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1.
Front Immunol ; 13: 834988, 2022.
Article in English | MEDLINE | ID: covidwho-1753370

ABSTRACT

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.


Subject(s)
Blood Platelets/immunology , COVID-19/immunology , Complement C5a/metabolism , Receptor, Anaphylatoxin C5a/metabolism , Receptors, IgG/metabolism , SARS-CoV-2/physiology , Thromboembolism/immunology , Adult , Aminopyridines/pharmacology , Cells, Cultured , Female , Hospitalization , Humans , Male , Morpholines/pharmacology , Platelet Activation , Pyrimidines/pharmacology , Severity of Illness Index , Signal Transduction , Syk Kinase/antagonists & inhibitors
2.
Am J Physiol Lung Cell Mol Physiol ; 321(2): L485-L489, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1299247

ABSTRACT

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.


Subject(s)
COVID-19/complications , Complement Activation/immunology , Complement C3b/immunology , Complement C4b/immunology , Erythrocytes/immunology , Peptide Fragments/immunology , Respiratory Insufficiency/diagnosis , Sepsis/diagnosis , COVID-19/immunology , COVID-19/virology , Complement C3b/metabolism , Complement C4b/metabolism , Erythrocytes/metabolism , Erythrocytes/virology , Female , Humans , Male , Middle Aged , Peptide Fragments/metabolism , Respiratory Insufficiency/immunology , Respiratory Insufficiency/metabolism , Respiratory Insufficiency/virology , SARS-CoV-2/isolation & purification , Sepsis/immunology , Sepsis/metabolism , Sepsis/virology
3.
Ann Intern Med ; 174(5): 613-621, 2021 05.
Article in English | MEDLINE | ID: covidwho-1239133

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic continues to surge in the United States and globally. OBJECTIVE: To describe the epidemiology of COVID-19-related critical illness, including trends in outcomes and care delivery. DESIGN: Single-health system, multihospital retrospective cohort study. SETTING: 5 hospitals within the University of Pennsylvania Health System. PATIENTS: Adults with COVID-19-related critical illness who were admitted to an intensive care unit (ICU) with acute respiratory failure or shock during the initial surge of the pandemic. MEASUREMENTS: The primary exposure for outcomes and care delivery trend analyses was longitudinal time during the pandemic. The primary outcome was all-cause 28-day in-hospital mortality. Secondary outcomes were all-cause death at any time, receipt of mechanical ventilation (MV), and readmissions. RESULTS: Among 468 patients with COVID-19-related critical illness, 319 (68.2%) were treated with MV and 121 (25.9%) with vasopressors. Outcomes were notable for an all-cause 28-day in-hospital mortality rate of 29.9%, a median ICU stay of 8 days (interquartile range [IQR], 3 to 17 days), a median hospital stay of 13 days (IQR, 7 to 25 days), and an all-cause 30-day readmission rate (among nonhospice survivors) of 10.8%. Mortality decreased over time, from 43.5% (95% CI, 31.3% to 53.8%) to 19.2% (CI, 11.6% to 26.7%) between the first and last 15-day periods in the core adjusted model, whereas patient acuity and other factors did not change. LIMITATIONS: Single-health system study; use of, or highly dynamic trends in, other clinical interventions were not evaluated, nor were complications. CONCLUSION: Among patients with COVID-19-related critical illness admitted to ICUs of a learning health system in the United States, mortality seemed to decrease over time despite stable patient characteristics. Further studies are necessary to confirm this result and to investigate causal mechanisms. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.


Subject(s)
COVID-19/mortality , COVID-19/therapy , Critical Illness/mortality , Critical Illness/therapy , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Shock/mortality , Shock/therapy , APACHE , Academic Medical Centers , Aged , Female , Hospital Mortality , Humans , Intensive Care Units , Length of Stay/statistics & numerical data , Male , Middle Aged , Pandemics , Patient Readmission/statistics & numerical data , Pennsylvania/epidemiology , Pneumonia, Viral/virology , Respiration, Artificial/statistics & numerical data , Retrospective Studies , SARS-CoV-2 , Shock/virology , Survival Rate
4.
Nat Med ; 27(7): 1280-1289, 2021 07.
Article in English | MEDLINE | ID: covidwho-1238011

ABSTRACT

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.


