Patients' Perspectives on Life and Recovery 1 Year After COVID-19 Hospitalization.

BACKGROUND: Many patients hospitalized for COVID-19 experience prolonged symptoms months after discharge. Little is known abou t patients' personal experiences recovering from COVID-19 in the United States (US), where medically underserved populations are at particular risk of adverse outcomes. OBJECTIVE: To explore patients' perspectives on the impact of COVID-19 hospitalization and barriers to and facilitators of recovery 1 year after hospital discharge in a predominantly Black American study population with high neighborhood-level socioeconomic disadvantage. DESIGN: Qualitative study utilizing individual, semi-structured interviews. PARTICIPANTS: Adult patients hospitalized for COVID-19 approximately 1 year after discharge home who were engaged in a COVID-19 longitudinal cohort study. APPROACH: The interview guide was developed and piloted by a multidisciplinary team. Interviews were audio-recorded and transcribed. Data were coded and organized into discrete themes using qualitative content analysis with constant comparison techniques. KEY RESULTS: Of 24 participants, 17 (71%) self-identified as Black, and 13 (54%) resided in neighborhoods with the most severe neighborhood-level socioeconomic disadvantage. One year after discharge, participants described persistent deficits in physical, cognitive, or psychological health that impacted their current lives. Repercussions included financial suffering and a loss of identity. Participants reported that clinicians often focused on physical health over cognitive and psychological health, an emphasis that posed a barrier to recovering holistically. Facilitators of recovery included robust financial or social support systems and personal agency in health maintenance. Spirituality and gratitude were common coping mechanisms. CONCLUSIONS: Persistent health deficits after COVID-19 resulted in downstream consequences in participants' lives. Though participants received adequate care to address physical needs, many described persistent unmet cognitive and psychological needs. A more comprehensive understanding of barriers and facilitators for COVID-19 recovery, contextualized by specific healthcare and socioeconomic needs related to socioeconomic disadvantage, is needed to better inform intervention delivery to patients that experience long-term sequelae of COVID-19 hospitalization.

Validating a Proteomic Signature of Severe COVID-19.

COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN: Prospective observational cohort study. SETTING: Two hospitals in the United States. PATIENTS: One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Validating a Proteomic Signature of Severe COVID-19

OBJECTIVES: COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN: Prospective observational cohort study. SETTING: Two hospitals in the United States. PATIENTS: One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88–0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

I-SPY COVID adaptive platform trial for COVID-19 acute respiratory failure: rationale, design and operations.

INTRODUCTION: The COVID-19 pandemic brought an urgent need to discover novel effective therapeutics for patients hospitalised with severe COVID-19. The Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis (ISPY COVID-19 trial) was designed and implemented in early 2020 to evaluate investigational agents rapidly and simultaneously on a phase 2 adaptive platform. This manuscript outlines the design, rationale, implementation and challenges of the ISPY COVID-19 trial during the first phase of trial activity from April 2020 until December 2021. METHODS AND ANALYSIS: The ISPY COVID-19 Trial is a multicentre open-label phase 2 platform trial in the USA designed to evaluate therapeutics that may have a large effect on improving outcomes from severe COVID-19. The ISPY COVID-19 Trial network includes academic and community hospitals with significant geographical diversity across the country. Enrolled patients are randomised to receive one of up to four investigational agents or a control and are evaluated for a family of two primary outcomes-time to recovery and mortality. The statistical design uses a Bayesian model with 'stopping' and 'graduation' criteria designed to efficiently discard ineffective therapies and graduate promising agents for definitive efficacy trials. Each investigational agent arm enrols to a maximum of 125 patients per arm and is compared with concurrent controls. As of December 2021, 11 investigational agent arms had been activated, and 8 arms were complete. Enrolment and adaptation of the trial design are ongoing. ETHICS AND DISSEMINATION: ISPY COVID-19 operates under a central institutional review board via Wake Forest School of Medicine IRB00066805. Data generated from this trial will be reported in peer-reviewed medical journals. TRIAL REGISTRATION NUMBER: NCT04488081.

Signaling Through FcγRIIA and the C5a-C5aR Pathway Mediate Platelet Hyperactivation in COVID-19.

Patients with COVID-19 present with a wide variety of clinical manifestations. Thromboembolic events constitute a significant cause of morbidity and mortality in patients infected with SARS-CoV-2. Severe COVID-19 has been associated with hyperinflammation and pre-existing cardiovascular disease. Platelets are important mediators and sensors of inflammation and are directly affected by cardiovascular stressors. In this report, we found that platelets from severely ill, hospitalized COVID-19 patients exhibited higher basal levels of activation measured by P-selectin surface expression and had poor functional reserve upon in vitro stimulation. To investigate this question in more detail, we developed an assay to assess the capacity of plasma from COVID-19 patients to activate platelets from healthy donors. Platelet activation was a common feature of plasma from COVID-19 patients and correlated with key measures of clinical outcome including kidney and liver injury, and APACHEIII scores. Further, we identified ferritin as a pivotal clinical marker associated with platelet hyperactivation. The COVID-19 plasma-mediated effect on control platelets was highest for patients that subsequently developed inpatient thrombotic events. Proteomic analysis of plasma from COVID-19 patients identified key mediators of inflammation and cardiovascular disease that positively correlated with in vitro platelet activation. Mechanistically, blocking the signaling of the FcγRIIa-Syk and C5a-C5aR pathways on platelets, using antibody-mediated neutralization, IgG depletion or the Syk inhibitor fostamatinib, reversed this hyperactivity driven by COVID-19 plasma and prevented platelet aggregation in endothelial microfluidic chamber conditions. These data identified these potentially actionable pathways as central for platelet activation and/or vascular complications and clinical outcomes in COVID-19 patients. In conclusion, we reveal a key role of platelet-mediated immunothrombosis in COVID-19 and identify distinct, clinically relevant, targetable signaling pathways that mediate this effect.

