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Background: Levels of plasma SARS-CoV-2 nucleocapsid (N) antigen may be an important biomarker in patients with COVID-19 and enhance our understanding of the pathogenesis of COVID-19.Objective: To evaluate whether levels of plasma antigen can predict short-term clinical outcomes and identify clinical and viral factors associated with plasma antigen levels in hospitalized patients with SARS-CoV-2.Design: Cross-sectional study of baseline plasma antigen level from 2540 participants enrolled in the TICO (Therapeutics for Inpatients With COVID-19) platform trial from August 2020 to November 2021, with additional data on day 5 outcome and time to discharge.Setting: 114 centers in 10 countries.Participants: Adults hospitalized for acute SARS-CoV-2 infection with 12 days or less of symptoms.Measurements: Baseline plasma viral N antigen level was measured at a central laboratory. Delta variant status was determined from baseline nasal swabs using reverse transcriptase polymerase chain reaction. Associations between baseline patient characteristics and viral factors and baseline plasma antigen levels were assessed using both unadjusted and multivariable modeling. Association between elevated baseline antigen level of 1000 ng/L or greater and outcomes, including worsening of ordinal pulmonary scale at day 5 and time to hospital discharge, were evaluated using logistic regression and Fine-Gray regression models, respectively.Results: Plasma antigen was below the level of quantification in 5% of participants at enrollment, and 1000 ng/L or greater in 57%. Baseline pulmonary severity of illness was strongly associated with plasma antigen level, with mean plasma antigen level 3.10-fold higher among those requiring noninvasive ventilation or high-flow nasal cannula compared with room air (95% CI, 2.22 to 4.34). Plasma antigen level was higher in those who lacked antispike antibodies (6.42 fold; CI, 5.37 to 7.66) and in those with the Delta variant (1.73 fold; CI, 1.41 to 2.13). Additional factors associated with higher baseline antigen level included male sex, shorter time since hospital admission, decreased days of remdesivir, and renal impairment. In contrast, race, ethnicity, body mass index, and immunocompromising conditions were not associated with plasma antigen levels. Plasma antigen level of 1000 ng/L or greater was associated with a markedly higher odds of worsened pulmonary status at day 5 (odds ratio, 5.06 [CI, 3.41 to 7.50]) and longer time to hospital discharge (median, 7 vs. 4 days; subhazard ratio, 0.51 [CI, 0.45 to 0.57]), with subhazard ratios similar across all levels of baseline pulmonary severity.Limitations: Plasma samples were drawn at enrollment, not hospital presentation. No point-of-care test to measure plasma antigen is currently available.Conclusion: Elevated plasma antigen is highly associated with both severity of pulmonary illness and clinically important patient outcomes. Multiple clinical and viral factors are associated with plasma antigen level at presentation. These data support a potential role of ongoing viral replication in the pathogenesis of SARS-CoV-2 in hospitalized patients.Primary Funding Source: U.S. government Operation Warp Speed and National Institute of Allergy and Infectious Diseases. [ABSTRACT FROM AUTHOR]

Introduction: Acute respiratory failure requiring mechanical ventilation is a leading cause of mortality in the intensive care unit. Although single peripheral blood oxygen saturation/fraction of inspired oxygen (SpO2/FiO2) ratios of hypoxemia have been evaluated to risk-stratify patients with acute respiratory distress syndrome, the utility of longitudinal SpO2/FiO2 ratios is unknown. Objective: To assess time-based SpO2/FiO2 ratios ≤ 150--Sp02/ FiO2 time at risk (SF-TAR)--for predicting mortality in mechanically ventilated patients. Methods: Retrospective, observational cohort study of mechanically ventilated patients at 21 community and 2 academic hospitals. Association between the SF-TAR in the first 24 hours of ventilation and mortality was examined using multivariable logistic regression and compared with the worst recorded isolated partial pressure of arterial oxygen/fraction of inspired oxygen (P/F) ratio. Results: In 28,758 derivation cohort admissions, every 10% increase in SF-TAR was associated with a 24% increase in adjusted odds of hospital mortality (adjusted odds ratio = 1.24; 95% confidence interval [CI] = 1.23-1.26); a similar association was observed in validation cohorts. Discrimination for mortality modestly improved with SF-TAR (area under the receiver operating characteristic curve [AUROC] = 0.81; 95% CI = 0.81-0.82) vs the worst P/F ratio (AUROC = 0.78; 95% CI = 0.78-0.79) and worst SpO2/FiO2 ratio (AUROC = 0.79; 95% CI = 0.79-0.80). The SF-TAR in the first 6 hours offered comparable discrimination for hospital mortality (AUROC = 0.80; 95% CI = 0.79-0.80) to the 24-hour SF-TAR. Conclusion: The SF-TAR can identify ventilated patients at increased risk of death, offering modest improvements compared with single SpO2/FiO2 and P/F ratios. This longitudinal, noninvasive, and broadly generalizable tool may have particular utility for early phenotyping and risk stratification using electronic health record data in ventilated patients. [ABSTRACT FROM AUTHOR]

