Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 3 de 3
Nat Med ; 27(7): 1280-1289, 2021 07.
Article in English | MEDLINE | ID: covidwho-1238011


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.

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
Lancet Digit Health ; 3(6): e340-e348, 2021 06.
Article in English | MEDLINE | ID: covidwho-1193002


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 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
Sci Immunol ; 5(49)2020 07 15.
Article in English | MEDLINE | ID: covidwho-646575


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.

B-Lymphocyte Subsets/immunology , Betacoronavirus/immunology , Coronavirus Infections/immunology , Neutrophils/immunology , Pneumonia, Viral/immunology , T-Lymphocytes/immunology , Aged , COVID-19 , Clonal Selection, Antigen-Mediated/immunology , Coronavirus Infections/pathology , Cytokines/metabolism , Female , Humans , Immunity, Innate/immunology , Immunologic Memory/immunology , Lymphocyte Activation/immunology , Lymphocyte Count , Male , Middle Aged , Pandemics , Pneumonia, Viral/pathology , SARS-CoV-2