Predictors of intubation in COVID-19 patients undergoing awake proning in the emergency department.

BACKGROUND: Awake prone positioning (PP) has been used to avoid intubations in hypoxic COVID-19 patients, but there is limited evidence regarding its efficacy. Moreover, clinicians have little information to identify patients at high risk of intubation despite awake PP. We sought to assess the intubation rate among patients treated with awake PP in our Emergency Department (ED) and identify predictors of need for intubation. METHODS: We conducted a multicenter retrospective cohort study of adult patients admitted for known or suspected COVID-19 who were treated with awake PP in the ED. We excluded patients intubated in the ED. Our primary outcome was prevalence of intubation during initial hospitalization. Other outcomes were intubation within 48 h of admission and mortality. We performed classification and regression tree analysis to identify the variables most likely to predict the need for intubation. RESULTS: We included 97 patients; 44% required intubation and 21% were intubated within 48 h of admission. Respiratory oxygenation (ROX) index and P/F (partial pressure of oxygen / fraction of inspired oxygen) ratio measured 24 h after admission were the variables most likely to predict need for intubation (area under the receiver operating characteristic curve = 0.82). CONCLUSIONS: Among COVID-19 patients treated with awake PP in the ED prior to admission, ROX index and P/F ratio, particularly 24 h after admission, may be useful tools in identifying patients at high risk of intubation.

Large-Scale Multi-omic Analysis of COVID-19 Severity.

We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many of which were involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, e.g., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool (covid-omics.app) enabling interactive exploration of our compendium and illustrate its utility through a machine learning approach for prediction of COVID-19 severity.