P. Lyons, S. Bhavani, A. Michelson, T. Kannampallil, P. Sinha
{"title":"Predictors of Hospital Outcomes and Clinical Subphenotypes Differ Between COVID-19 and Influenza Pneumonia","authors":"P. Lyons, S. Bhavani, A. Michelson, T. Kannampallil, P. Sinha","doi":"10.1164/AJRCCM-CONFERENCE.2021.203.1_MEETINGABSTRACTS.A1060","DOIUrl":"https://doi.org/10.1164/AJRCCM-CONFERENCE.2021.203.1_MEETINGABSTRACTS.A1060","url":null,"abstract":"Introduction: Pneumonia due to SARS-CoV-2 (Coronavirus Disease 2019, COVID-19) has frequently been compared to other viral pneumonias, including influenza. While some data suggest significant differences in biological responses, dissimilarities in the clinical course and characteristics between SARS-COV-2 and influenza pneumonia remain unknown. We evaluated differences in clinical predictors of outcomes and early clinical subphenotypes in COVID-19 and influenza pneumonia. Methods: We performed a retrospective cohort study of all patients hospitalized for > 24 hours, requiring oxygen support, at Barnes-Jewish Hospital with COVID-19 (March-July 2020) or influenza (Jan 2012-Dec 2018). In-hospital mortality or hospice discharge was the primary outcome. First, supervised machine learning classifier models (XGBoost) were trained using bootstrap replications of each viral cohort to predict the primary outcome. 28 candidate predictor variables among the most extreme vital signs and laboratory values within 24 hours of hospitalization were preselected, excluding highly correlated variables. We compared each model's internal discrimination to its performance in the alternate cohort and evaluated differences in variable importance between the two viral pneumonia models. Next, we evaluated differences in clinical subphenotypes in two ways: 1) a previously-validated algorithm to group patients into four distinct subphenotypes based on temperature trajectories within 72 hours of hospitalization;2) latent class analysis (LCA) to identify unmeasured subgroups within each viral cohort based on the predictor variables described above. In both analyses, we compared frequency of subphenotype membership and each subphenotype's primary outcome between viral cohorts. Results: We evaluated 321 unique hospitalizations with COVID-19 and 535 with influenza. The primary outcome was experienced in 23% and 9.5% of patients, respectively. Influenza predictor model discriminated outcomes worse in COVID-19 than on internal evaluation (Panel A), suggesting prognostic variables differ between the viral pneumonias. Only one of the top five contributory variables was shared between the two models (Panel B). Prevalences of temperature trajectory subphenotype also differed significantly between viral pneumonias. All COVID-19 temperature trajectory subphenotypes experienced the primary outcome more frequently than their influenza counterparts (Panel C). LCA identified two distinct classes in each cohort, with each viral pneumonia's minority class experiencing worse outcomes than the majority class. Of each model's top 5 classdefining variables, only 2 were shared (Panel D). Conclusions: COVID-19 and influenza pneumonia differ markedly in predictors of outcome and in clinical subphenotypes. These findings emphasize observable pathogen-specific differential responses in viral pneumonias and suggest that distinct management approaches should be investigated for these diseases. (Table P","PeriodicalId":7087,"journal":{"name":"A13. A013 ARDS IN THE TIME OF COVID-19","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75013284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Bassi, J. Suen, H. Dalton, N. White, A. Barnett, A. Corley, Samuel Hinton, Simon Forsyth, J. Laffey, D. Brodie, A. Burrell, E. Fan, R. Bartlett, A. Torres, D. Chiumello, A. Elhazmi, C. Hodgson, S. Ichiba, C. Luna, S. Murthy, A. Nichol, P. Ng, Mark T. Ogino, J. Fraser
