Ventilatory Ratio Is a Valuable Prognostic Indicator in an Observational Cohort of Patients With ARDS.

BACKGROUND: How indices specific to respiratory compromise contribute to prognostication in patients with ARDS is not well characterized in general clinical populations. The primary objective of this study was to identify variables specific to respiratory failure that might add prognostic value to indicators of systemic illness severity in an observational cohort of subjects with ARDS. METHODS: Fifty subjects with ARDS were enrolled in a single-center, prospective, observational cohort. We tested the contribution of respiratory variables (oxygenation index, ventilatory ratio [VR], and the radiographic assessment of lung edema score) to logistic regression models of 28-d mortality adjusted for indicators of systemic illness severity (the Acute Physiology and Chronic Health Evaluation [APACHE] III score or severity of shock as measured by the number of vasopressors required at baseline) using likelihood ratio testing. We also compared a model utilizing APACHE III with one including baseline number of vasopressors by comparing the area under the receiver operating curve (AUROC). RESULTS: VR significantly improved model performance by likelihood ratio testing when added to APACHE III (P = .036) or the number of vasopressors at baseline (P = .01). Number of vasopressors required at baseline had similar prognostic discrimination to the APACHE III. A model including the number of vasopressors and VR (AUROC 0.77 [95% CI 0.64-0.90]) was comparable to a model including APACHE III and VR (AUROC 0.81 [95% CI 0.68-0.93]; P for comparison = .58.). CONCLUSIONS: In this observational cohort of subjects with ARDS, the VR significantly improved discrimination for mortality when combined with indicators of severe systemic illness. The number of vasopressors required at baseline and APACHE III had similar discrimination for mortality when combined with VR. VR is easily obtained at the bedside and offers promise for clinical prognostication.

Cigarette Smoke Exposure and Acute Respiratory Distress Syndrome in Sepsis: Epidemiology, Clinical Features, and Biologic Markers.

Rationale: Cigarette smoke exposure is associated with an increased risk of developing acute respiratory distress syndrome (ARDS) in trauma, transfusion, and nonpulmonary sepsis. It is unknown whether this relationship exists in the general sepsis population. Furthermore, it is unknown if patients with ARDS have differences in underlying biology based on smoking status. Objectives: To assess the relationship between cigarette smoke exposure and ARDS in sepsis and identify tobacco-related biomarkers of lung injury. Methods: We studied a prospective cohort of 592 patients with sepsis from 2009 to 2017. Plasma cotinine and urine NNAL [urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol] were measured to categorize smoking status. Plasma biomarkers of inflammation and lung injury were measured, including in a smaller cohort of trauma patients with ARDS to increase generalizability. Measurements and Main Results: Passive and active smoking were associated with increased odds of developing ARDS in patients with sepsis. Among patients with sepsis and ARDS, active cigarette smokers were younger and had lower severity of illness than nonsmokers. Patients with ARDS with cigarette smoke exposure had lower plasma levels of IL-8 (P = 0.01) and sTNFR-1 (soluble tumor necrosis factor 1; P = 0.01) compared with those without exposure. Similar biomarker patterns were observed in blunt trauma patients with ARDS. Conclusions: Passive and active smoking are associated with an increased risk of developing ARDS in patients with pulmonary and nonpulmonary sepsis. Among patients with ARDS, those with cigarette smoke exposure have less systemic inflammation, while active smokers also have lower severity of illness compared with nonsmokers, suggesting that smoking contributes to biological heterogeneity in ARDS.

Functional Transcriptomic Studies of Immune Responses and Endotoxin Tolerance in Early Human Sepsis.

