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Background. Infection with SARS-CoV-2 and the resulting host immune response has been primarily characterized in middle and older aged populations due to a higher incidence of symptoms in these age groups. Due to reduced severity of disease, children were poorly studied and assumed to be less frequently infected compared to older age groups. We measured the viral load and adaptive immune response across the age-spectrum to define the age-dependent viral and host responses. Methods. From March 2020-March 2022, we enrolled individuals across the age spectrum who presented to U.S. military medical treatment facilities with COVID-19-like symptoms. In this longitudinal cohort study, demographic and clinical data were collected in addition to nasopharyngeal swabs and peripheral blood. Magnitude of viral RNA was measured by quantitative PCR (qPCR) from nasopharyngeal samples and SARS-CoV-2-specific IgG antibodies were measured from blood with multiplex microsphere immunoassays. Results. 4,768 SARS-CoV-2 positive participants were enrolled, among whom 42, 64, 89, 380, 948 and 245 individuals were in age brackets 0-4y, 5-11y, 12-17y, 18-44, 45-64y, and >65y, respectively. Viral load as measured by qPCR was determined to be similar across age groups within the first week post symptom onset. The magnitude of the IgG antibody response against the spike protein was also compared across age groups at early and convalescent time points and was higher in those over the age of 65 years. Conclusion. Early viral load during acute infection did not correlate with age in individuals who experienced COVID-19. These findings diverge from other respiratory viruses, such as respiratory syncytial virus and influenza where children tend to have higher viral loads. In contrast, the magnitude of the antibody response against the spike protein correlated with older age at acute and convalescent time points. Together our data suggest that the host response against SAR-CoV-2 differs with age and is not associated with the acute viral load. Defining age-dependent immunity against SARS-CoV-2 has the potential to identify key immunologic responses that can be used to optimize treatment and vaccine strategies.
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Background. Characterizing, diagnosing, and caring for 'long COVID' patients has proven to be challenging due to heterogenous symptoms and broad definitions of these post-acute sequelae. Here, we take a machine learning approach to identify discrete clusters of long COVID symptoms which may define specific long COVID phenotypes. Figure 1: (A) Principal component analysis followed by K-means clustering identified three groups of participants. (B) Heatmap depicting three distinct clusters (high values are in red and low value are in blue);Cluster 1 exhibits sensory symptoms (e.g., loss of smell and/or taste), Cluster 2 exhibits fatigue and difficulty thinking (e.g., changes in ability to think) symptoms, and Cluster 3 exhibits difficulty breathing and exercise intolerance symptoms. (C) Clinical and demographic characteristics of 97 military health system beneficiaries by identified clusters Methods. The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal COVID-19 cohort study with data and biospecimens collected from 10 military treatment facilities and online recruitment. Demographic and clinical characteristics were collected using case report forms and surveys completed at enrollment and at 1, 3, 6, 9, and 12 months. For this analysis, we identified those who reported any moderate to severe persistent symptoms on surveys collected 6-months post-COVID-19 symptom onset. Using the survey responses, we applied principal component analysis (PCA) followed by unsupervised machine learning clustering algorithm K-means to identify groups with distinct clusters of symptoms. Results. Of 1299 subjects with 6-month survey responses, 97 (7.47%) reported moderate to severe persistent symptoms. Among these subjects, three clusters were identified using PCA (Figure 1A). Cluster 1 is characterized by sensory symptoms (loss of taste and/or smell), Cluster 2 by fatigue and difficulty thinking, and Cluster 3 by difficulty breathing and exercise intolerance (Figure 1B). More than half of these subjects (57%) were female, 64% were 18-44 years old, and 64% had no comorbidities at enrollment (Figure 1C). Those in the sensory symptom cluster were all outpatients at the time of initial COVID-19 presentation (p < 0.01). The difficulty breathing and exercise intolerance symptom-clusters had a higher proportion of older participants (Age group >= 45-64) with more comorbidities (CCI >= 1-2). Conclusion. We identified three distinct 'long COVID' phenotypes among those with moderate to severe COVID-19 symptoms at 6-months post-symptom onset. With further validation and characterization, this framework may allow more precise classification of long COVID cases, and potentially improve the diagnosis, prognosis, and treatment of post- infectious sequelae.