{"title":"Factors Associated with Mortality in Patients with COVID-19 Requiring Mechanical Ventilation: An International Cohort Study from 139 Intensive Care Unit Across 6 Continents","authors":"G. Bassi, J. Suen, H. Dalton, N. White, A. Barnett, A. Corley, Samuel Hinton, Simon Forsyth, J. Laffey, D. Brodie, A. Burrell, E. Fan, R. Bartlett, A. Torres, D. Chiumello, A. Elhazmi, C. Hodgson, S. Ichiba, C. Luna, S. Murthy, A. Nichol, P. Ng, Mark T. Ogino, J. Fraser","doi":"10.1164/AJRCCM-CONFERENCE.2021.203.1_MEETINGABSTRACTS.A1059","DOIUrl":"https://doi.org/10.1164/AJRCCM-CONFERENCE.2021.203.1_MEETINGABSTRACTS.A1059","url":null,"abstract":"Rationale: Patients with COVID-19 commonly develop severe hypoxemic respiratory failure and require invasive mechanical ventilation (MV). The disease burden and predictors of mortality in this population remain uncertain. Methods: Prospective observational cohort study from 139 intensive care units of the international COVID-19 Critical Care Consortium. Patients enrolled from January 14th through November 31st 2020 were included in the analysis. Patient's characteristics and clinical data were assessed. Multivariable Cox proportional hazards analysis was conducted to identify indipendent predictors of mortality within 28 days from commencement of MV. Results: 1578 patients on MV were included into the analysis. Mean±SD age was 59 years±13 and patients were predominantly males (66%). 542 Patients (34.4%) died within 28 days from commencement of MV. Nonsurvivors were slightly older (mean age±SD 62±13 vs. 59±13) and presented more frequently hypertension, chronic cardiac disease and diabetes. Median (IQR) PaO2/FiO2 upon commencement of MV was 96 (68-135) and 111 (81-173) in patients who did not survive vs. survivors, respectively (p=0.04). ECMO (13% vs 25%, p<0.01), inhaled nitric oxide (11% vs 15%, p=0.02) and recruitment manoeauvres (26% vs 31%, p<0.01) were used less frequently in patients who did not survive. Independent risk factors associated with 28-day mortality included age older than 70 years (hazard ratio [HR], 2.83;95% CI, 1.32-6.07), higher creatinine levels upon ICU admission (HR, 1.20;95% CI, 1.03-1.40), and lower pH within 24h from commencement of MV (HR, 0.12;95% CI, 0.02-0.62), while a shorter period (day) from early symptoms to hospitalisation reduced mortality risks (HR, 0.96;95% CI, 0.93-0.99). Conclusions: Our findings from a large international cohort of critically-ill COVID-19 patients on mechanical ventilation emphasises that elderly patients, not promptly admitted to the hospital, and who present higher creatinine levels and acidosis are at higher risk of mortality.","PeriodicalId":7087,"journal":{"name":"A13. A013 ARDS IN THE TIME OF COVID-19","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85518337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Bhatraju, E. Morrell, L. Zelnick, N. Sathe, Xin-Ya Chai, S. Sahi, A. Sader, D. Lum, T. Liu, N. Koetje, A. Garay, E. Barnes, J. Lawson, G. Cromer, M. Bray, S. Pipavath, B. Kestenbaum, C. Liles, S. Fink, T. West, Laura E. Evans, C. Mikacenic, M. Wurfel
{"title":"Comparison of Host Endothelial, Epithelial and Inflammatory Response in ICU Patients With and Without COVID-19","authors":"P. Bhatraju, E. Morrell, L. Zelnick, N. Sathe, Xin-Ya Chai, S. Sahi, A. Sader, D. Lum, T. Liu, N. Koetje, A. Garay, E. Barnes, J. Lawson, G. Cromer, M. Bray, S. Pipavath, B. Kestenbaum, C. Liles, S. Fink, T. West, Laura E. Evans, C. Mikacenic, M. Wurfel","doi":"10.1164/AJRCCM-CONFERENCE.2021.203.1_MEETINGABSTRACTS.A1063","DOIUrl":"https://doi.org/10.1164/AJRCCM-CONFERENCE.2021.203.1_MEETINGABSTRACTS.A1063","url":null,"abstract":"Rationale: Analyses of blood biomarkers involved in the host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral infection can reveal distinct biological pathways and inform development and testing of therapeutics for COVID-19. Objective: To evaluate host endothelial, epithelial and inflammatory biomarkers in COVID-19Methods: We prospectively enrolled 169 ICU patients with suspicion of COVID-19 infection, including 78 (46%) patients positive and 91 (54%) negative for SARS-CoV-2 infection from April to September, 2020. We compared 22 plasma biomarkers in blood collected within 24 hours and 3 days after ICU admission. Measurement and Main Results: ICU patients with and without COVID-19 had similar rates of severe acute kidney injury, shock, thromboembolism and in-hospital mortality. Rates of ARDS were higher in COVID-19 (aRR = 5.9, 95% CI: 