BACKGROUND: Limited studies have functionally evaluated the heterogeneity in early ex vivo immune responses during sepsis. Our aim was to characterize early sepsis ex vivo functional immune response heterogeneity by studying whole blood endotoxin responses and derive a transcriptional metric of ex vivo endotoxin response. METHODS: Blood collected within 24 h of hospital presentation from 40 septic patients was divided into two fractions and incubated with media (unstimulated) or endotoxin. Supernatants and cells were isolated, and responses measured using: supernatant cytokines, lung endothelial permeability after supernatant exposure, and RNA expression. A transcriptomic signature was derived in unstimulated cells to predict the ex vivo endotoxin response. The signature was tested in a separate cohort of 191 septic patients to evaluate for association with clinical outcome. Plasma biomarkers were quantified to measure in vivo host inflammation. RESULTS: Ex vivo response to endotoxin varied and was unrelated to immunosuppression, white blood cell count, or the causative pathogen. Thirty-five percent of patients demonstrated a minimal response to endotoxin, suggesting early immunosuppression. High ex vivo cytokine production by stimulated blood cells correlated with increased in vitro pulmonary endothelial cell permeability and was associated with attenuated in vivo host inflammation. A four-gene signature of endotoxin response detectable without the need for a functional assay was identified. When tested in a separate cohort of septic patients, its expression was inversely associated with hospital mortality. CONCLUSIONS: An attenuated ex vivo endotoxin response in early sepsis is associated with greater host in vivo inflammation and a worse clinical outcome.

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis.

BACKGROUND: Two acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. METHODS: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. FINDINGS: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90-0·95) in EARLI and 0·88 (0·84-0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81-0·94] vs 0·92 [0·88-0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). INTERPRETATION: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. FUNDING: US National Institutes of Health and European Society of Intensive Care Medicine.

Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study.

RATIONALE: Using latent class analysis (LCA), two subphenotypes of acute respiratory distress syndrome (ARDS) have consistently been identified in five randomised controlled trials (RCTs), with distinct biological characteristics, divergent outcomes and differential treatment responses to randomised interventions. Their existence in unselected populations of ARDS remains unknown. We sought to identify subphenotypes in observational cohorts of ARDS using LCA. METHODS: LCA was independently applied to patients with ARDS from two prospective observational cohorts of patients admitted to the intensive care unit, derived from the Validating Acute Lung Injury markers for Diagnosis (VALID) (n=624) and Early Assessment of Renal and Lung Injury (EARLI) (n=335) studies. Clinical and biological data were used as class-defining variables. To test for concordance with prior ARDS subphenotypes, the performance metrics of parsimonious classifier models (interleukin 8, bicarbonate, protein C and vasopressor-use), previously developed in RCTs, were evaluated in EARLI and VALID with LCA-derived subphenotypes as the gold-standard. RESULTS: A 2-class model best fit the population in VALID (p=0.0010) and in EARLI (p<0.0001). Class 2 comprised 27% and 37% of the populations in VALID and EARLI, respectively. Consistent with the previously described 'hyperinflammatory' subphenotype, Class 2 was characterised by higher proinflammatory biomarkers, acidosis and increased shock and worse clinical outcomes. The similarities between these and prior RCT-derived subphenotypes were further substantiated by the performance of the parsimonious classifier models in both cohorts (area under the curves 0.92-0.94). The hyperinflammatory subphenotype was associated with increased prevalence of chronic liver disease and neutropenia and reduced incidence of chronic obstructive pulmonary disease. Measurement of novel biomarkers showed significantly higher levels of matrix metalloproteinase-8 and markers of endothelial injury in the hyperinflammatory subphenotype, whereas, matrix metalloproteinase-9 was significantly lower. CONCLUSION: Previously described subphenotypes are generalisable to unselected populations of non-trauma ARDS.

Plasma Metabolites in Early Sepsis Identify Distinct Clusters Defined by Plasma Lipids.

