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

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.

Understanding 'hybrid immunity': comparison and predictors of humoral immune responses to SARS-CoV-2 infection and COVID-19 vaccines.

BACKGROUND: Comparing humoral responses in SARS-CoV-2 vaccinees, those with SARS-CoV-2 infection, or combinations of vaccine/infection ('hybrid immunity'), may clarify predictors of vaccine immunogenicity. METHODS: We studied 2660 U.S. Military Health System beneficiaries with a history of SARS-CoV-2 infection-alone (n = 705), vaccination-alone (n = 932), vaccine-after-infection (n = 869), and vaccine-breakthrough-infection (n = 154). Peak anti-spike-IgG responses through 183 days were compared, with adjustment for vaccine product, demography, and comorbidities. We excluded those with evidence of clinical or sub-clinical SARS-CoV-2 reinfection from all groups. RESULTS: Multivariable regression results indicated vaccine-after-infection anti-spike-IgG responses were higher than infection-alone (p < 0.01), regardless of prior infection severity. An increased time between infection and vaccination was associated with a greater post-vaccination IgG response (p < 0.01). Vaccination-alone elicited a greater IgG response, but more rapid waning of IgG (p < 0.01), compared to infection-alone (p < 0.01). BNT162b2 and mRNA-1273 vaccine-receipt was associated with greater IgG responses compared to JNJ-78436735 (p < 0.01), regardless of infection history. Those with vaccine-after-infection or vaccine-breakthrough-infection had a more durable anti-spike-IgG response compared to infection-alone (p < 0.01). CONCLUSIONS: Vaccine-receipt elicited higher anti-spike-IgG responses than infection-alone, although IgG levels waned faster in those vaccinated (compared to infection-alone). Vaccine-after-infection elicits a greater humoral response compared to vaccine or infection alone; and the timing, but not disease severity, of prior infection predicted these post-vaccination IgG responses. While differences between groups were small in magnitude, these results offer insights into vaccine immunogenicity variations that may help inform vaccination timing strategies.

Distinct blood inflammatory biomarker clusters stratify host phenotypes during the middle phase of COVID-19.

The associations between clinical phenotypes of coronavirus disease 2019 (COVID-19) and the host inflammatory response during the transition from peak illness to convalescence are not yet well understood. Blood plasma samples were collected from 129 adult SARS-CoV-2 positive inpatient and outpatient participants between April 2020 and January 2021, in a multi-center prospective cohort study at 8 military hospitals across the United States. Plasma inflammatory protein biomarkers were measured in samples from 15 to 28 days post symptom onset. Topological Data Analysis (TDA) was used to identify patterns of inflammation, and associations with peak severity (outpatient, hospitalized, ICU admission or death), Charlson Comorbidity Index (CCI), and body mass index (BMI) were evaluated using logistic regression. The study population (n = 129, 33.3% female, median 41.3 years of age) included 77 outpatient, 31 inpatient, 16 ICU-level, and 5 fatal cases. Three distinct inflammatory biomarker clusters were identified and were associated with significant differences in peak disease severity (p < 0.001), age (p < 0.001), BMI (p < 0.001), and CCI (p = 0.001). Host-biomarker profiles stratified a heterogeneous population of COVID-19 patients during the transition from peak illness to convalescence, and these distinct inflammatory patterns were associated with comorbid disease and severe illness due to COVID-19.

Persistent COVID-19 Symptoms at 6 Months After Onset and the Role of Vaccination Before or After SARS-CoV-2 Infection.

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.

Temporal Improvements in COVID-19 Outcomes for Hospitalized Adults: A Post Hoc Observational Study of Remdesivir Group Participants in the Adaptive COVID-19 Treatment Trial.

