understanding of complex patterns in humoral immunity that informs the selection of future COVID-19 vaccine strains.