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Background. Omicron SARS-CoV-2 infections are associated with less frequent olfactory sensory loss and a predominance of pharyngitis symptoms compared to prior variants, with proposed diagnostic implications. We examined whether such symptomology predicts a higher RNA abundance in the oropharynx. We further investigated how age, symptom-day, vaccination history and clinical severity correlate with viral load to inform clinical prognostication and transmission modeling. Methods. The EPICC study is a longitudinal cohort of COVID-19 cases enrolled through U.S military medical treatment facilities. Demographic and clinical characteristics were measured with interviews and surveys. Nasopharyngeal (NP), oropharyngeal (OP) and nasal swabs (NS) were collected for SARS-CoV-2 qPCR and sequence genotyping. Multivariable linear regression models were fit to estimate the effect of anatomical site on SARS-CoV-2 RNA abundance (a proxy for viral load), adjusting for sampling time, vaccine history and host age. Results. We analyzed 77 sequence-confirmed Omicron cases;no BA.2 cases were detected. The median age was 38.8 years. 81.8% were vaccinated and 15.6% cases were hospitalized. 80.0%, 21.8%, and 65.5% reported nasal congestion, loss of smell or taste, and sore throat, respectively. The median RNA abundance was lowest in OP swabs (p < 0.001) (Fig 1). Linear regression confirmed that OP sampling was associated with lower viral load (p < 0.001). We further noted that greater age and symptom-day were independent correlates of viral load (Table 1). By bivariate analysis there was a trend toward lower RNA abundance in vaccinated subjects (p = 0.35). RNA abundance (at any site) was substantially higher in hospitalized (10634 N2 genome equivalents [GE]/reaction) versus outpatient cases (1419 N1 GE/reaction) but this was not statistically significant (p = 0.26). Conclusion. We noted prevalent sore throat symptoms and infrequent sensory loss in Omicron cases. Despite this, viral load was highest in NP/NS collected swabs as has been noted in prior variants. We note an age correlation with RNA abundance, and provide a viral load decay rate which may be useful for transmission modeling. Vaccination and clinical severity may also correlate with Omicron viral load, as noted with prior SARS-CoV-2 variants.
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Background. COVID-19 may have deleterious effects on the fitness of active duty US military service members. We seek to understand the long-term functional consequences of SARS-CoV-2 infection in this critical population, and in other military healthcare beneficiaries. Methods. The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal cohort study to describe the outcomes of SARS-CoV-2 infection in US Military Health System beneficiaries. Subjects provided information about difficulties experienced with daily activities, exercise, and physical fitness performance via electronic surveys. Subjects completed surveys at enrollment and at 1, 3, 6, 9, and 12 months. Results. 5,910 subjects completed survey fitness questions, 3,244 (55%) of whom tested SARS-CoV-2 positive at least once during the period of observation. Over 75% of subjects were young adults and over half were male (Table 1). 1,093 (34.3%) of SARS-CoV-2-positive subjects reported new or increased difficulty exercising compared to 393 (14.8%) SARS-CoV-2 negative subjects (p < 0.01) (Table 2). The most commonly reported symptoms related to problems with exercise and activities were dyspnea and fatigue.Among the active-duty members who answered the question about their service-mandated physical fitness test scores, 43.2% of SARS-CoV-2-positive participants reported that their scores had worsened in the study period, compared with 24.3%of SARS-CoV-2 negative participants.Among SARS-CoV-2-positive subjects, reports of difficulty exercising and performing daily activities were highest within one month of the first positive test, decreasing in prevalence among the cohort only slightly to 24% and 18%, respectively, at 12 months (Figure 1). Conclusion. A substantial proportion of military service-members in this cohort have reported impairment of their service-mandated physical fitness scores after COVID-19;this proportion is significantly higher than those who are SARS-CoV-2 negative and persists to 12 months in many;similar complaints were reported among non-active duty. Further objective evaluation of post-COVID fitness impairment in this population is warranted. (Figure Presented).
