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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]

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]

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]

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]