Patients with hypothermic sepsis have a unique gene expression profile compared to patients with fever and sepsis

The pathophysiology of hypothermia during sepsis is unclear. Using genomic profiling of blood leukocytes, we aimed to determine if hypothermia is associated with a different gene expression profile compared to fever during sepsis. Patients with sepsis and either hypothermia or fever within 24 hours after ICU admission were included in the study (n = 168). Hypothermia was defined as body temperature below 36 °C. Fever was defined as body temperature equal to or above 38.3°C. We compared blood gene expression (whole‐genome transcriptome in leukocytes) in hypothermic septic compared to febrile septic patients in an unmatched analysis and matched for APACHE IV score and the presence of shock. In total, 67 septic patients were hypothermic and 101 patients were febrile. Hypothermia was associated with a distinct gene expression profile in both unmatched and matched analyses. There were significant differences related to the up‐ and downregulation of canonical signalling pathways. In the matched analysis, the top upregulated gene was cold‐inducible mRNA binding protein (CIRBP) which plays a role in cold‐induced suppression of cell proliferation. In addition, we found three signalling pathways significantly upregulated in hypothermic patients compared to febrile patients; tryptophan degradation X, phenylalanine degradation IV and putrescine degradation III. In conclusion, there are distinct signalling pathways and genes associated with hypothermia, including tryptophan degradation and CIRBP expression, providing a possible link to the modulation of body temperature and early immunosuppression. Future studies may focus on the canonical signalling pathways presented in this paper to further investigate spontaneous hypothermia in sepsis.

Lung Microbiota Predict Clinical Outcomes in Critically Ill Patients

Rationale: Recent studies have revealed that, in critically ill patients, lung microbiota are altered and correlate with alveolar inflammation. The clinical significance of altered lung bacteria in critical illness is unknown.Objectives: To determine if clinical outcomes of critically ill patients are predicted by features of the lung microbiome at the time of admission.Methods: We performed a prospective, observational cohort study in an ICU at a university hospital. Lung microbiota were quantified and characterized using droplet digital PCR and bacterial 16S ribosomal RNA gene quantification and sequencing. Primary predictors were the bacterial burden, community diversity, and community composition of lung microbiota. The primary outcome was ventilator-free days, determined at 28 days after admission.Measurements and Main Results: Lungs of 91 critically ill patients were sampled using miniature BAL within 24 hours of ICU admission. Patients with increased lung bacterial burden had fewer ventilator-free days (hazard ratio, 0.43; 95% confidence interval, 0.21-0.88), which remained significant when the analysis was controlled for pneumonia and severity of illness. The community composition of lung bacteria predicted ventilator-free days (P = 0.003), driven by the presence of gut-associated bacteria (e.g., species of the Lachnospiraceae and Enterobacteriaceae families). Detection of gut-associated bacteria was also associated with the presence of acute respiratory distress syndrome.Conclusions: Key features of the lung microbiome (bacterial burden and enrichment with gut-associated bacteria) predict outcomes in critically ill patients. The lung microbiome is an understudied source of clinical variation in critical illness and represents a novel therapeutic target for the prevention and treatment of acute respiratory failure.

Age-dependent differences in pulmonary host responses in ARDS: a prospective observational cohort study

Background

Results from preclinical studies suggest that age-dependent differences in host defense and the pulmonary renin–angiotensin system (RAS) are responsible for observed differences in epidemiology of acute respiratory distress syndrome (ARDS) between children and adults. The present study compares biomarkers of host defense and RAS in bronchoalveolar lavage (BAL) fluid from neonates, children, adults, and older adults with ARDS.

Methods

In this prospective observational study, we enrolled mechanical ventilated ARDS patients categorized into four age groups: 20 neonates (< 28 days corrected postnatal age), 29 children (28 days–18 years), 26 adults (18–65 years), and 17 older adults (> 65 years of age). All patients underwent a nondirected BAL within 72 h after intubation. Activities of the two main enzymes of RAS, angiotensin converting enzyme (ACE) and ACE2, and levels of biomarkers of inflammation, endothelial activation, and epithelial damage were determined in BAL fluid.

Results

Levels of myeloperoxidase, interleukin (IL)-6, IL-10, and p-selectin were higher with increasing age, whereas intercellular adhesion molecule-1 was higher in neonates. No differences in activity of ACE and ACE2 were seen between the four age groups.

Conclusions

Age-dependent differences in the levels of biomarkers in lungs of ARDS patients are present. Especially, higher levels of markers involved in the neutrophil response were found with increasing age. In contrast to preclinical studies, age is not associated with changes in the pulmonary RAS.

Electronic supplementary material

The online version of this article (10.1186/s13613-019-0529-4) contains supplementary material, which is available to authorized users.

Risk stratification using SpO2/FiO2 and PEEP at initial ARDS diagnosis and after 24 h in patients with moderate or severe ARDS

BACKGROUND

We assessed the potential of risk stratification of ARDS patients using SpO₂/FiO₂ and positive end-expiratory pressure (PEEP) at ARDS onset and after 24 h.

