ABSTRACT
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
ABSTRACT
Chronic pain produces the largest non-fatal burden of disease, yet our understanding of factors that contribute to the transition from acute chronic pain are poorly understood. The Acute to Chronic Pain Signatures Program (A2CPS) is a longitudinal, multi-site observational study to identify biomarkers (individual or biosignature combinations) that predict susceptibility or resilience to the development of chronic pain after surgery (knee replacement or thoracotomy). Due to the COVID-19 pandemic, however, travel between sites was restricted just as the study was preparing to begin enrollment. Here, we present multiple training protocol adaptations that were successfully implemented to facilitate remote research-related training. The A2CPS consortium includes 2 Multisite Clinical Centers (MCCs, 10 recruitment sites), a Clinical Coordinating Center (CCC), a Data Integration and Resource Center (DIRC), 3 Omics Data Generation Centers, and representation from the NIH Pain Consortium, Common Fund, and National Institute of Drug Abuse. The A2CPS is collecting candidate and exploratory biomarkers including pain, fatigue, function, sleep, psychosocial factors, quantitative sensory testing (QST), genomics, proteomics, metabolomics, lipidomics, and brain imaging. The CCC adapted the A2CPS training and evaluation techniques for certifying the MCCs to ensure competency with recruitment, assessments (surveys, QST, function), and data entry across clinical sites using a combination of virtual training sessions, standardized quantitative measurements, video demonstrations, and reliability assessments. Staff at data collection sites have been successfully certified in all psychophysical assessments (QST, function). This included use of stop watches and metronomes to ensure standard application rates, completion of application-rate and inter-rater-reliability worksheets at each clinical site, designation of site-specific master examiners, training rubrics and video demonstration to verify competency was harmonized across sites. Adaptation of training protocols to a remote format enabled initiation of subject enrollment while maintaining documented standards with high data completion rates for surveys and assessments. The A2CPS Consortium is supported by the National Institutes of Health Common Fund, which is managed by the OD/Office of Strategic Coordination (OSC). Consortium components include: Clinical Coordinating Center (UO1NS077179), Data Integration and Resource Center (UO1NS077352), Omics Data Generation Centers (U54DA049116, U54DA049115, U54DA09113), and Multisite Clinical Centers: MCC 1 (UM1NS112874) and MCC 2 (UM1NS118922). Postdoctoral support for GB provided by the National Institutes of Neurological Disease and Stroke (NINDS) of the NIH under Award Number U24NS112873-03S2.