3.2-11.0). While concentrations of interleukin 6 and 8 were not different between groups, markers of epithelial cell injury (soluble receptor for advanced glycation end products, sRAGE) and acute phase proteins (serum amyloid A, SAA) were significantly higher in COVID-19 compared to non-COVID-19, adjusting for demographics and APACHE III scores (Figure 1). In contrast, angiopoietin 2:1 (Ang-2:1 ratio) and soluble tumor necrosis factor receptor 1 (sTNFR-1), markers of endothelial dysfunction and inflammation, were significantly lower in COVID-19 (Bonferroni corrected p<0.002). Ang-2:1 ratio and SAA were associated with mortality only in non-COVID-19 patients.Conclusions: These studies demonstrate that, unlike other well-studied causes of critical illness, endothelial dysfunction is not characteristic of severe COVID-19 early after ICU admission. Pathways resulting in elaboration of acute phase proteins and inducing epithelial cell injury may be promising targets for therapeutics. 2 (Table Presented).","PeriodicalId":7087,"journal":{"name":"A13. A013 ARDS IN THE TIME OF COVID-19","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90866239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Drohan, Hang Yang, C. Schaefer, N. Bensen, M. Lu, B. Methé, S. Qin, Y. Zhang, G. Lisius, W. Bain, F. Shah, B. McVerry, A. Morris, G. Kitsios
{"title":"Comparison of Host-Response Biomarkers and Inflammatory Subphenotypes in COVID-19 Without ARDS, COVID-ARDS, and Non-COVID-ARDS","authors":"C. Drohan, Hang Yang, C. Schaefer, N. Bensen, M. Lu, B. Methé, S. Qin, Y. Zhang, G. Lisius, W. Bain, F. Shah, B. McVerry, A. Morris, G. Kitsios","doi":"10.1164/AJRCCM-CONFERENCE.2021.203.1_MEETINGABSTRACTS.A1062","DOIUrl":"https://doi.org/10.1164/AJRCCM-CONFERENCE.2021.203.1_MEETINGABSTRACTS.A1062","url":null,"abstract":"Rationale: A dysregulated host inflammatory response in COVID-19 is considered a central pathogenetic mechanism of acute lung injury and extrapulmonary end-organ damage. However, limited comparative data are available as to whether the host-response in COVID-19 ARDS differs from patients with other (non-COVID) ARDS etiologies, and how such differences may inform targeted immunomodulating therapeutics. Methods: We prospectively enrolled 36 intubated patients with COVID-19 ARDS and 70 hospitalized non-intubated patients with COVID-19 (COVID-19 non-ARDS), and compared them with a pre-COVID-19 cohort of patients with bacterial (n=21), viral (n=14), and culture-negative ARDS (n=30). We measured 10 host-response biomarkers of innate immunity and epithelial/endothelial injury (IL-6, IL-8, IL-10, RAGE, TNFR1, Angiopoeitin-2, Procalcitonin, Fractalkine, Pentraxin-3, ST2) in plasma. Using a 4-variable predictive model (TNFR1, Angiopoeitin-2, Procalcitonin and bicarbonate levels), we classified patients into hyper-vs. hypo-inflammatory subphenotypes. We compared biomarker levels, subphenotypes and outcomes between the clinical groups. Results: Host-response biomarker levels were widely distributed between the 5 groups, with a characteristic pattern for IL-6, IL-8, Angiopoeitin-2, Procalcitonin, ST-2 and fractalkine: COVID-19 ARDS patients had higher biomarker levels than COVID-19 non-ARDS (p<0.01), lower levels than bacterial or culture-negative ARDS (p<0.01), and similar levels to viral ARDS (Figure 1A example for IL-6). A lower proportion of the COVID-19 ARDS cohort was classified in the adverse hyper-inflammatory subphenotype (15%) compared to bacterial (47%) and culture-negative ARDS (31%) (Figure 1B). Despite the lower level of inflammatory host responses, COVID-ARDS patients had longer median duration of mechanical ventilation (20.5 [10.0-40.8] days) compared to bacterial (8.0 [5.0-25.0]), culture-negative (7.0 [5.2-9.8]) and viral ARDS (7.5 [3.5-14.8]) (p<0.01). Patients with COVID-19 but without ARDS had lower 30-day mortality (6%) compared to patients with ARDS from COVID-19 (31%) or other etiologies (bacterial 33%, culture-negative 40% and viral 21%). Conclusion: Development of ARDS from COVID-19 is characterized by intensified inflammation compared to hospitalized COVID-19 patients not requiring mechanical ventilation. Compared to ARDS from other etiologies, host-response inflammatory profiles in COVID-19 ARDS appear similar to other viral etiologies of ARDS, and are lower compared to bacterial or culture-negative ARDS. The etiology of worse clinical outcomes of COVID-19 ARDS despite the lower frequency of the prognostically adverse hyper-inflammatory subphenotype warrant urgent investigation. (Table Presented).","PeriodicalId":7087,"journal":{"name":"A13. A013 ARDS IN THE TIME OF COVID-19","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86603221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Leisman, A. Mehta, N. Hacohen, M. Filbin, M. Goldberg