METHODS

We used data from a prospective observational study in patients admitted to a mixed medical-surgical intensive care unit of a university hospital in the Netherlands. Risk stratification was by cutoffs for SpO₂/FiO₂ and PEEP. The primary outcome was in-hospital mortality. Patients with moderate or severe ARDS with a length of stay of > 24 h were included in this study. Patients were assigned to four predefined risk groups: group I (SpO₂/FiO₂ ≥ 190 and PEEP < 10 cm H₂O), group II (SpO₂/FiO₂ ≥ 190 and PEEP ≥ 10 cm), group III (SpO₂/FiO₂ < 190 and PEEP < 10 cm H₂O) and group IV (SpO₂/FiO₂ < 190 and PEEP ≥ 10 cm H₂O).

RESULTS

The analysis included 456 patients. SpO₂/FiO₂ and PaO₂/FiO₂ had a strong relationship (P < 0.001, R ₂ = 0.676) that could be described in a linear regression equation (SpO₂/FiO₂ = 42.6 + 1.0 * PaO₂/FiO₂). Risk stratification at initial ARDS diagnosis resulted in groups that had no differences in in-hospital mortality. Risk stratification at 24 h resulted in groups with increasing mortality rates. The association between group assignment at 24 h and outcome was confounded by several factors, including APACHE IV scores, arterial pH and plasma lactate levels, and vasopressor therapy.

CONCLUSIONS

In this cohort of patients with moderate or severe ARDS, SpO₂/FiO₂ and PaO₂/FiO₂ have a strong linear relationship. In contrast to risk stratification at initial ARDS diagnosis, risk stratification using SpO₂/FiO₂ and PEEP after 24 h resulted in groups with worsening outcomes. Risk stratification using SpO₂/FiO₂ and PEEP could be practical, especially in resource-limited settings.

Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis

RATIONALE

We hypothesised that patients with acute respiratory distress syndrome (ARDS) can be clustered based on concentrations of plasma biomarkers and that the thereby identified biological phenotypes are associated with mortality.

METHODS

Consecutive patients with ARDS were included in this prospective observational cohort study. Cluster analysis of 20 biomarkers of inflammation, coagulation and endothelial activation provided the phenotypes in a training cohort, not taking any outcome data into account. Logistic regression with backward selection was used to select the most predictive biomarkers, and these predicted phenotypes were validated in a separate cohort. Multivariable logistic regression was used to quantify the independent association with mortality.

RESULTS

Two phenotypes were identified in 454 patients, which we named 'uninflamed' (N=218) and 'reactive' (N=236). A selection of four biomarkers (interleukin-6, interferon gamma, angiopoietin 1/2 and plasminogen activator inhibitor-1) could be used to accurately predict the phenotype in the training cohort (area under the receiver operating characteristics curve: 0.98, 95% CI 0.97 to 0.99). Mortality rates were 15.6% and 36.4% (p<0.001) in the training cohort and 13.6% and 37.5% (p<0.001) in the validation cohort (N=207). The 'reactive phenotype' was independent from confounders associated with intensive care unit mortality (training cohort: OR 1.13, 95% CI 1.04 to 1.23; validation cohort: OR 1.18, 95% CI 1.06 to 1.31).

CONCLUSIONS

Patients with ARDS can be clustered into two biological phenotypes, with different mortality rates. Four biomarkers can be used to predict the phenotype with high accuracy. The phenotypes were very similar to those found in cohorts derived from randomised controlled trials, and these results may improve patient selection for future clinical trials targeting host response in patients with ARDS.

External validation of the APPS, a new and simple outcome prediction score in patients with the acute respiratory distress syndrome

Background

A recently developed prediction score based on age, arterial oxygen partial pressure to fractional inspired oxygen ratio (PaO₂/FiO₂) and plateau pressure (abbreviated as ‘APPS’) was shown to accurately predict mortality in patients diagnosed with the acute respiratory distress syndrome (ARDS). After thorough temporal external validation of the APPS, we tested the spatial external validity in a cohort of ARDS patients recruited during 3 years in two hospitals in the Netherlands.

Methods

Consecutive patients with moderate or severe ARDS according to the Berlin definition were included in this observational multicenter cohort study from the mixed medical-surgical ICUs of two university hospitals. The APPS was calculated per patient with the maximal airway pressure instead of the plateau pressure as all patients were ventilated in pressure-controlled mode. The predictive accuracy for hospital mortality was evaluated by calculating the area under the receiver operating characteristics curve (AUC-ROC). Additionally, the score was recalibrated and reassessed.

Results

In total, 439 patients with moderate or severe ARDS were analyzed. All-cause hospital mortality was 43 %. The APPS predicted all-cause hospital mortality with moderate accuracy, with an AUC-ROC of 0.62 [95 % confidence interval (CI) 0.56–0.67]. Calibration was moderate using the original cutoff values (Hosmer–Lemeshow goodness of fit P < 0.001), and recalibration was performed for the cutoff value for age and plateau pressure. This resulted in good calibration (P = 1.0), but predictive accuracy did not improve (AUC-ROC 0.63, 95 % CI 0.58–0.68).

Conclusions

The predictive accuracy for all-cause hospital mortality of the APPS was moderate, also after recalibration of the score, and thus the APPS does not seem to be fitted for that purpose. The APPS might serve as simple tool for stratification of mortality in patients with moderate or severe ARDS. Without recalibrations, the performance of the APPS was moderate and we should therefore hesitate to blindly apply the score to other cohorts of ARDS patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s13613-016-0190-0) contains supplementary material, which is available to authorized users.