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SESSION TITLE: Long COVID: It Can Take Your Breath Away SESSION TYPE: Original Investigations PRESENTED ON: 10/16/2022 10:30 am - 11:30 am PURPOSE: As the novel coronavirus SARS-CoV-2 swept the globe causing COVID-19 infection, a syndrome now known as “long COVID” has been well described in 10-30% of those who have experienced COVID-19. This study hoped to characterize changes in anatomical structure and physiology that may explain the ongoing dyspnea experienced by some individuals affected by the COVID-19 pandemic. METHODS: Patients with a history of symptomatic COVID-19 confirmed by positive PCR or antibody testing, between the age of 18-65, without pre-existing significant cardiopulmonary disease, and currently experiencing ongoing exertional or respiratory symptoms at least 3 months after onset of initial COVID symptoms were enrolled into this study. Each participant underwent standardized testing for underlying cardiopulmonary pathology by performance of a high-resolution chest CT, transthoracic echocardiography, electrocardiogram, full pulmonary function testing with lung volumes and diffusing capacity, impulse oscillometry, and a six minute walk test. RESULTS: To date, 63 patients have enrolled in the study with ongoing completion of study procedures. Of the current patients enrolled, 29 have had a high resolution chest CT completed;16 or 55% had radiographic evidence of pulmonary pathology. Most common were a nodular pattern (38%), mosaic attenuation (34%), residual ground glass opacities (28%), septal thickening (14%). Thirty-six participants performed the six minute walk test with an average walk distance of 1338.9 feet ± 520.4 feet with no participants desaturating below 90%. Pulmonary function testing has been completed in 36 participants with normal mean values. Impulse oscillometry testing performed on 30 individuals revealed mixed results with resistance at 5 Hz (R5) showing no substantive change to bronchodilator with a -14% ± 5%, however the area of reactance showed a potentially significant bronchodilator response with bronchodilator change of -43% ± 41%. CONCLUSIONS: In this interim analysis, we evaluated the radiographic and physiologic changes seen in a group of patients at least three months after symptomatic infection with COVID-19. There were radiographic changes in 50% of patients with a reticulonodular pattern as the most often reported finding. However, this finding did not correlate with PFT or exercise findings in the cohort;few showed significant PFT changes and the 6MWT did not show desaturations or limitation in walking distance. Pulmonary function testing and impulse oscillometry showed no statistically substantive physiologic derangements that might explain the ongoing symptoms of the group evaluated. CLINICAL IMPLICATIONS: Other than radiographic findings, there were no unified findings that could shed further light on the effects of COVID-19 that would predispose an individual to ongoing symptoms. DISCLOSURES: No relevant relationships by Brian Agan no disclosure on file for Timothy Burgess;no disclosure on file for Anuradha Ganesan;No relevant relationships by Stephen Goertzen No relevant relationships by Travis Harrell no disclosure on file for Nikhil Huprikar;No relevant relationships by David Lindholm No relevant relationships by Katrin Mende Speaker/Speaker's Bureau relationship with Janssen Please note: $1001 - $5000 by Michael Morris, value=Honoraria Speaker/Speaker's Bureau relationship with GSK Please note: $1001 - $5000 by Michael Morris, value=Honoraria Removed 03/29/2022 by Michael Morris no disclosure on file for Simon Pollett;no disclosure on file for Julia Rozman;No relevant relationships by Mark Simons No relevant relationships by David Tribble No relevant relationships by Robert Walter
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Background. The long-term health effects after SARS-CoV-2 infection remain poorly understood. We evaluated health and healthcare usage after SARS-CoV-2 infection via surveys and longitudinal electronic medical record (EMR) review within the Military Health System (MHS). Methods. We studied MHS beneficiaries enrolled in the Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) cohort from March to December 2020. COVID-19 illness symptom severity and duration were derived from surveys initiated in late 2020. In addition, multi-year healthcare encounter history before and after onset of COVID-19 symptoms was collected from the MHS EMR. Odds of organ-system clinical diagnoses within the 3 months pre- and post-symptom onset were calculated using generalized linear models, controlling for age, sex, and race, and including participant as a random effect. Results. 1,015 participants were included who were SARS-CoV-2 positive, symptomatic, and had 3-month follow-up data available in the EMR (Table 1). 625 of these participants had survey data collected more than 28 days post-symptom onset, among whom 17% and 6% reported persistent symptoms at 28-84 days, and 85+ days, respectively. 9.6% had not resumed normal activities by one month. The most frequently reported symptoms persisting beyond 28 days were dyspnea, loss of smell and/or taste, fatigue, and exercise intolerance (Figure 1A). When compared with the period 61 to 90 days prior to symptom onset, the first month post-symptom onset period was associated with increases of pulmonary (aOR = 57, 95% CI 28-112), renal (aOR = 29, 95% CI 10-84), cardiovascular (aOR = 7, 95% CI 5-11), and neurological diagnoses (aOR = 3, 95% CI 2-4) (Figures 1B and 1C). Cardiovascular disease diagnoses remained elevated through 3 months (aOR = 2, 95% CI 1-3). Fig1A. Symptoms reported by EPICC participants with illnesses longer than 28 days;1B. Percent of participants with organ system specific diagnoses on each day, 90 days pre- and post-symptom onset;1C. Odds of organ system specific diagnoses within each month, +/- 3 months of symptom onset, were calculated using generalized linear models, controlling for age, sex, and race and included participants as a random effect. Odds shown are relative to the earliest period included in the model, 61-90 days before onset. Conclusion. In this MHS cohort, a significant proportion of participants had persistent symptoms and cardiovascular disease diagnoses 3 months after COVID-19 illness onset. These findings emphasize the long-term morbidity of COVID-19 and the importance of mitigating SARS-CoV-2 infections. Further analyses will evaluate demographic, clinical, and biomarker predictors of medium-to-long term organ-specific post-acute sequelae.
