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.

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.

Assessing the use of a micro-sampling device for measuring blood protein levels in healthy subjects and COVID-19 patients.

BACKGROUND: Venous phlebotomy performed by trained personnel is critical for patient diagnosis and monitoring of chronic disease, but has limitations in resource-constrained settings, and represents an infection control challenge during outbreaks. Self-collection devices have the potential to shift phlebotomy closer to the point of care, supporting telemedicine strategies and virtual clinical trials. Here we assess a capillary blood micro-sampling device, the Tasso Serum Separator Tube (SST), for measuring blood protein levels in healthy subjects and non-hospitalized COVID-19 patients. METHODS: 57 healthy controls and 56 participants with mild/moderate COVID-19 were recruited at two U.S. military healthcare facilities. Healthy controls donated Tasso SST capillary serum, venous plasma and venous serum samples at multiple time points, while COVID-19 patients donated a single Tasso SST serum sample at enrolment. Concentrations of 17 protein inflammatory biomarkers were measured in all biospecimens by Ella multi-analyte immune-assay. RESULTS: Tasso SST serum protein measurements in healthy control subjects were highly reproducible, but their agreements with matched venous samples varied. Most of the selected proteins, including CRP, Ferritin, IL-6 and PCT, were well-correlated between Tasso SST and venous serum with little sample type bias, but concentrations of D-dimer, IL-1B and IL-1Ra were not. Self-collection at home with delayed sample processing was associated with significant concentrations differences for several analytes compared to supervised, in-clinic collection with rapid processing. Finally, Tasso SST serum protein concentrations were significantly elevated in in non-hospitalized COVID-19 patients compared with healthy controls. CONCLUSIONS: Self-collection of capillary blood with micro-sampling devices provides an attractive alternative to routine phlebotomy. However, concentrations of certain analytes may differ significantly from those in venous samples, and factors including user proficiency, temperature control and time lags between specimen collection and processing need to be considered for their effect on sample quality and reproducibility.

COVID-19 Outcomes Among US Military Health System Beneficiaries Include Complications Across Multiple Organ Systems and Substantial Functional Impairment.

BACKGROUND: We evaluated clinical outcomes, functional burden, and complications 1 month after coronavirus disease 2019 (COVID-19) infection in a prospective US Military Health System (MHS) cohort of active duty, retiree, and dependent populations using serial patient-reported outcome surveys and electronic medical record (EMR) review. METHODS: MHS beneficiaries presenting at 9 sites across the United States with a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test, a COVID-19-like illness, or a high-risk SARS-CoV-2 exposure were eligible for enrollment. Medical history and clinical outcomes were collected through structured interviews and International Classification of Diseases-based EMR review. Risk factors associated with hospitalization were determined by multivariate logistic regression. RESULTS: A total of 1202 participants were enrolled. There were 1070 laboratory-confirmed SARS-CoV-2 cases and 132 SARS-CoV-2-negative participants. In the first month post-symptom onset among the SARS-CoV-2-positive cases, there were 212 hospitalizations, 80% requiring oxygen, 20 ICU admissions, and 10 deaths. Risk factors for COVID-19-associated hospitalization included race (increased for Asian, Black, and Hispanic compared with non-Hispanic White), age (age 45-64 and 65+ compared with <45), and obesity (BMI≥30 compared with BMI<30). Over 2% of survey respondents reported the need for supplemental oxygen, and 31% had not returned to normal daily activities at 1 month post-symptom onset. CONCLUSIONS: Older age, reporting Asian, Black, or Hispanic race/ethnicity, and obesity are associated with SARS-CoV-2 hospitalization. A proportion of acute SARS-CoV-2 infections require long-term oxygen therapy; the impact of SARS-CoV-2 infection on short-term functional status was substantial. A significant number of MHS beneficiaries had not yet returned to normal activities by 1 month.

Durability of SARS-CoV-2-Specific T-Cell Responses at 12 Months Postinfection.

BACKGROUND: Characterizing the longevity and quality of cellular immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enhances understanding of coronavirus disease 2019 (COVID-19) immunity that influences clinical outcomes. Prior studies suggest SARS-CoV-2-specific T cells are present in peripheral blood 10 months after infection. Analysis of the function, durability, and diversity of cellular response long after natural infection, over a range of ages and disease phenotypes, is needed to identify preventative and therapeutic interventions. METHODS: We identified participants in our multisite longitudinal, prospective cohort study 12 months after SARS-CoV-2 infection representing a range of disease severity. We investigated function, phenotypes, and frequency of T cells specific for SARS-CoV-2 using intracellular cytokine staining and spectral flow cytometry, and compared magnitude of SARS-CoV-2-specific antibodies. RESULTS: SARS-CoV-2-specific antibodies and T cells were detected 12 months postinfection. Severe acute illness was associated with higher frequencies of SARS-CoV-2-specific CD4 T cells and antibodies at 12 months. In contrast, polyfunctional and cytotoxic T cells responsive to SARS-CoV-2 were identified in participants over a wide spectrum of disease severity. CONCLUSIONS: SARS-CoV-2 infection induces polyfunctional memory T cells detectable at 12 months postinfection, with higher frequency noted in those who experienced severe disease.