Background

Cystatin C is a well-validated marker of glomerular filtration rate in chronic kidney disease. Higher plasma concentrations of cystatin C are associated with worse clinical outcomes in heterogenous populations of critically ill patients and may be superior to creatinine in identifying kidney injury in critically ill patients. We hypothesized that elevated levels of plasma cystatin C in patients with acute respiratory distress syndrome (ARDS) would be associated with mortality risk.

Methods

In a retrospective study, cystatin C was measured by nephelometry on plasma obtained at enrollment from 919 patients in the Fluid and Catheter Treatment Trial. Multivariable logistic regression was performed testing the association between quartiles of cystatin C and 60-day mortality. Analyses were stratified by acute kidney injury (AKI) status identified in the first 7 days after enrollment by Kidney Disease: Improving Global Outcomes (KDIGO) criteria.

Results

Cystatin C was significantly higher among those patients who died compared to those who survived to 60 days [1.2 (0.9-1.9) mg/L vs. 0.8 (0.6-1.2) mg/L, p < 0.001]. Compared to the lower three quartiles, subjects in the highest quartile of cystatin C had a significantly higher odds of death at 60 days [OR 1.8 (1.2-2.6), p = 0.003 in adjusted analyses]; the odds of death incrementally rose in higher cystatin C quartiles compared to the lowest quartile (OR 1.1, 1.8, and 2.5). In adjusted analyses stratified by AKI status, compared to subjects in the lower three quartiles, subjects in the highest quartile of cystatin C with AKI had a significantly higher odds of death at 60 days both in participants with AKI [OR 1.6 (1.0-2.4), p = 0.048] and those without AKI [OR 2.4 (1.2-5.0), p = 0.017]. In adjusted analyses, there was no significant association between sex-stratified baseline creatinine quartiles and mortality.

Conclusions

Higher plasma levels of cystatin C on enrollment were strongly associated with mortality at 60 days in patients with ARDS with and without AKI identified by creatinine-based definitions. Compared to creatinine, cystatin C may be a better biomarker of kidney function in patients with ARDS and therefore identify patients with multiple organ failure at higher risk of death.

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.

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.

ABSTRACT

Lower respiratory tract infections (LRTI) 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, non-infectious inflammatory syndromes resembling LRTI 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 rules-based and logistic regression models (RBM, LRM) in a derivation cohort of 20 patients with LRTI or non-infectious 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 non-infectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an 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 novel tool for LRTI diagnosis.

SIGNIFICANCE STATEMENT

Lower respiratory tract infections (LRTI) are the leading cause of infectious disease-related death worldwide yet remain challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, non-infectious respiratory syndromes that resemble LRTI further complicate diagnosis and confound targeted treatment. To address this, we developed a novel metagenomic sequencing-based approach that simultaneously interrogates three core elements of acute airway infections: the pathogen, airway microbiome and host response. We studied this approach in a prospective cohort of critically ill patients with acute respiratory failure and found that combining pathogen, microbiome and host gene expression metrics achieved accurate LRTI diagnosis and identified etiologic pathogens in patients with clinically identified infections but otherwise negative testing.

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.

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.

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.