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Background. The risk factors of venous thromboembolism (VTE) in COVID-19 warrant further study. We leveraged a cohort in the Military Health System (MHS) to identify clinical and virological predictors of incident deep venous thrombosis (DVT), pulmonary embolism (PE), and other VTE within 90-days after COVID-19 onset. Methods. PCR or serologically-confirmed SARS-CoV-2 infected MHS beneficiaries were enrolled via nine military treatment facilities (MTF) through April 2021. Case characteristics were derived from interview and review of the electronic medical record (EMR) through one-year follow-up in outpatients and inpatients. qPCR was performed on upper respiratory swab specimens collected post-enrollment to estimate SARS-CoV-2 viral load. The frequency of incident DVT, PE, or other VTE by 90-days post-COVID-19 onset were ascertained by ICD-10 code. Correlates of 90-day VTE were determined through multivariate logistic regression, including age and sampling-time-adjusted log10-SARS-CoV-2 GE/reaction as a priori predictors in addition to other demographic and clinical covariates which were selected through stepwise regression. Results. 1473 participants with SARS-CoV-2 infection were enrolled through April 2021. 21% of study participants were inpatients;the mean age was 41 years (SD = 17.0 years). The median Charlson Comorbidity Index score was 0 (IQR = 0 -1, range = 0 - 13). 27 (1.8%) had a prior history of VTE. Mean maximum viral load observed was 1.65 x 107 genome equivalents/reaction. 36 (2.4%) of all SARS-CoV-2 cases (including inpatients and outpatients), 29 (9.5%) of COVID-19 inpatients, and 7 (0.6%) of outpatients received an ICD-10 diagnosis of any VTE within 90 days after COVID-19 onset. Logistic regression identified hospitalization (aOR = 11.1, p = 0.003) and prior VTE (aOR = 6.2 , p = 0.009) as independent predictors of VTE within 90 days of symptom onset. Neither age (aOR = 1.0, p = 0.50), other demographic covariates, other comorbidities, nor SARS-CoV-2 viral load (aOR = 1.1, p = 0.60) were associated with 90-day VTE. Conclusion. VTE was relatively frequent in this MHS cohort. SARS-CoV-2 viral load did not increase the odds of 90-day VTE. Rather, being hospitalized for SARS-CoV-2 and prior VTE history remained the strongest predictors of this complication.
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Background. Approximately 10-20% of patients with critical COVID-19 harbor neutralizing autoantibodies (auto-Abs) that target type I interferons (IFN), a family of cytokines that induce critical innate immune defense mechanisms upon viral infection. Studies to date indicate that these auto-Abs are mostly detected in men over age 65. Methods. We screened for type I IFN serum auto-Abs in sera collected < 21 days post-symptom onset in a subset of 103 COVID-19 inpatients and 24 outpatients drawn from a large prospective cohort study of SARS-CoV-2 infected patients enrolled across U.S. Military Treatment Facilities. The mean age of this n = 127 subset of study participants was 55.2 years (SD = 15.2 years, range 7.7 - 86.2 years), and 86/127 (67.7%) were male. Results. Among those hospitalized 49/103 (47.6%) had severe COVID-19 (required at least high flow oxygen), and nine subjects died. We detected neutralizing auto-Abs against IFN-α, IFN-ω, or both, in four inpatients (3.9%, 8.2% of severe cases), with no auto-Abs detected in outpatients. Three of these patients were white males over the age of 62, all with multiple comorbidities;two of whom died and the third requiring high flow oxygen therapy. The fourth patient was a 36-year-old Hispanic female with a history of obesity who required mechanical ventilation during her admission for COVID-19. Conclusion. These findings support the association between type I IFN auto-antibody production and life-threatening COVID-19. With further validation, reliable high-throughput screening for type I IFN auto-Abs may inform diagnosis, pathogenesis and treatment strategies for COVID-19, particularly in older males. Our finding of type I IFN auto-Ab production in a younger female prompts further study of this autoimmune phenotype in a broader population.