Objective: A high plasma level of inflammasome mediator interleukin-18 was associated with mortality in observational acute respiratory distress syndrome cohorts. Statin exposure increases both inflammasome activation and lung injury in mouse models. We tested whether randomization to statin therapy correlated with increased interleukin-18 in the ARDS Network Statins for Acutely Injured Lungs from Sepsis trial.Design: Retrospective analysis of randomized controlled clinical trial.Setting: Multicenter North American clinical trial, the ARDS Network Statins for Acutely Injured Lungs from Sepsis.Patients: Six hundred eighty-three subjects with infection-related acute respiratory distress syndrome, representing 92% of the original trial population.Interventions: Random assignment of rosuvastatin or placebo for up to 28 days or 3 days after ICU discharge.Measurements and Main Results: We measured plasma interleukin-18 levels in all Statins for Acutely Injured Lungs from Sepsis patients with sample available at day 0 (baseline, n = 683) and day 3 (after randomization, n = 588). We tested the association among interleukin-18 level at baseline, rising interleukin-18, and the impact of statin therapy on 60-day mortality, adjusting for severity of illness. Baseline plasma interleukin-18 level greater than or equal to 800 pg/mL was highly associated with 60-day mortality, with a hazard of death of 2.3 (95% CI, 1.7-3.1). Rising plasma interleukin-18 was also associated with increased mortality. For each unit increase in log2 (interleukin-18) at day 3 compared with baseline, the hazard of death increased by 2.3 (95% CI, 1.5-3.5). Subjects randomized to statin were significantly more likely to experience a rise in plasma interleukin-18 levels. Subjects with acute kidney injury, shock, low baseline interleukin-18, and those not receiving systemic corticosteroids were more likely to experience rising interleukin-18. Randomization to statin therapy was associated with rising in interleukin-18 in all of those subsets, however.Conclusions: Elevated baseline plasma interleukin-18 was associated with higher mortality in sepsis-induced acute respiratory distress syndrome. A rise in plasma interleukin-18 was also associated with increased mortality and was more common in subjects randomized to statin therapy in this clinical trial. [ABSTRACT FROM AUTHOR]

There is considerable biological and physiological heterogeneity among patients who meet standard clinical criteria for acute respiratory distress syndrome (ARDS). In this study, we tested the hypothesis that there exists a subgroup of ARDS patients who exhibit a metabolically distinct profile. We examined undiluted pulmonary edema fluid obtained at the time of endotracheal intubation from 16 clinically phenotyped ARDS patients and 13 control patients with hydrostatic pulmonary edema. Nontargeted metabolic profiling was carried out on the undiluted edema fluid. Univariate and multivariate statistical analyses including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were conducted to find discriminant metabolites. Seven-hundred and sixty unique metabolites were identified in the pulmonary edema fluid of these 29 patients. We found that a subset of ARDS patients (6/16, 38%) presented a distinct metabolic profile with the overrepresentation of 235 metabolites compared with edema fluid from the other 10 ARDS patients, whose edema fluid metabolic profile was indistinguishable from those of the 13 control patients with hydrostatic edema. This “high metabolite” endotype was characterized by higher concentrations of metabolites belonging to all of the main metabolic classes including lipids, amino acids, and carbohydrates. This distinct group with high metabolite levels in the edema fluid was also associated with a higher mortality rate. Thus metabolic profiling of the edema fluid of ARDS patients supports the hypothesis that there is considerable biological heterogeneity among ARDS patients who meet standard clinical and physiological criteria for ARDS. [ABSTRACT FROM AUTHOR]

Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis-acting eQTLs (SNPs within 100kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues (n = 586). At a false discovery rate < 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes (P < 10−04), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2–2.0, P < 10−11) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5–2.3, P < 10−11). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3–10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization. [ABSTRACT FROM AUTHOR]

A better understanding of the pathogenesis and the resolution of the acute respiratory distress syndrome (ARDS) is needed. Although some progress has been made with the use of protein biomarkers and candidate gene studies in understanding the pathobiology of ARDS, we propose that new studies that measure the chemical breakdown products of cellular metabolism (metabolomics) may provide new insights into ARDS, in part because metabolomics targets a later point in the genomics cascade than is possible with studies of DNA, RNA, and protein biomarkers. Technological advances have made largescale metabolomic profiling increasingly feasible. Metabolomic approaches have already achieved novel insights in nonpulmonary diseases such as diabetes mellitus and malignancy, as well as in sepsis, a major risk factor for developing ARDS. Metabolomic profiling is a promising approach to identify novel pathways in both patients at risk for developing ARDS as well as in the early phase of established ARDS. [ABSTRACT FROM AUTHOR]

Objective: To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. Rationale: Comprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible. Methods: We performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study. Results: We tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations). Conclusion: Both individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients. [ABSTRACT FROM AUTHOR]

Abstract: Structural genetic variation, including copy-number variation (CNV), constitutes a substantial fraction of total genetic variability and the importance of structural genetic variants in modulating human disease is increasingly being recognized. Early successes in identifying disease-associated CNVs via a candidate gene approach mandate that future disease association studies need to include structural genetic variation. Such analyses should not rely on previously developed methodologies that were designed to evaluate single nucleotide polymorphisms (SNPs). Instead, development of novel technical, statistical, and epidemiologic methods will be necessary to optimally capture this newly-appreciated form of genetic variation in a meaningful manner. [Copyright &y& Elsevier]

Background: Metabolic predictors and potential mediators of survival in sepsis have been incompletely characterized. We examined whether machine learning (ML) tools applied to the human plasma metabolome could consistently identify and prioritize metabolites implicated in sepsis survivorship, and whether these methods improved upon conventional statistical approaches. Methods: Plasma gas chromatography–liquid chromatography mass spectrometry quantified 411 metabolites measured ≤ 72 h of ICU admission in 60 patients with sepsis at a single center (Brigham and Women's Hospital, Boston, USA). Seven ML approaches were trained to differentiate survivors from non-survivors. Model performance predicting 28 day mortality was assessed through internal cross-validation, and innate top-feature (metabolite) selection and rankings were compared across the 7 ML approaches and with conventional statistical methods (logistic regression). Metabolites were consensus ranked by a summary, ensemble ML ranking procedure weighing their contribution to mortality risk prediction across multiple ML models. Results: Median (IQR) patient age was 58 (47, 62) years, 45% were women, and median (IQR) SOFA score was 9 (6, 12). Mortality at 28 days was 42%. The models' specificity ranged from 0.619 to 0.821. Partial least squares regression-discriminant analysis and nearest shrunken centroids prioritized the greatest number of metabolites identified by at least one other method. Penalized logistic regression demonstrated top-feature results that were consistent with many ML methods. Across the plasma metabolome, the 13 metabolites with the strongest linkage to mortality defined through an ensemble ML importance score included lactate, bilirubin, kynurenine, glycochenodeoxycholate, phenylalanine, and others. Four of these top 13 metabolites (3-hydroxyisobutyrate, indoleacetate, fucose, and glycolithocholate sulfate) have not been previously associated with sepsis survival. Many of the prioritized metabolites are constituents of the tryptophan, pyruvate, phenylalanine, pentose phosphate, and bile acid pathways. Conclusions: We identified metabolites linked with sepsis survival, some confirming prior observations, and others representing new associations. The application of ensemble ML feature-ranking tools to metabolomic data may represent a promising statistical platform to support biologic target discovery. [ABSTRACT FROM AUTHOR]

