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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
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RATIONALE: Communities of color are bearing a disproportionate burden of coronavirus disease 2019 (COVID-19) morbidity and mortality. Social determinants of health have resulted in higher prevalence and severity of COVID-19 among minority groups. Published work on COVID-19 disparities has focused on higher transmission, hospitalization, and mortality risk among people of color, but studies on disparities in the post-acute care setting are scarce. Our aim was to identify socioeconomic disparities in health resource utilization after hospital discharge. METHODS: This was a retrospective study. We identified adult patients who were hospitalized at CUIMC or the Allen Hospital from March 1st through April 30th 2020, had a positive RT-PCR for severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), developed severe hypoxemic respiratory failure requiring invasive mechanical ventilation, and were successfully discharged from the hospital without need for ventilator support. Patients who received a tracheostomy and were weaned off the ventilator prior to discharge were included. Exclusion criteria included transfer from or to another institution, prior tracheostomy, in-hospital death, and discharge with a ventilator. RESULTS: We identified 195 patients meeting inclusion criteria. The median age was 59 (IQR 47-67), and 135 (66.5%) were men. There were 25 (12.8%) patients who were uninsured and 116 (59.5%) patients who had public insurance. There were 121 (62%) Hispanic, 34 (17%) Black, and 18 (9%) White patients. Uninsured patients within our cohort were more likely to be Hispanic and Spanish-speaking (p=0.027;p<0.001, respectively). Uninsured patients were also more likely to be discharged to home (p<0.001) than to a rehabilitation facility. 8.8% of patients were readmitted to CUIMC within 30 days and 41.5% saw a medical provider at CUIMC within 30 days of discharge. Insurance status did not predict 30-day re-hospitalization or completion of outpatient follow-up, although our study was underpowered to answer these questions. CONCLUSION: Our study demonstrated that race/ethnicity and primary language are associated with insurance status with Hispanic and Spanish-speaking patients being more likely to be uninsured. Uninsured patients were more likely to be discharged home after hospitalization, rather than to facility for further care and rehabilitation. We did not demonstrate any short-term differences in 30-day re-hospitalization rates or follow-up visits but we suspect socioeconomic disparities represent a significant barrier to adequate follow-up care in the long term. We plan to investigate this further with longitudinal follow-up and survey data.
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Rationale: Higher levels of circulating interleukin-6 (IL-6) and lower respiratory system compliance have each been associated with increased mortality in severe coronavirus 2019 (COVID-19). IL-6 levels are associated with disease severity and mortality in non-COVID-19-related acute respiratory distress syndrome (ARDS). The purpose of this study was to examine the relationship between IL-6 and respiratory mechanics in COVID-19-related ARDS. Methods: This retrospective cohort study took place at two Columbia University Irving Medical Center hospitals. We identified patients age >18 years with laboratory confirmed COVID-19, who were intubated from March 1st through April 30th, 2020, and met the Berlin definition of ARDS. Electronic medical records were reviewed for clinical data. Outcomes were censored at 90 days after intubation. For patients without IL-6 levels recorded on the initial day of intubation, serum samples were obtained from the Columbia University Biobank and tested using the Quantikine Human IL-6 Immunoassay. IL-6 values were log-transformed. The primary outcome was respiratory system compliance. Secondary outcomes were calculated ventilatory ratio, PaO2:FiO2 ratio, and mortality. Linear regression and logistic regression were used for statistical analyses. Results: During the study period, 483 patients had COVID-19-associated ARDS. Median time of follow up was 37 days (IQR 11-90). At 90 days, 260 (53.8%) patients were deceased, 206 (42.7%) had been discharged, and 17 (3.5%) were still admitted. Two hundred sixteen (44.7%) patients had available data on respiratory system compliance and serum IL-6 levels from the initial day of mechanical ventilation. The median IL-6 value was 204.1 pg/ml (IQR 110-469.7). Median compliance was 25.5 ml/cmH2O (IQR 21.4-33.3), median ventilatory ratio was 1.96 (IQR 1.51-2.57), and median PaO2:FiO2 ratio was 134 (IQR 87-196). In unadjusted linear regression, higher IL-6 was associated with lower respiratory system compliance (log [IL-6] coefficient-1.80, p = 0.001) (Figure 1). This relationship remained significant when adjusting for age, sex, body mass index, race, ethnicity, and Sequential Organ Failure Assessment (SOFA) score (coefficient-2.43, p<0.001). There was no significant association between IL-6 and ventilatory ratio (0.76 p=0.08) or PaO2:FiO2 ratio (-6.15 p=0.06). Higher IL-6 was associated with higher odds of death at 90 days (OR 1.35 per unit increase in log [IL-6], p-value 0.022) when adjusting for age, sex, body mass index, race, ethnicity, and SOFA score. Conclusion: In COVID-19-associated ARDS, higher levels of IL-6 were associated with lower respiratory system compliance even adjusting for measured confounders. Higher IL-6 was also associated with higher mortality.
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Introduction: The SARS-CoV2 pandemic impacted numerous aspects of medical practice, including continuingmedical education. In-person and single-institution educational formats could not address the challenges of socialdistancing, heterogeneous regional experiences, and continuously emerging data. The vulnerability of cancerpatients to SARS-CoV2 added further urgency to overcoming these barriers. To fulfill these unmet educational andpatient care needs, we established a novel cross-institutional trainee-driven, on-line collaborative for the purpose ofgenerating multidisciplinary seminars on emerging best practices for the acute management of patients with SARS-CoV2. Methods: The COVID Learning Initiative is currently managed by clinical trainees and faculty from 13 institutionsacross 10 states. Weekly Zoom conferences were led by a rotating team consisting of 2-3 fellows overseen by 4-5expert faculty from throughout the country. Format consisted of two 15-minute instructional segments presented bytrainees, followed by a concluding 30-minute faculty Q&A panel moderated by a trainee. Attendees completedbaseline demographics, SARS-CoV2 experience surveys, and pre/post conference knowledge questions.Conferences were recorded and archived to enhance access and dissemination of knowledge. Results: Within 6 weeks and beginning just 2 weeks after inception we produced five 1-hour-longmultidisciplinary video conferences covering emerging antiviral therapies, coagulopathy, pulmonary complications, provider resilience, and ethics of resource scarcity. On average, there were 100 participants per seminar. Post-conference questioning consistently demonstrated acquisition of knowledge across topics and disciplines. Attendeesalso improved in their self-assessed comfort managing multidisciplinary aspects of SARS-CoV2. Overall, presentingcollaborations involved 11 fellows and 28 faculty representing 6 medical specialties and 17 institutions. Severalcollaborations persisted, resulting in further dissemination of knowledge with tangible outcomes such as generationof peer-reviewed manuscripts. Conclusions: The COVID Learning Initiative demonstrated a novel continuing medical education platform capableof rapidly disseminating knowledge at a national scale, while realizing new opportunities for remote traineementoring and skills development. With initial feasibility and continued interest among participating institutions, COVID Learning Initiative plans to evolve to Fellows ACHIEVE: Alliance for Collaborative Hematology OncologyInter-Institutional Education through Videoconferencing to conduct an extended multi-institutional educational serieson adapting cancer management and training program best practices.