Unbiased global metabolomic profiling has not been used to identify distinct subclasses in patients with early sepsis and sepsis-associated acute respiratory distress syndrome. In this study, we examined whether the plasma metabolome reflects systemic illness in early sepsis and in acute respiratory distress syndrome. DESIGN: Plasma metabolites were measured in subjects with early sepsis. SETTING: Patients were admitted from the emergency department to the ICU in a plasma sample collected within 24 hours of ICU admission. Metabolic profiling of 970 metabolites was performed by Metabolon (Durham, NC). Hierarchical clustering and partial least squares discriminant clustering were used to identify distinct clusters among patients with early sepsis and sepsis-associated acute respiratory distress syndrome. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among critically ill patients with early sepsis (n = 197), three metabolically distinct subgroups were identified, with metabolic subtype driven by plasma lipids. Group 1, with 45 subjects (23% of cohort), had increased 60-day mortality (odds ratio, 2; 95% CI, 0.99-4.0; p = 0.04 for group 1 vs all others). This group also had higher rates of vasopressor-dependent shock, acute kidney injury, and met Berlin acute respiratory distress syndrome criteria more often (all p < 0.05). Conversely, metabolic group 3, with 76 subjects (39% of cohort), had the lowest risk of 60-day mortality (odds ratio, 0.44; 95% CI, 0.22-0.86; p = 0.01) and lower rates of organ dysfunction as reflected in a lower Simplified Acute Physiology Score II (p < 0.001). In contrast, global metabolomic profiling did not separate patient with early sepsis with moderate-to-severe acute respiratory distress syndrome (n = 78) from those with sepsis without acute respiratory distress syndrome (n = 75). CONCLUSIONS: Plasma metabolomic profiling in patients with early sepsis identified three metabolically distinct groups that were characterized by different plasma lipid profiles, distinct clinical phenotypes, and 60-day mortality. Plasma metabolites did not distinguish patients with early sepsis who developed acute respiratory distress syndrome from those who did not.

A neutrophil subset defined by intracellular olfactomedin 4 is associated with mortality in sepsis.

Sepsis is a heterogeneous syndrome clinically and biologically, but biomarkers of distinct host response pathways for early prognostic information and testing targeted treatments are lacking. Olfactomedin 4 (OLFM4), a matrix glycoprotein of neutrophil-specific granules, defines a distinct neutrophil subset that may be an independent risk factor for poor outcomes in sepsis. We hypothesized that increased percentage of OLFM4+ neutrophils on sepsis presentation would be associated with mortality. In a single-center, prospective cohort study, we enrolled adults admitted to an academic medical center from the emergency department (ED) with suspected sepsis [identified by 2 or greater systemic inflammatory response syndrome (SIRS) criteria and antibiotic receipt] from March 2016 through December 2017, followed by sepsis adjudication according to Sepsis-3. We collected 200 µL of whole blood within 24 h of admission and stained for the neutrophil surface marker CD66b followed by intracellular staining for OLFM4 quantitated by flow cytometry. The predictors for 60-day mortality were 1) percentage of OLFM4+ neutrophils and 2) OLFM4+ neutrophils at a cut point of ≥37.6% determined by the Youden Index. Of 120 enrolled patients with suspected sepsis, 97 had sepsis and 23 had nonsepsis SIRS. The mean percentage of OLFM4+ neutrophils was significantly increased in both sepsis and nonsepsis SIRS patients who died (P ≤ 0.01). Among sepsis patients with elevated OLFM4+ (≥37.6%), 56% died, compared with 18% with OLFM4+ <37.6% (P = 0.001). The association between OLFM4+ and mortality withstood adjustment for age, sex, absolute neutrophil count, comorbidities, and standard measures of severity of illness (SOFA score, APACHE III) (P < 0.03). In summary, OLFM4+ neutrophil percentage is independently associated with 60-day mortality in sepsis and may represent a novel measure of the heterogeneity of host response to sepsis.

Alternative Tobacco Product Use in Critically Ill Patients.