Background: Metabolic predictors and potential mediators of survival in sepsis have been incompletely characterized. We examined whether machine learning (ML) tools applied to the human plasma metabolome could consistently identify and prioritize metabolites implicated in sepsis survivorship, and whether these methods improved upon conventional statistical approaches. Methods: Plasma gas chromatography–liquid chromatography mass spectrometry quantified 411 metabolites measured ≤ 72 h of ICU admission in 60 patients with sepsis at a single center (Brigham and Women's Hospital, Boston, USA). Seven ML approaches were trained to differentiate survivors from non-survivors. Model performance predicting 28 day mortality was assessed through internal cross-validation, and innate top-feature (metabolite) selection and rankings were compared across the 7 ML approaches and with conventional statistical methods (logistic regression). Metabolites were consensus ranked by a summary, ensemble ML ranking procedure weighing their contribution to mortality risk prediction across multiple ML models. Results: Median (IQR) patient age was 58 (47, 62) years, 45% were women, and median (IQR) SOFA score was 9 (6, 12). Mortality at 28 days was 42%. The models' specificity ranged from 0.619 to 0.821. Partial least squares regression-discriminant analysis and nearest shrunken centroids prioritized the greatest number of metabolites identified by at least one other method. Penalized logistic regression demonstrated top-feature results that were consistent with many ML methods. Across the plasma metabolome, the 13 metabolites with the strongest linkage to mortality defined through an ensemble ML importance score included lactate, bilirubin, kynurenine, glycochenodeoxycholate, phenylalanine, and others. Four of these top 13 metabolites (3-hydroxyisobutyrate, indoleacetate, fucose, and glycolithocholate sulfate) have not been previously associated with sepsis survival. Many of the prioritized metabolites are constituents of the tryptophan, pyruvate, phenylalanine, pentose phosphate, and bile acid pathways. Conclusions: We identified metabolites linked with sepsis survival, some confirming prior observations, and others representing new associations. The application of ensemble ML feature-ranking tools to metabolomic data may represent a promising statistical platform to support biologic target discovery. [ABSTRACT FROM AUTHOR]

Specifically, rosuvastatin (used in the SAILS trial) is one of the more hydrophilic statins, and the hydrophilic statin pravastatin was associated with inflammasome activation enhanced bleomycin-induced lung inflammation in mice ([7]). Importantly, we view our results as a provocative hypothesis-generating study to facilitate future precision medicine-targeted treatment approaches for sepsis and ARDS. 2 Rogers AJ, Guan J, Trtchounian A. Association of elevated plasma interleukin-18 level with increased mortality in a clinical trial of statin treatment for acute respiratory distress syndrome. [Extracted from the article]

A table of contents for the issue is presented.