BACKGROUND: The COVID-19 standard of care (SOC) evolved rapidly during 2020 and 2021, but its cumulative effect over time is unclear. OBJECTIVE: To evaluate whether recovery and mortality improved as SOC evolved, using data from ACTT (Adaptive COVID-19 Treatment Trial). DESIGN: ACTT is a series of phase 3, randomized, double-blind, placebo-controlled trials that evaluated COVID-19 therapeutics from February 2020 through May 2021. ACTT-1 compared remdesivir plus SOC to placebo plus SOC, and in ACTT-2 and ACTT-3, remdesivir plus SOC was the control group. This post hoc analysis compared recovery and mortality between these comparable sequential cohorts of patients who received remdesivir plus SOC, adjusting for baseline characteristics with propensity score weighting. The analysis was repeated for participants in ACTT-3 and ACTT-4 who received remdesivir plus dexamethasone plus SOC. Trends in SOC that could explain outcome improvements were analyzed. (ClinicalTrials.gov: NCT04280705 [ACTT-1], NCT04401579 [ACTT-2], NCT04492475 [ACTT-3], and NCT04640168 [ACTT-4]). SETTING: 94 hospitals in 10 countries (86% U.S. participants). PARTICIPANTS: Adults hospitalized with COVID-19. INTERVENTION: SOC. MEASUREMENTS: 28-day mortality and recovery. RESULTS: Although outcomes were better in ACTT-2 than in ACTT-1, adjusted hazard ratios (HRs) were close to 1 (HR for recovery, 1.04 [95% CI, 0.92 to 1.17]; HR for mortality, 0.90 [CI, 0.56 to 1.40]). Comparable patients were less likely to be intubated in ACTT-2 than in ACTT-1 (odds ratio, 0.75 [CI, 0.53 to 0.97]), and hydroxychloroquine use decreased. Outcomes improved from ACTT-2 to ACTT-3 (HR for recovery, 1.43 [CI, 1.24 to 1.64]; HR for mortality, 0.45 [CI, 0.21 to 0.97]). Potential explanatory factors (SOC trends, case surges, and variant trends) were similar between ACTT-2 and ACTT-3, except for increased dexamethasone use (11% to 77%). Outcomes were similar in ACTT-3 and ACTT-4. Antibiotic use decreased gradually across all stages. LIMITATION: Unmeasured confounding. CONCLUSION: Changes in patient composition explained improved outcomes from ACTT-1 to ACTT-2 but not from ACTT-2 to ACTT-3, suggesting improved SOC. These results support excluding nonconcurrent controls from analysis of platform trials in rapidly changing therapeutic areas. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases.

Antigenic cartography of well-characterized human sera shows SARS-CoV-2 neutralization differences based on infection and vaccination history.

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.

COVID-19 Patient-Reported Symptoms Using FLU-PRO Plus in a Cohort Study: Associations With Infecting Genotype, Vaccine History, and Return to Health.

Background: Patient-reported outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are an important measure of the full burden of coronavirus disease (COVID). Here, we examine how (1) infecting genotype and COVID-19 vaccination correlate with inFLUenza Patient-Reported Outcome (FLU-PRO) Plus score, including by symptom domains, and (2) FLU-PRO Plus scores predict return to usual activities and health. Methods: The epidemiology, immunology, and clinical characteristics of pandemic infectious diseases (EPICC) study was implemented to describe the short- and long-term consequences of SARS-CoV-2 infection in a longitudinal, observational cohort. Multivariable linear regression models were run with FLU-PRO Plus scores as the outcome variable, and multivariable Cox proportional hazards models evaluated effects of FLU-PRO Plus scores on return to usual health or activities. Results: Among the 764 participants included in this analysis, 63% were 18-44 years old, 40% were female, and 51% were White. Being fully vaccinated was associated with lower total scores (ß = -0.39; 95% CI, -0.57 to -0.21). The Delta variant was associated with higher total scores (ß = 0.25; 95% CI, 0.05 to 0.45). Participants with higher FLU-PRO Plus scores were less likely to report returning to usual health and activities (health: hazard ratio [HR], 0.46; 95% CI, 0.37 to 0.57; activities: HR, 0.56; 95% CI, 0.47 to 0.67). Fully vaccinated participants were more likely to report returning to usual activities (HR, 1.24; 95% CI, 1.04 to 1.48). Conclusions: Full SARS-CoV-2 vaccination is associated with decreased severity of patient-reported symptoms across multiple domains, which in turn is likely to be associated with earlier return to usual activities. In addition, infection with the Delta variant was associated with higher FLU-PRO Plus scores than previous variants, even after controlling for vaccination status.