Background: Alternative tobacco product (ATP) use has bee linked to critical illness, however, few studies have examined the use of these substances in critically ill populations. We sought to examine ATP use within critically ill patients and to define barriers in accurately assessing use within this population. Methods: We prospectively studied 533 consecutive patients from the Early Assessment of Renal and Lung Injury study, enrolled between 2013 and 2016 at a tertiary referral center and a safety-net hospital. ATP use information (electronic cigarettes, cigars, pipes, hookahs/waterpipes, and snus/chewing tobacco) was obtained from the patient or surrogate using a detailed survey. Reasons for non-completion of the survey were recorded, and differences between survey responders vs. non-responders, self- vs. surrogate responders, and ATP users vs. non-users were explored. Results: Overall, 80% (n = 425) of subjects (56% male) completed a tobacco product use survey. Of these, 12.2% (n = 52) reported current ATP use, while 5.6% reported using multiple ATP products. When restricted to subjects who were self-responders, 17% reported ATP use, while 10% reported current cigarette smoking alone. The mean age of ATP users was 57 ± 17 years. Those who did not complete a survey were sicker and more likely to have died during admission. Subjects who completed the survey as self-responders reported higher levels of ATP use than ones with surrogate responders (p < 0.0001). Conclusion: ATP use is common among critically ill patients despite them being generally older than traditional users. Survey self-responders were more likely than surrogate responders to report use. These findings highlight the importance of improving our current methods of surveillance of ATP use in older adults in the outpatient setting.

Clinician Recognition of the Acute Respiratory Distress Syndrome: Risk Factors for Under-Recognition and Trends Over Time.

OBJECTIVES: The acute respiratory distress syndrome is common in critically ill patients. Recognition is crucial because acute respiratory distress syndrome is associated with a high mortality rate, and low tidal volume ventilation improves mortality. However, acute respiratory distress syndrome often goes unrecognized. Risk factors for under-recognition and trends over time have not been fully described. DESIGN: Retrospective chart review of patients with acute respiratory distress syndrome from a prospective cohort study of critically ill patients. For each patient's ICU stay, we searched the chart for terms that indicated that acute respiratory distress syndrome was diagnosed, in the differential diagnosis, or treated with low tidal volume ventilation. SETTING: ICUs at a tertiary hospital at the University of California, San Francisco between 2008 and 2016. PATIENTS: Critically ill patients with acute respiratory distress syndrome. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Acute respiratory distress syndrome was recognized in 70% of patients, and recognition increased from 60% in 2008-2009 to 92% in 2016 (p = 0.004). Use of tidal volumes less than 6.5 mL/kg also increased (p < 0.001) from 20% to 92%. Increased acute respiratory distress syndrome severity (p = 0.01) and vasopressor use (p = 0.04) were associated with greater recognition. Clinician diagnosis of acute respiratory distress syndrome and inclusion of acute respiratory distress syndrome in the differential diagnosis were associated with tidal volumes less than 6.5 mL/kg (51% use of tidal volume ≤ 6.5 mL/kg if acute respiratory distress syndrome recognized vs 15% if not recognized; p = 0.002). Diagnosing acute respiratory distress syndrome was associated with lower tidal volume in multivariate analysis. CONCLUSIONS: Although acute respiratory distress syndrome recognition and low tidal volume ventilation use have increased over time, they remain less than universal. Clinician recognition of acute respiratory distress syndrome is associated with both systemic and respiratory severity of illness and is also associated with use of low tidal volume ventilation.

Acute respiratory distress syndrome-attributable mortality in critically ill patients with sepsis.

PURPOSE: Previous studies assessing impact of acute respiratory distress syndrome (ARDS) on mortality have shown conflicting results. We sought to assess the independent association of ARDS with in-hospital mortality among intensive care unit (ICU) patients with sepsis. METHODS: We studied two prospective sepsis cohorts drawn from the Early Assessment of Renal and Lung Injury (EARLI; n = 474) and Validating Acute Lung Injury markers for Diagnosis (VALID; n = 337) cohorts. ARDS was defined by Berlin criteria. We used logistic regression to compare in-hospital mortality in patients with and without ARDS, controlling for baseline severity of illness. We also estimated attributable mortality, adjusted for illness severity by stratification. RESULTS: ARDS occurred in 195 EARLI patients (41%) and 99 VALID patients (29%). ARDS was independently associated with risk of hospital death in multivariate analysis, even after controlling for severity of illness, as measured by APACHE II (odds ratio [OR] 1.65 (95% confidence interval [CI] 1.02, 2.67), p = 0.04 in EARLI; OR 2.12 (CI 1.16, 3.92), p = 0.02 in VALID). Patients with severe ARDS (P/F < 100) primarily drove this relationship. The attributable mortality of ARDS was 27% (CI 14%, 37%) in EARLI and 37% (CI 10%, 51%) in VALID. ARDS was independently associated with ICU mortality, hospital length of stay (LOS), ICU LOS, and ventilator-free days. CONCLUSIONS: Development of ARDS among ICU patients with sepsis confers increased risk of ICU and in-hospital mortality in addition to other important outcomes. Clinical trials targeting patients with severe ARDS will be best poised to detect measurable differences in these outcomes.