OBJECTIVES: Interleukin-18 (IL-18) plasma level and latent class analysis (LCA) have separately been shown to predict prognosis and treatment response in acute respiratory distress syndrome (ARDS). IL-18 is a measure of inflammasome activation, a pathway potentially distinct from inflammation captured by biomarkers defining previously published LCA classes. We hypothesized that elevated IL-18 would identify distinct “high-risk” patients not captured by prior LCA classifications. DESIGN: Statins for acutely injured lungs from sepsis (SAILS) and hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in acute lung injury to reduce pulmonary dysfunction trial (HARP-2) are two large randomized, controlled trials in ARDS in which both LCA assignments and IL-18 levels were shown to predict mortality. We first evaluated the overlap between high IL-18 levels (≥ 800 pg/mL) with prior LCA class assignments using McNemar’s test and then tested the correlation between IL-18 and LCA biomarkers using Pearson’s exact test on log-2 transformed values. Our primary analysis was the association of IL-18 level with 60-day mortality in the hypoinflammatory LCA class, which was assessed using the Fisher exact test and Cox proportional hazards modeling adjusting for age, Acute Physiology and Chronic Health Evaluation score, and gender. Secondary analyses included the association of IL-18 and LCA with mortality within each IL-18/LCA subgroup. SETTING: Secondary analysis of two multicenter, randomized controlled clinical trials of ARDS patients. SUBJECTS: Six hundred eighty-three patients in SAILS and 511 patients in HARP-2. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We found that 33% of patients in SAILS and HARP-2 were discordant by IL-18 level and LCA class. We further found that IL-18 level was only modestly correlated (0.17–0.47) with cytokines used in the LCA assignment. A substantial subset of individuals classified as hypoinflammatory by LCA (14% of SAILS and 43% of HARP-2) were classified as high risk by elevated IL-18. These individuals were at high risk for mortality in both SAILS (42% 60-d mortality, odds ratio [OR] 3.3; 95% CI, 1.8–6.1; p < 0.001) and HARP-2 (27% 60-d mortality, OR 2.1; 95% CI, 1.2–3.8; p = 0.009). CONCLUSIONS: Plasma IL-18 level provides important additional prognostic information to LCA subphenotypes defined largely by traditional inflammatory biomarkers in two large ARDS cohorts. [ABSTRACT FROM AUTHOR]

Determinants of Post-Acute Sequelae of COVID-19 are not known. Here we show that 83.3% of patients with viral RNA in blood (RNAemia) at presentation were symptomatic in the post-acute phase. RNAemia at presentation successfully predicted PASC, independent of patient demographics, worst disease severity, and length of symptoms. [ABSTRACT FROM AUTHOR]

The article discusses a study by D. J. Kor and colleagues which examines the accuracy of early warning identification systems, pulmonary vascular permeability index (PVPI) and peak extravascular lung water (EVLWi), in identifying early acute respiratory distress syndrome (ARDS). It says that PVPI and EVLWi were examined as markers of pulmonary edema in patients undergoing high-risk cardiac surgery. It discusses how area under curve (AUC) compare to identification methods for high-risk patients.

Background The vast majority of coronavirus disease 2019 (COVID-19) disease occurs in outpatients where treatment is limited to antivirals for high-risk subgroups. Acebilustat, a leukotriene B4 inhibitor, has potential to reduce inflammation and symptom duration. Methods In a single-center trial spanning Delta and Omicron variants, outpatients were randomized to 100 mg/d of oral acebilustat or placebo for 28 days. Patients reported daily symptoms via electronic query through day 28 with phone follow-up on day 120 and collected nasal swab samples on days 1–10. The primary outcome was sustained symptom resolution to day 28. Secondary 28-day outcomes included time to first symptom resolution, area under the curve (AUC) for longitudinal daily symptom scores, duration of viral shedding through day 10, and symptoms on day 120. Results Sixty participants were randomized to each study arm. At enrollment, the median duration was 4 days (interquartile range, 3–5 days), and the median number of symptoms was 9 (7–11). Most patients (90%) were vaccinated, with 73% having neutralizing antibodies. A minority of participants (44%; 35% in the acebilustat arm and 53% in placebo) had sustained symptom resolution at day 28 (hazard ratio, 0.6 [95% confidence interval,.34–1.04]; P =.07 favoring placebo). There was no difference in the mean AUC for symptom scores over 28 days (difference in mean AUC, 9.4 [95% confidence interval, −42.1 to 60.9]; P =.72). Acebilustat did not affect viral shedding or symptoms at day 120. Conclusions Sustained symptoms through day 28 were common in this low-risk population. Despite this, leukotriene B4 antagonism with acebilustat did not shorten symptom duration in outpatients with COVID-19. Clinical Trials Registration. NCT04662060. [ABSTRACT FROM AUTHOR]

