METABOLOMIC PROFILING AND PROGNOSTICATION IN COVID-19-RELATED ARDS

SESSION TITLE: Late Breaking Posters in Critical Care SESSION TYPE: Original Investigation Posters PRESENTED ON: 10/18/2022 01:30 pm - 02:30 pm PURPOSE: The majority of deaths in COVID-19 are due to acute respiratory distress syndrome (ARDS). We recently identified two subphenotypes among patients with COVID-19 related ARDS (C-ARDS) with divergent outcomes and responses to therapies. However, the precise biological processes that distinguish the subphenotypes, remain to be fully elucidated. High-resolution profiling of the metabolome can be used to gain precise insights into disease pathogenesis. The purpose of this study was to use precise, metabolomic profiling at the onset of C-ARDS to identify metabolic alterations and predict hospital mortality. METHODS: This was a retrospective, matched cohort study. Participants were adults with COVID-19 who met Berlin criteria for ARDS on the initial day of mechanical ventilation. All participants had prospectively banked plasma samples collected within one week of intubation. Twenty-five survivors to 90-days were matched on age, sex, and ethnicity to 25 patients who died within 28 days of intubation. Untargeted and targeted metabolomic analysis was performed using mass spectrometry and compared between survivors and non-survivors. Statistical analyses were performed with conditional logistic regression modeling with Bayesian inference. Compounds associated with mortality were identified using a cut-off of Bayes Factor (BF) > 3. Biological clustering analysis was performed using ChemRICH. Competitive modeling by four machine learning models—LASSO, adaptive LASSO, Random Forest, and XGBoost—was used to predict mortality. Three sets of predictors were explored: all metabolites, metabolites with BF > 1, and metabolites with BF > 3. RESULTS: Targeted and untargeted metabolomics of metabolic analytes yielded data for 30 bile acids, 340 biogenic amines, 522 complex lipids, 83 oxylipins, and 133 primary metabolites. Twenty-five compounds were identified with significant differences between survivors and non-survivors. Five compounds had increased levels associated with mortality, and 20 had decreased levels associated with mortality. Biological clustering analysis on these compounds identified four key clusters of compounds—unsaturated and saturated lysophosphatidylcholines, plasmalogens, and saturated ceramides—that were decreased amongst non-survivors. A machine learning-derived signature reflecting these metabolites showed excellent discrimination in predicting mortality, with the best model demonstrating area-under-the-receiver-operating-characteristic curve of 0.91. CONCLUSIONS: Metabolomic analysis identified differential enrichment of lipid metabolites in C-ARDS survivors compared to non-survivors. A machine learning model was able to accurately predict mortality from C-ARDS based on metabolomic profiles. CLINICAL IMPLICATIONS: Improved characterization of the metabolomic derangements in COVID-19 ARDS may lead to an enhanced understanding of drivers of mortality and improve prognostication and precision therapy. DISCLOSURES: No relevant relationships by Thomas Briese No relevant relationships by Xiaoyu Che No relevant relationships by Matthew Cummings No relevant relationships by Oliver Fiehn No relevant relationships by David Furfaro No relevant relationships by Wenhao Gou no disclosure on file for Walter Lipkin;no disclosure on file for Nischay Mishra;No relevant relationships by Max O'Donnell

Intervention and Control of COVID-19 Requires both Community-Based Monitoring and Contact Tracing

There is growing evidence that the current worldwide epidemic of COVID-19 has many asymptomatic infections. This raises new questions for the study of medical researchers, and it is also new challenge for non-medical interventions. Based on the SEIR model with periodic boundary, this study simulates the actual spread of pathogens and the interventions by multi-agent simulation. The simulations show that epidemic prevention and control can be feasible only with the joint intervention of the two measures when there are asymptomatic infections, one cannot leave without. Our results are of guiding importance for the prevention and control of epidemic situation in the world. © 2020, The Journal of Agency of Complex Systems and Complexity Science. All right reserved.