Peripheral blood leukocyte telomere length is associated with survival of sepsis patients.

Shorter peripheral blood leukocyte (PBL) telomere length (TL) has been associated with poor outcomes in various chronic lung diseases. Whether PBL-TL is associated with survival from critical illness was tested in this study.We analysed data from a prospective observational cohort study of 937 critically ill patients at Vanderbilt University Medical Center (VUMC). PBL-TL was measured using quantitative PCR of DNA isolated from PBLs. Findings were validated in an independent cohort of 394 critically ill patients with sepsis admitted to the University of California San Francisco (UCSF).In the VUMC cohort, shorter PBL-TL was associated with worse 90-day survival (adjusted hazard ratio (aHR) 1.3, 95% CI 1.1-1.6 per 1 kb TL decrease; p=0.004); in subgroup analyses, shorter PBL-TL was associated with worse 90-day survival for patients with sepsis (aHR 1.5, 95% CI 1.2-2.0 per 1 kb TL decrease; p=0.001), but not trauma. Although not associated with development of acute respiratory distress syndrome (ARDS), among ARDS subjects, shorter PBL-TL was associated with more severe ARDS (OR 1.7, 95% CI 1.2-2.5 per 1 kb TL decrease; p=0.006). The associations of PBL-TL with survival (adjusted HR 1.6, 95% CI 1.2-2.1 per 1 kb TL decrease; p=0.003) and risk for developing severe ARDS (OR 2.5, 95% CI 1.1-6.3 per 1 kb TL decrease; p=0.044) were validated in the UCSF cohort.Short PBL-TL is strongly associated with worse survival and more severe ARDS in critically ill patients, especially patients with sepsis. These findings suggest that telomere dysfunction may contribute to outcomes from critical illness.

Plasma sTNFR1 and IL8 for prognostic enrichment in sepsis trials: a prospective cohort study.

BACKGROUND: Enrichment strategies improve therapeutic targeting and trial efficiency, but enrichment factors for sepsis trials are lacking. We determined whether concentrations of soluble tumor necrosis factor receptor-1 (sTNFR1), interleukin-8 (IL8), and angiopoietin-2 (Ang2) could identify sepsis patients at higher mortality risk and serve as prognostic enrichment factors. METHODS: In a multicenter prospective cohort study of 400 critically ill septic patients, we derived and validated thresholds for each marker and expressed prognostic enrichment using risk differences (RD) of 30-day mortality as predictive values. We then used decision curve analysis to simulate the prognostic enrichment of each marker and compare different prognostic enrichment strategies. MEASUREMENTS AND MAIN RESULTS: An admission sTNFR1 concentration > 8861 pg/ml identified patients with increased mortality in both the derivation (RD 21.6%) and validation (RD 17.8%) populations. Among immunocompetent patients, an IL8 concentration > 94 pg/ml identified patients with increased mortality in both the derivation (RD 17.7%) and validation (RD 27.0%) populations. An Ang2 level > 9761 pg/ml identified patients at 21.3% and 12.3% increased risk of mortality in the derivation and validation populations, respectively. Using sTNFR1 or IL8 to select high-risk patients improved clinical trial power and efficiency compared to selecting patients with septic shock. Ang2 did not outperform septic shock as an enrichment factor. CONCLUSIONS: Thresholds for sTNFR1 and IL8 consistently identified sepsis patients with higher mortality risk and may have utility for prognostic enrichment in sepsis trials.