Using latent class analysis (LCA) of clinical and protein biomarkers, researchers have identified two phenotypes of the acute respiratory distress syndrome (ARDS) with divergent clinical trajectories and treatment responses. We investigated whether plasma metabolites differed among patients with LCA-derived hyperinflammatory and hypoinflammatory ARDS, and we tested the prognostic utility of adding metabolic clusters to LCA phenotypes. We analyzed data from 93 patients with ARDS and sepsis enrolled in a multicenter prospective cohort of critically ill patients, comparing 970 metabolites between the two LCA-derived phenotypes. In all, 188 metabolites were differentially abundant between the two LCA-derived phenotypes. After adjusting for age, sex, confounding medications, and comorbid liver and kidney disease, 82 metabolites remained significantly different. Patients with hyperinflammatory ARDS had reduced circulating lipids but high levels of pyruvate, lactate, and malate. Metabolic cluster and LCA-derived phenotypes were each significantly and independently associated with survival. Patients with hyperinflammatory ARDS may be experiencing a glycolytic shift leading to dysregulated lipid metabolism. Metabolic profiling offers prognostic information beyond what is captured by LCA phenotypes alone. Deeper biological profiling may identify key differences in pathogenesis among patients with ARDS and may lead to novel targeted therapies. [ABSTRACT FROM AUTHOR]

Introduction: We describe a protocol for FIRE CORAL, an observational cohort study that examines the recovery from COVID-19 disease following acute hospitalization with an emphasis on functional, imaging, and respiratory evaluation. Methods and analysis: FIRE CORAL is a multicenter prospective cohort study of participants recovering from COVID-19 disease with in-person follow-up for functional and pulmonary phenotyping conducted by the National Heart, Lung and Blood Institute (NHLBI) Prevention and Early Treatment of Acute Lung Injury (PETAL) Network. FIRE CORAL will include a subset of participants enrolled in Biology and Longitudinal Epidemiology of PETAL COVID-19 Observational Study (BLUE CORAL), an NHLBI-funded prospective cohort study describing the clinical characteristics, treatments, biology, and outcomes of hospitalized patients with COVID-19 across the PETAL Network. FIRE CORAL consists of a battery of in-person assessments objectively measuring pulmonary function, abnormalities on lung imaging, physical functional status, and biospecimen analyses. Participants will attend and perform initial in-person testing at 3 to 9 months after hospitalization. The primary objective of the study is to determine the feasibility of longitudinal assessments investigating multiple domains of recovery from COVID-19. Secondarily, we will perform descriptive statistics, including the prevalence and characterization of abnormalities on pulmonary function, chest imaging, and functional status. We will also identify potential clinical and biologic factors that predict recovery or the occurrence of persistent impairment of pulmonary function, chest imaging, and functional status. Ethics and dissemination: FIRE CORAL is approved via the Vanderbilt University central institutional review board (IRB) and via reliance agreement with the site IRBs. Results will be disseminated via the writing group for the protocol committee and reviewed by the PETAL Network publications committee prior to publication. Data obtained via the study will subsequently be made publicly available via NHLBI's biorepository. Strengths and limitations of the study: Strengths: First US-based multicenter cohort of pulmonary and functional outcomes in patients previously hospitalized for COVID-19 infection Longitudinal biospecimen measurement allowing for biologic phenotyping of abnormalities Geographically diverse cohort allowing for a more generalizable understanding of post-COVID pulmonary sequela Limitations: Selected cohort given proximity to a participating center Small cohort which may be underpowered to identify small changes in pulmonary function [ABSTRACT FROM AUTHOR]