Understanding 'hybrid immunity': comparison and predictors of humoral immune responses to SARS-CoV-2 infection and COVID-19 vaccines.

BACKGROUND: Comparing humoral responses in SARS-CoV-2 vaccinees, those with SARS-CoV-2 infection, or combinations of vaccine/infection ('hybrid immunity'), may clarify predictors of vaccine immunogenicity. METHODS: We studied 2660 U.S. Military Health System beneficiaries with a history of SARS-CoV-2 infection-alone (n = 705), vaccination-alone (n = 932), vaccine-after-infection (n = 869), and vaccine-breakthrough-infection (n = 154). Peak anti-spike-IgG responses through 183 days were compared, with adjustment for vaccine product, demography, and comorbidities. We excluded those with evidence of clinical or sub-clinical SARS-CoV-2 reinfection from all groups. RESULTS: Multivariable regression results indicated vaccine-after-infection anti-spike-IgG responses were higher than infection-alone (p < 0.01), regardless of prior infection severity. An increased time between infection and vaccination was associated with a greater post-vaccination IgG response (p < 0.01). Vaccination-alone elicited a greater IgG response, but more rapid waning of IgG (p < 0.01), compared to infection-alone (p < 0.01). BNT162b2 and mRNA-1273 vaccine-receipt was associated with greater IgG responses compared to JNJ-78436735 (p < 0.01), regardless of infection history. Those with vaccine-after-infection or vaccine-breakthrough-infection had a more durable anti-spike-IgG response compared to infection-alone (p < 0.01). CONCLUSIONS: Vaccine-receipt elicited higher anti-spike-IgG responses than infection-alone, although IgG levels waned faster in those vaccinated (compared to infection-alone). Vaccine-after-infection elicits a greater humoral response compared to vaccine or infection alone; and the timing, but not disease severity, of prior infection predicted these post-vaccination IgG responses. While differences between groups were small in magnitude, these results offer insights into vaccine immunogenicity variations that may help inform vaccination timing strategies.

Anatomical Site, Viral Ribonucleic Acid Abundance, and Time of Sampling Correlate With Molecular Detection of Severe Acute Respiratory Syndrome Coronavirus 2 During Infection.

BACKGROUND: Nasopharyngeal (NP) swabs are the standard for SARS-CoV-2 diagnosis. If less invasive alternatives to NP swabs (eg, oropharyngeal [OP] or nasal swabs [NS]) are comparably sensitive, the use of these techniques may be preferable in terms of comfort, convenience, and safety. METHODS: This study compared the detection of SARS-CoV-2 in swab samples collected on the same day among participants with at least one positive PCR test. RESULTS: Overall, 755 participants had at least one set of paired swabs. Concordance between NP and other swab types was 75% (NS), 72% (OP), 54% (rectal swabs [RS]), and 78% (NS/OP combined). Kappa values were moderate for the NS, OP, and NS/OP comparisons (0.50, 0.45, and 0.54, respectively). Highest sensitivity relative to NP (0.87) was observed with a combination of NS/OP tests (positive if either NS or OP was positive). Sensitivity of the non-NP swab types was highest in the first week postsymptom onset and decreased thereafter. Similarly, virus RNA quantity was highest in the NP swabs as compared with NS, OP, and RS within two weeks postsymptom onset. OP and NS performance decreased as virus RNA quantity decreased. No differences were noted between NS specimens collected at home or in clinic. CONCLUSIONS: NP swabs detected more SARS-CoV-2 cases than non-NP swabs, and the sensitivity of the non-NP swabs decreased with time postsymptom onset. While other swabs may be simpler to collect, NP swabs present the best chance of detecting SARS-CoV-2 RNA, which is essential for clinical care as well as genomic surveillance.