Chemokines, soluble PD-L1, and immune cell hyporesponsiveness are distinct features of SARS-CoV-2 critical illness.

Critically ill patients manifest many of the same immune features seen in coronavirus disease 2019 (COVID-19), including both "cytokine storm" and "immune suppression." However, direct comparisons of molecular and cellular profiles between contemporaneously enrolled critically ill patients with and without severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are limited. We sought to identify immune signatures specifically enriched in critically ill patients with COVID-19 compared with patients without COVID-19. We enrolled a multisite prospective cohort of patients admitted under suspicion for COVID-19, who were then determined to be SARS-CoV-2-positive (n = 204) or -negative (n = 122). SARS-CoV-2-positive patients had higher plasma levels of CXCL10, sPD-L1, IFN-γ, CCL26, C-reactive protein (CRP), and TNF-α relative to SARS-CoV-2-negative patients adjusting for demographics and severity of illness (Bonferroni P value < 0.05). In contrast, the levels of IL-6, IL-8, IL-10, and IL-17A were not significantly different between the two groups. In SARS-CoV-2-positive patients, higher plasma levels of sPD-L1 and TNF-α were associated with fewer ventilator-free days (VFDs) and higher mortality rates (Bonferroni P value < 0.05). Lymphocyte chemoattractants such as CCL17 were associated with more severe respiratory failure in SARS-CoV-2-positive patients, but less severe respiratory failure in SARS-CoV-2-negative patients (P value for interaction < 0.01). Circulating T cells and monocytes from SARS-CoV-2-positive subjects were hyporesponsive to in vitro stimulation compared with SARS-CoV-2-negative subjects. Critically ill SARS-CoV-2-positive patients exhibit an immune signature of high interferon-induced lymphocyte chemoattractants (e.g., CXCL10 and CCL17) and immune cell hyporesponsiveness when directly compared with SARS-CoV-2-negative patients. This suggests a specific role for T-cell migration coupled with an immune-checkpoint regulatory response in COVID-19-related critical illness.

Identifying ARDS using the Hierarchical Attention Network with Sentence Objectives Framework.

Acute respiratory distress syndrome (ARDS) is a life-threatening condition that is often undiagnosed or diagnosed late. ARDS is especially prominent in those infected with COVID-19. We explore the automatic identification of ARDS indicators and confounding factors in free-text chest radiograph reports. We present a new annotated corpus of chest radiograph reports and introduce the Hierarchical Attention Network with Sentence Objectives (HANSO) text classification framework. HANSO utilizes fine-grained annotations to improve document classification performance. HANSO can extract ARDS-related information with high performance by leveraging relation annotations, even if the annotated spans are noisy. Using annotated chest radiograph images as a gold standard, HANSO identifies bilateral infiltrates, an indicator of ARDS, in chest radiograph reports with performance (0.87 F1) comparable to human annotations (0.84 F1). This algorithm could facilitate more efficient and expeditious identification of ARDS by clinicians and researchers and contribute to the development of new therapies to improve patient care.