Background: The Medical Student Performance Evaluations (MSPE) is a cornerstone of residency applications. Little is known regarding adherence to Association of American Medical Colleges (AAMC) MSPE recommendations and longitudinal changes in MSPE content.Objectives: Evaluate current MSPE quality and longitudinal changes in MSPE and grading practices.Design: Retrospective analysis.Participants: Students from all Liaison Committee on Medical Education (LCME)-accredited medical schools from which the Stanford University Internal Medicine residency program received applications between 2014-2015 and 2019-2020.Main Measures: Inclusion of key words to describe applicant performance and metrics thereof, including distribution among students and key word assignment explanation; inclusion of clerkship grades, grade distributions, and grade composition; and evidence of grade inflation over time.Key Results: MSPE comprehensiveness varied substantially among the 149 schools analyzed. In total, 25% of schools provided complete information consistent with AAMC recommendations regarding key word/categorization of medical students and clerkship grades in 2019-2020. Seventy-seven distinct key word terms appeared across the 139 schools examined in 2019-2020. Grading practices markedly varied, with 2-83% of students receiving the top internal medicine clerkship grade depending on the year and school. Individual schools frequently changed key word and grading practices, with 33% and 18% of schools starting and/or stopping use of key words and grades, respectively. Significant grade inflation occurred over the 6-year study period, with an average 14% relative increase in the proportion of students receiving top clerkship grades.Conclusions: A minority of schools complies with AAMC MSPE guidelines, and MSPEs are inconsistent across time and schools. These practices may impair evaluation of students within and between schools. [ABSTRACT FROM AUTHOR]

In this paper, Choi and colleagues analyzed levels of mitochondrial DNA in two prospective observational cohort studies and found that increased mtDNA levels are associated with ICU mortality, and improve risk prediction in medical ICU patients. The data suggests that mtDNA could serve as a viable plasma biomarker in MICU patients. [ABSTRACT FROM AUTHOR]

Background The determinants of coronavirus disease 2019 (COVID-19) disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNAemia and disease severity, clinical deterioration, and specific EPCs. Methods We used quantitative and digital polymerase chain reaction (qPCR and dPCR) to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the emergency department with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression. Results Of SARS-CoV-2–positive patients, 23.0% (44 of 191) had viral RNA detected in plasma by dPCR, compared with 1.4% (2 of 147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAemic patients were more likely to manifest severe disease (odds ratio, 6.72 [95% confidence interval, 2.45–19.79]), worsening of disease severity (2.43 [1.07–5.38]), and EPCs (2.81 [1.26–6.36]). RNA loads were correlated with maximum severity (r  = 0.47 [95% confidence interval,.20–.67]). Conclusions dPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Because many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate. [ABSTRACT FROM AUTHOR]

Several clinical calculators predict intensive care unit (ICU) mortality, however these are cumbersome and often require 24 h of data to calculate. Retrospective studies have demonstrated the utility of whole blood transcriptomic analysis in predicting mortality. In this study, we tested prospective validation of an 11-gene messenger RNA (mRNA) score in an ICU population. Whole blood mRNA from 70 subjects in the Stanford ICU Biobank with samples collected within 24 h of Emergency Department presentation were used to calculate an 11-gene mRNA score. We found that the 11-gene score was highly associated with 60-day mortality, with an area under the receiver operating characteristic curve of 0.68 in all patients, 0.77 in shock patients, and 0.98 in patients whose primary determinant of prognosis was acute illness. Subjects with the highest quartile of mRNA scores were more likely to die in hospital (40% vs 7%, p < 0.01) and within 60 days (40% vs 15%, p = 0.06). The 11-gene score improved prognostication with a categorical Net Reclassification Improvement index of 0.37 (p = 0.03) and an Integrated Discrimination Improvement index of 0.07 (p = 0.02) when combined with Simplified Acute Physiology Score 3 or Acute Physiology and Chronic Health Evaluation II score. The test performed poorly in the 95 independent samples collected > 24 h after emergency department presentation. Tests will target a 30-min turnaround time, allowing for rapid results early in admission. Moving forward, this test may provide valuable real-time prognostic information to improve triage decisions and allow for enrichment of clinical trials. [ABSTRACT FROM AUTHOR]