Subject(s)
CD8-Positive T-Lymphocytes/immunology , COVID-19/immunology , Hematologic Neoplasms/immunology , Neoplasms/immunology , Aged , Antibodies, Viral/immunology , B-Lymphocytes/immunology , COVID-19/complications , COVID-19/mortality , Cohort Studies , Female , Hematologic Neoplasms/complications , Humans , Immunity, Cellular/immunology , Immunity, Humoral/immunology , Immunophenotyping , Logistic Models , Male , Middle Aged , Multivariate Analysis , Neoplasms/complications , Proportional Hazards Models , Prospective Studies , SARS-CoV-2 , Survival Rate
5.
Chest ; 160(3): 929-943, 2021 09.
Article in English | MEDLINE | ID: covidwho-1220138

ABSTRACT

BACKGROUND: Subphenotypes have been identified in patients with sepsis and ARDS and are associated with different outcomes and responses to therapies. RESEARCH QUESTION: Can unique subphenotypes be identified among critically ill patients with COVID-19? STUDY DESIGN AND METHODS: Using data from a multicenter cohort study that enrolled critically ill patients with COVID-19 from 67 hospitals across the United States, we randomly divided centers into discovery and replication cohorts. We used latent class analysis independently in each cohort to identify subphenotypes based on clinical and laboratory variables. We then analyzed the associations of subphenotypes with 28-day mortality. RESULTS: Latent class analysis identified four subphenotypes (SP) with consistent characteristics across the discovery (45 centers; n = 2,188) and replication (22 centers; n = 1,112) cohorts. SP1 was characterized by shock, acidemia, and multiorgan dysfunction, including acute kidney injury treated with renal replacement therapy. SP2 was characterized by high C-reactive protein, early need for mechanical ventilation, and the highest rate of ARDS. SP3 showed the highest burden of chronic diseases, whereas SP4 demonstrated limited chronic disease burden and mild physiologic abnormalities. Twenty-eight-day mortality in the discovery cohort ranged from 20.6% (SP4) to 52.9% (SP1). Mortality across subphenotypes remained different after adjustment for demographics, comorbidities, organ dysfunction and illness severity, regional and hospital factors. Compared with SP4, the relative risks were as follows: SP1, 1.67 (95% CI, 1.36-2.03); SP2, 1.39 (95% CI, 1.17-1.65); and SP3, 1.39 (95% CI, 1.15-1.67). Findings were similar in the replication cohort. INTERPRETATION: We identified four subphenotypes of COVID-19 critical illness with distinct patterns of clinical and laboratory characteristics, comorbidity burden, and mortality.


Subject(s)
Acute Kidney Injury/therapy , COVID-19/epidemiology , Critical Illness/epidemiology , Pandemics , Renal Replacement Therapy/methods , Acute Kidney Injury/epidemiology , Aged , COVID-19/therapy , Comorbidity , Female , Humans , Male , Middle Aged , SARS-CoV-2 , United States/epidemiology
6.
Lancet Digit Health ; 3(6): e340-e348, 2021 06.
Article in English | MEDLINE | ID: covidwho-1193002

ABSTRACT

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.


Subject(s)
Deep Learning , Neural Networks, Computer , Radiographic Image Interpretation, Computer-Assisted/methods , Radiography, Thoracic , Respiratory Distress Syndrome/diagnosis , Aged , Algorithms , Area Under Curve , Datasets as Topic , Female , Hospitals , Humans , Lung/diagnostic imaging , Lung/pathology , Male , Middle Aged , Pleural Cavity/diagnostic imaging , Pleural Cavity/pathology , Pleural Diseases , Radiography , Respiratory Distress Syndrome/diagnostic imaging , Retrospective Studies , United States
7.
Science ; 369(6508)2020 09 04.
Article in English | MEDLINE | ID: covidwho-981641

ABSTRACT

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.


Subject(s)
B-Lymphocytes/immunology , Betacoronavirus/immunology , Coronavirus Infections/immunology , Pneumonia, Viral/immunology , T-Lymphocytes/immunology , Adaptive Immunity , Adult , Aged , Aged, 80 and over , Antibodies, Viral/blood , B-Lymphocyte Subsets/immunology , CD4-Positive T-Lymphocytes/immunology , CD8-Positive T-Lymphocytes/immunology , COVID-19 , Cytokines/blood , Female , Humans , Immunologic Memory , Lymphocyte Activation , Male , Middle Aged , Pandemics , Plasma Cells/immunology , SARS-CoV-2 , Severity of Illness Index , T-Lymphocyte Subsets/immunology , T-Lymphocytes, Helper-Inducer/immunology , Time Factors , Young Adult
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