Metagenomic comparison of tracheal aspirate and mini-bronchial alveolar lavage for assessment of respiratory microbiota.

Accurate and informative microbiological testing is essential for guiding diagnosis and management of pneumonia in patients who are critically ill. Sampling of tracheal aspirate (TA) is less invasive compared with mini-bronchoalveolar lavage (mBAL) and is now recommended as a frontline diagnostic approach in patients who are mechanically ventilated, despite the historical belief that TA was suboptimal due to contamination from oral microbes. Advancements in metagenomic next-generation sequencing (mNGS) now permit assessment of airway microbiota without a need for culture and, as such, provide an opportunity to examine differences between mBAL and TA at a resolution previously unachievable. Here, we engaged shotgun mNGS to assess quantitatively the airway microbiome in matched mBAL and TA specimens from a prospective cohort of critically ill adults. We observed moderate differences between sample types across all subjects; however, we found significant compositional similarity in subjects with bacterial pneumonia, whose microbial communities were characterized by dominant pathogens. In contrast, in patients with noninfectious acute respiratory illnesses, significant differences were observed between sample types. Our findings suggest that TA sampling provides a similar assessment of airway microbiota as more invasive testing by mBAL in patients with pneumonia.

Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults.

Lower respiratory tract infections (LRTIs) lead to more deaths each year than any other infectious disease category. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests. In critically ill patients, noninfectious inflammatory syndromes resembling LRTIs further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the airway microbiome, and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed a rules-based model (RBM) and logistic regression model (LRM) in a derivation cohort of 20 patients with LRTIs or noninfectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with noninfectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an area under the receiver-operating curve (AUC) of 0.96 (95% CI, 0.86-1.00), the diversity metric with an AUC of 0.80 (95% CI, 0.63-0.98), and the host transcriptional classifier with an AUC of 0.88 (95% CI, 0.75-1.00). Combining these achieved a negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome, and host transcriptome may hold promise as a tool for LRTI diagnosis.

Human pulmonary endothelial cell permeability after exposure to LPS-stimulated leukocyte supernatants derived from patients with early sepsis.

Systemic immune activation is the hallmark of sepsis, which can result in endothelial injury and the acute respiratory distress syndrome (ARDS). The aim of this study was to investigate heterogeneity in sepsis-mediated endothelial permeability using primary human pulmonary microvascular endothelial cells (HPMECs) and the electric cell-substrate impedance sensing (ECIS) platform. After plasma removal, cellular component of whole blood from 35 intensive care unit (ICU) patients with early sepsis was diluted with media and stimulated with either lipopolysaccharide (LPS) or control media. Resulting supernatants were cocultured with HPMECs seeded on ECIS plates, and resistance was continually measured. A decrease in resistance signified increased permeability. After incubation, HPMECs were detached and cell adhesion proteins were quantified using flow cytometry and immunohistochemistry, and gene expression was analyzed with quantitative PCR. Significant heterogeneity in endothelial permeability after exposure to supernatants of LPS-stimulated leukocytes was identified. ICU patients with sepsis stratified into one of the following three groups: minimal (9/35, 26%), intermediate (18/35, 51%), and maximal (8/35, 23%) permeability. Maximal permeability was associated with increased intercellular adhesion molecule-1 protein and mRNA expression and decreased vascular endothelial-cadherin mRNA expression. These findings indicate that substantial heterogeneity in pulmonary endothelial permeability is induced by supernatants of LPS-stimulated leukocytes derived from patients with early sepsis and provide insights into some of the mechanisms that induce lung vascular injury. In addition, this in vitro model of lung endothelial permeability from LPS-stimulated leukocytes may be a useful method for testing therapeutic agents that could mitigate endothelial injury in early sepsis.