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Background. The novel coronavirus disease 2019 (COVID-19) pandemic remains a global challenge. Accurate COVID-19 prognosis remains an important aspect of clinical management. While many prognostic systems have been proposed, most are derived from analyses of individual symptoms or biomarkers. Here, we take a machine learning approach to first identify discrete clusters of early stage-symptoms which may delineate groups with distinct symptom phenotypes. We then sought to identify whether these groups correlate with subsequent disease severity. Methods. The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal cohort study with data and biospecimens collected from nine military treatment facilities over 1 year of follow-up. Demographic and clinical characteristics were measured with interviews and electronic medical record review. Early symptoms by organ-domain were measured by FLU-PRO-plus surveys collected for 14 days post-enrollment, with surveys completed a median 14.5 (Interquartile Range, IQR = 13) days post-symptom onset. Using these FLU-PRO-plus responses, we applied principal component analysis followed by unsupervised machine learning algorithm k-means to identify groups with distinct clusters of symptoms. We then fit multivariate logistic regression models to determine how these early-symptom clusters correlated with hospitalization risk after controlling for age, sex, race, and obesity. Results. Using SARS-CoV-2 positive participants (n = 1137) from the EPICC cohort (Figure 1), we transformed reported symptoms into domains and identified three groups of participants with distinct clusters of symptoms. Logistic regression demonstrated that cluster-2 was associated with an approximately three-fold increased odds [3.01 (95% CI: 2-4.52);P < 0.001] of hospitalization which remained significant after controlling for other factors [2.97 (95% CI: 1.88-4.69);P < 0.001]. (A) Baseline characteristics of SARS-CoV-2 positive participants. (B) Heatmap comparing FLU-PRO response in each participant. (C) Principal component analysis followed by k-means clustering identified three groups of participants. (D) Crude and adjusted association of identified cluster with hospitalization. Conclusion. Our findings have identified three distinct groups with early-symptom phenotypes. With further validation of the clusters' significance, this tool could be used to improve COVID-19 prognosis in a precision medicine framework and may assist in patient triaging and clinical decision-making.
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Background: While the majority of illness due to COVID-19 does not require hospitalization, little has been described about the host inflammatory response in the ambulatory setting. Differences in the levels of inflammatory signaling proteins between outpatient and hospitalized populations could identify key maladaptive immune responses during COVID-19. Methods: Samples were collected from 76 participants (41% female, mean 46.8 years of age) enrolled at five military treatment facilities between March 20, 2020 and June 17, 2020 in an ongoing prospective COVID-19 cohort. This analysis was restricted to those with positive SARS-CoV-2 (severe acute respiratory syndrome- coronavirus 2) RT-PCR testing and included hospitalized (N=29;10 requiring an ICU stay) and non-hospitalized (N=43) participants. Severity markers (IL6, D-dimer, procalcitonin, ferritin, ICAM-1, IL5, lipocalin, RAGE, TNFR, VEGFA, IFNγ, IL1β) were measured in plasma (mg/dL) using the Ella immunoassay and natural log transformed. Univariate negative binomial regression was performed to determine relative risk of hospitalization. Using the full marker panel, we performed a Principal Component Analysis (PCA) to determine directions of maximal variance in the data. Pearson's correlation coefficient was determined between analytes and each axis. Results: Participants requiring ambulatory-, hospital-, and ICU-level care had samples collected at 44.0 (IQR: 35.0-51.0), 40.0 (13.0-51.0), and 47.5 (21.0-54.0) days, respectively. Higher unadjusted levels of IL6, D-dimer, procalcitonin, or ferritin were each associated with hospitalization (Table 1). The PCA showed a separation along axes between level of care and duration of symptoms (Fig 1). While significant correlations were noted with a number of biomarkers, PC1 most correlated with TNFR1 (r=0.88) and PC2 most correlated with IL6Ra (r=0.95). PC1 axis variation accounted for 36.5% of variance and the PC2 axis accounted for 20.0% of variance. Conclusion: TNFR1 and IL6Ra levels correlated with differences in the proinflammatory states between hospitalized and non-hospitalized individuals including time points late in the course of illness. Further analysis of these preliminary findings is needed to evaluate for differences by stages of illness.