{"title":"Trajectories of Pulmonary Epithelial and Endothelial Injury Markers in COVID-19 Patients Requiring Respiratory Support at Presentation","authors":"D. Leisman, A. Mehta, N. Hacohen, M. Filbin, M. Goldberg","doi":"10.1164/AJRCCM-CONFERENCE.2021.203.1_MEETINGABSTRACTS.A1061","DOIUrl":"https://doi.org/10.1164/AJRCCM-CONFERENCE.2021.203.1_MEETINGABSTRACTS.A1061","url":null,"abstract":"RATIONALE:Acute respiratory distress syndrome (ARDS) phenotypes differ by pulmonary epithelial vs. endothelial injury marker predominance. Epithelial vs. endothelial injury patterns in severe SARS-CoV-2 infection have not been directly compared. METHODS:Adult patients presenting to a single ED in Boston from 3/24-4/30/20 were enrolled. Inclusion criteria: clinical concern for COVID-19 ARDS and 1) respirations ≥22/minute or 2) SpO2≤92% on room air or 3) respiratory support. For this study, we excluded patients without subsequently polymerase chain reaction-confirmed COVID-19 or without supplemental oxygen or invasive mechanical ventilation (IMV) at presentation (non-invasive mechanical ventilation for COVID-19 was against hospital policy during enrollment). On Day=0, 3, and 7, patients had dedicated research blood draws and detailed clinical data were recorded. Data included clinical/respiratory status using the World Health Organization (WHO)-scale, and non-pulmonary (renal, cardiovascular, and coagulation) dysfunctions. Clinical status on Day=28 was also recorded. Blood was analyzed using the Olink Proximity Extension Assay, an oligonucleotide-labelled antibody assay that provides high-specificity analysis of plasma proteins, including low abundance proteins. Targets included markers of epithelial injury (n=5), endothelial activation and injury (n=11), and inflammatory cytokines (interleukin-6, interleukin-8, soluble-Tumor Necrosis Factor Receptor-1 (sTNF-R1). We used multivariable mixed-effects generalized linear models to determine associations between biomarker and clinical status trajectories. Multivariable proportional-odds models measured associations between biomarker trajectories with 28-day outcome. Models were adjusted for age, sex, BMI, heart, lung, and renal comorbidities, and initial Sequential Organ-Failure Assessment score. RESULTS:Figure-A shows (n=225) patients' clinical status over time. At Day=0, epithelial injury markers were higher in patients requiring IMV vs. supplemental oxygen and decreased over time independent of respiratory status (Figure-B). They did not discriminate renal, cardiovascular, or coagulation dysfunctions. In contrast, endothelial markers were initially lower for IMV than supplemental oxygen patients;they fell over time in lower severity patients but rose sharply in IMV patients (Figure-C). Endothelial markers discriminated patients with non-pulmonary organ dysfunction from those without. More endothelial (8/11, 73%) than epithelial (1/5, 20%) markers were significantly associated with worse 28-day outcome (Figure-E). Change from Day=0 to Day=3 was significantly associated with 28-day WHO-scale for all 11 (100%) endothelial vs. 3/5 (60%) epithelial markers. Endothelial effect-sizes were substantially larger (median odds-ratio:3.60 vs. 1.58). CONCLUSIONS:In COVID-19 patients with respiratory distress, endothelial markers are more strongly associated with clinical progression, non-pulmonary organ dysfunctio","PeriodicalId":7087,"journal":{"name":"A13. A013 ARDS IN THE TIME OF COVID-19","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86697154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}