Background: A better understanding of the clinical features associated with prolonged hospitalization in acute respiratory failure may allow for better-informed care planning.Research Question: What are the incidence, mortality, cost, and clinical determinants of prolonged hospitalization among patients with acute respiratory failure (ARF)?Study Design and Methods: Using the National Inpatient Sample data from 2004 to 2014, we identified adults 18 years and older with International Classification of Diseases, 9th edition (ICD-9) codes for ARF requiring mechanical ventilation for at least 2 days (ICD-9 518.81 or 518.82, 96.7 or 96.04, and 96.05). Outcomes studied included incidence, in-hospital mortality, cost of hospitalization, and associated patient-level and hospital-level characteristics. Trends were assessed by logistic regression, linear regression, and general linear modeling with Poisson distribution.Results: Of the 5,539,567 patients with ARF, 77,665 (1.4%) had a prolonged length of stay (pLOS), defined as ≥ 60 days. Among those with pLOS, 52,776 (68%) survived to discharge. Over the study period, the incidence of pLOS decreased by 48%, in-patient mortality decreased by 18%, per-patient cost of care rose, but the percentage of the total cost of ARF care consumed by patients with pLOS did not significantly decrease (P = .06). Prolonged LOS was more likely to occur in urban teaching hospitals (OR, 6.8; 95% CI, 4.6-10.2; P < .001), hospitals located in the northeastern United States (OR, 3.6; 95% CI, 3.0-4.3; P < .001), and among patients with Medicaid insurance coverage (OR, 2.1; 95% CI, 1.9-2.4; P < .001).Interpretation: From 2004 to 2014, incidence and mortality decreased among patients with ARF and pLOS, and although per-patient costs rose, the percentage of total cost of care remained stable. There is substantial variation in length of stay for patients with ARF by US region, hospital teaching status, and patient insurance coverage. [ABSTRACT FROM AUTHOR]

Objectives: To identify a novel, generalizable diagnostic for acute respiratory distress syndrome using whole-blood gene expression arrays from multiple acute respiratory distress syndrome cohorts of varying etiologies.Data Sources: We performed a systematic search for human whole-blood gene expression arrays of acute respiratory distress syndrome in National Institutes of Health Gene Expression Omnibus and ArrayExpress. We also included the Glue Grant gene expression cohorts.Study Selection: We included investigator-defined acute respiratory distress syndrome within 48 hours of diagnosis and compared these with relevant critically ill controls.Data Extraction: We used multicohort analysis of gene expression to identify genes significantly associated with acute respiratory distress syndrome, both with and without adjustment for clinical severity score. We performed gene ontology enrichment using Database for Annotation, Visualization and Integrated Discovery and cell type enrichment tests for both immune cells and pneumocyte gene expression. Finally, we selected a gene set optimized for diagnostic power across the datasets and used leave-one-dataset-out cross validation to assess robustness of the model.Data Synthesis: We identified datasets from three adult cohorts with sepsis, one pediatric cohort with acute respiratory failure, and two datasets of adult patients with trauma and burns, for a total of 148 acute respiratory distress syndrome cases and 268 critically ill controls. We identified 30 genes that were significantly associated with acute respiratory distress syndrome (false discovery rate < 20% and effect size >1.3), many of which had been previously associated with sepsis. When metaregression was used to adjust for clinical severity scores, none of these genes remained significant. Cell type enrichment was notable for bands and neutrophils, suggesting that the gene expression signature is one of acute inflammation rather than lung injury per se. Finally, an attempt to develop a generalizable diagnostic gene set for acute respiratory distress syndrome showed a mean area under the receiver-operating characteristic curve of only 0.63 on leave-one-dataset-out cross validation.Conclusions: The whole-blood gene expression signature across a wide clinical spectrum of acute respiratory distress syndrome is likely confounded by systemic inflammation, limiting the utility of whole-blood gene expression studies for uncovering a generalizable diagnostic gene signature. [ABSTRACT FROM AUTHOR]

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity. [Display omitted] • "Weighted nearest neighbor" analysis integrates multimodal single-cell data • A multimodal reference "atlas" of the circulating human immune system • Identification and validation of novel sources of lymphoid heterogeneity • "Reference-based" mapping of query datasets onto a multimodal atlas A framework that allows for the integration of multiple data types using single cells is applied to understand distinct immune cell states, previously unidentified immune populations, and to interpret immune responses to vaccinations. [ABSTRACT FROM AUTHOR]