Erythrocytes identify complement activation in patients with COVID-19.

COVID-19, the disease caused by the SARS-CoV-2 virus, can progress to multisystem organ failure and viral sepsis characterized by respiratory failure, arrhythmias, thromboembolic complications, and shock with high mortality. Autopsy and preclinical evidence implicate aberrant complement activation in endothelial injury and organ failure. Erythrocytes express complement receptors and are capable of binding immune complexes; therefore, we investigated complement activation in patients with COVID-19 using erythrocytes as a tool to diagnose complement activation. We discovered enhanced C3b and C4d deposition on erythrocytes in COVID-19 sepsis patients and non-COVID sepsis patients compared with healthy controls, supporting the role of complement in sepsis-associated organ injury. Our data suggest that erythrocytes may contribute to a precision medicine approach to sepsis and have diagnostic value in monitoring complement dysregulation in COVID-19-sepsis and non-COVID sepsis and identifying patients who may benefit from complement targeted therapies.

CD8+ T cells contribute to survival in patients with COVID-19 and hematologic cancer.

Patients with cancer have high mortality from coronavirus disease 2019 (COVID-19), and the immune parameters that dictate clinical outcomes remain unknown. In a cohort of 100 patients with cancer who were hospitalized for COVID-19, patients with hematologic cancer had higher mortality relative to patients with solid cancer. In two additional cohorts, flow cytometric and serologic analyses demonstrated that patients with solid cancer and patients without cancer had a similar immune phenotype during acute COVID-19, whereas patients with hematologic cancer had impairment of B cells and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific antibody responses. Despite the impaired humoral immunity and high mortality in patients with hematologic cancer who also have COVID-19, those with a greater number of CD8 T cells had improved survival, including those treated with anti-CD20 therapy. Furthermore, 77% of patients with hematologic cancer had detectable SARS-CoV-2-specific T cell responses. Thus, CD8 T cells might influence recovery from COVID-19 when humoral immunity is deficient. These observations suggest that CD8 T cell responses to vaccination might provide protection in patients with hematologic cancer even in the setting of limited humoral responses.

Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for ARDS. We sought to train a deep convolutional neural network (CNN) to detect ARDS findings on chest radiographs. METHODS: CNNs were pretrained on 595 506 radiographs from two centres to identify common chest findings (eg, opacity and effusion), and then trained on 8072 radiographs annotated for ARDS by multiple physicians using various transfer learning approaches. The best performing CNN was tested on chest radiographs in an internal and external cohort, including a subset reviewed by six physicians, including a chest radiologist and physicians trained in intensive care medicine. Chest radiograph data were acquired from four US hospitals. FINDINGS: In an internal test set of 1560 chest radiographs from 455 patients with acute hypoxaemic respiratory failure, a CNN could detect ARDS with an area under the receiver operator characteristics curve (AUROC) of 0·92 (95% CI 0·89-0·94). In the subgroup of 413 images reviewed by at least six physicians, its AUROC was 0·93 (95% CI 0·88-0·96), sensitivity 83·0% (95% CI 74·0-91·1), and specificity 88·3% (95% CI 83·1-92·8). Among images with zero of six ARDS annotations (n=155), the median CNN probability was 11%, with six (4%) assigned a probability above 50%. Among images with six of six ARDS annotations (n=27), the median CNN probability was 91%, with two (7%) assigned a probability below 50%. In an external cohort of 958 chest radiographs from 431 patients with sepsis, the AUROC was 0·88 (95% CI 0·85-0·91). When radiographs annotated as equivocal were excluded, the AUROC was 0·93 (0·92-0·95). INTERPRETATION: A CNN can be trained to achieve expert physician-level performance in ARDS detection on chest radiographs. Further research is needed to evaluate the use of these algorithms to support real-time identification of ARDS patients to ensure fidelity with evidence-based care or to support ongoing ARDS research. FUNDING: National Institutes of Health, Department of Defense, and Department of Veterans Affairs.

Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications.

Coronavirus disease 2019 (COVID-19) is currently a global pandemic, but human immune responses to the virus remain poorly understood. We used high-dimensional cytometry to analyze 125 COVID-19 patients and compare them with recovered and healthy individuals. Integrated analysis of ~200 immune and ~50 clinical features revealed activation of T cell and B cell subsets in a proportion of patients. A subgroup of patients had T cell activation characteristic of acute viral infection and plasmablast responses reaching >30% of circulating B cells. However, another subgroup had lymphocyte activation comparable with that in uninfected individuals. Stable versus dynamic immunological signatures were identified and linked to trajectories of disease severity change. Our analyses identified three immunotypes associated with poor clinical trajectories versus improving health. These immunotypes may have implications for the design of therapeutics and vaccines for COVID-19.

Comprehensive mapping of immune perturbations associated with severe COVID-19.

Although critical illness has been associated with SARS-CoV-2-induced hyperinflammation, the immune correlates of severe COVID-19 remain unclear. Here, we comprehensively analyzed peripheral blood immune perturbations in 42 SARS-CoV-2 infected and recovered individuals. We identified extensive induction and activation of multiple immune lineages, including T cell activation, oligoclonal plasmablast expansion, and Fc and trafficking receptor modulation on innate lymphocytes and granulocytes, that distinguished severe COVID-19 cases from healthy donors or SARS-CoV-2-recovered or moderate severity patients. We found the neutrophil to lymphocyte ratio to be a prognostic biomarker of disease severity and organ failure. Our findings demonstrate broad innate and adaptive leukocyte perturbations that distinguish dysregulated host responses in severe SARS-CoV-2 infection and warrant therapeutic investigation.