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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., peer-reviewed

Background Immunomodulatory therapies that improve the outcome of sepsis are not available. We sought to determine whether treatment of critically ill patients with sepsis with low-dose erythromycin—a macrolide antibiotic with broad immunomodulatory effects—decreased mortality and ameliorated underlying disease pathophysiology. Methods We conducted a target trial emulation, comparing patients with sepsis admitted to two intensive care units (ICU) in the Netherlands for at least 72 h, who were either exposed or not exposed during this period to treatment with low-dose erythromycin (up to 600 mg per day, administered as a prokinetic agent) but no other macrolides. We used two common propensity score methods (matching and inverse probability of treatment weighting) to deal with confounding by indication and subsequently used Cox regression models to estimate the treatment effect on the primary outcome of mortality rate up to day 90. Secondary clinical outcomes included change in SOFA, duration of mechanical ventilation and the incidence of ICU-acquired infections. We used linear mixed models to assess differences in 15 host response biomarkers reflective of key pathophysiological processes from admission to day 4. Results In total, 235 patients started low-dose erythromycin treatment, 470 patients served as controls. Treatment started at a median of 38 [IQR 25–52] hours after ICU admission for a median of 5 [IQR 3–8] total doses in the first course. Matching and weighting resulted in populations well balanced for proposed confounders. We found no differences between patients treated with low-dose erythromycin and control subjects in mortality rate up to day 90: matching HR 0.89 (95% CI 0.64–1.24), weighting HR 0.95 (95% CI 0.66–1.36). There were no differences in secondary clinical outcomes. The change in host response biomarker levels from admission to day 4 was similar between erythromycin-treated and control subjects. Conclusion In this target trial emulation in critically ill patients with sepsis, we could not demonstrate an effect of treatment with low-dose erythromycin on mortality, secondary clinical outcomes or host response biomarkers.

Purpose: There is limited knowledge on how the source of infection impacts the host response to sepsis. We aimed to compare the host response in sepsis patients with a single, known source at admission (< 24 h) to the intensive care unit., Methods: From the molecular diagnosis and risk stratification of sepsis (MARS) prospective cohort, we measured 16 plasma host response biomarkers reflective of key host response pathways in 621 sepsis patients. In a subgroup (n = 335), blood leukocyte transcriptomes were compared between the sources. Differences in clinical patient profiles and survival were compared in the whole sepsis cohort (n = 2019)., Results: The plasma biomarker cohort was categorized into sepsis originating from the respiratory tract (n = 334, 53.8%), abdomen (n = 159, 25.6%), urinary tract (n = 44, 7.1%), cardiovascular (n = 41, 6.6%), central nervous system (CNS) (n = 18, 2.9%), or skin (n = 25, 4%). This analysis revealed stronger inflammatory and cytokine responses, loss of vascular integrity and coagulation activation in abdominal sepsis relative to respiratory. Endothelial cell activation was prominent in urinary, cardiovascular and skin infections, while CNS infection was associated with the least host response aberrations. The leukocyte transcriptional response showed the largest overlap between abdominal and pulmonary infections (76% in common); notable differences between the sources were detected regarding hemostasis, cytokine signaling, innate and adaptive immune, and metabolic transcriptional pathways. After adjustment for confounders, the source of infection remained an independent contributor to 30-day mortality (unadjusted p = 0.001, adjusted p = 0.028)., Conclusion: Sepsis heterogeneity is partly explained by source-specific host response dysregulations and should be considered when selecting patients for trials testing immune modulatory drugs., peer-reviewed

A novel multiplex real-time PCR for bloodstream infections (BSI-PCR) detects pathogens directly in blood. This study aimed at determining the positive predictive value (PPV) of BSI-PCR in critically ill patients with sepsis. We included consecutive patients with presumed sepsis upon admission to the intensive care unit (ICU). The multiplexed BSI-PCR included 17 individual PCRs for a broad panel of species- and genus-specific DNA targets. BSI-PCR results were compared with a reference diagnosis for which plausibility of infection and causative pathogen(s) had been prospectively assessed by trained observers, based on available clinical and microbiological evidence. PPV and false positive proportion (FPP) were calculated. Clinical plausibility of discordant positive results was adjudicated by an expert panel. Among 325 patients, infection likelihood was categorized as confirmed, uncertain, and ruled out in 210 (65%), 88 (27%), and 27 (8%) subjects, respectively. BSI-PCR identified one or more microorganisms in 169 (52%) patients, of whom 104 (61%) had at least one detection in accordance with the reference diagnosis. Discordant positive PCR results were observed in 95 patients, including 30 subjects categorized as having an “unknown” pathogen. Based on 5525 individual PCRs yielding 295 positive results, PPV was 167/295 (57%) and FPP was 128/5525 (2%). Expert adjudication of the 128 discordant PCR findings resulted in an adjusted PPV of 68% and FPP of 2%. BSI-PCR was all-negative in 156 patients, including 79 (51%) patients in whom infection was considered ruled out. BSI-PCR may complement conventional cultures and expedite the microbiological diagnosis of sepsis in ICU patients, but improvements in positive predictive value of the test are warranted before its implementation in clinical practice can be considered. Electronic supplementary material The online version of this article (10.1007/s10096-019-03616-w) contains supplementary material, which is available to authorized users.

BackgroundThe relationship between muscle function in critically ill patients assessed using bedside techniques and clinical outcomes has not been systematically described. We aimed to evaluate the association between muscle weakness assessed by bedside evaluation and mortality or weaning from mechanical ventilation, and the capacity of each evaluation tool to predict outcomes.MethodsFive databases (PubMed, Embase, CINAHL, Cochrane Library, Science Direct) were searched from January 2000 to December 2018. Data were extracted and random effects meta-analyses were performed.Results60 studies were analysed, including 4382 patients. Intensive care unit (ICU)-related muscle weakness was associated with an increase in overall mortality with odds ratios ranging from 1.2 (95% CI 0.60–2.40) to 4.48 (95% CI 1.49–13.42). Transdiaphragmatic twitch pressure had the highest predictive capacity for overall mortality, with a sensitivity of 0.87 (95% CI 0.76–0.93) and a specificity of 0.36 (95% CI 0.27–0.43). The area under the curve (AUC) was 0.74 (95% CI 0.70–0.78). Muscle weakness was associated with an increase in mechanical ventilation weaning failure rate with an odds ratio ranging from 2.64 (95% CI 0.72–9.64) to 19.07 (95% CI 9.35–38.9). Diaphragm thickening fraction had the highest predictive capacity for weaning failure with a sensitivity of 0.76 (95% CI 0.67–0.83) and a specificity of 0.86 (95% CI 0.78–0.92). The AUC was 0.86 (95% CI 0.83–0.89).ConclusionICU-related muscle weakness detected by bedside techniques is a serious issue associated with a high risk of death or prolonged mechanical ventilation. Evaluating diaphragm function should be a clinical priority in the ICU.

Background Indirect indices for measuring impaired ventilation, such as the estimated dead space fraction and the ventilatory ratio, have been shown to be independently associated with an increased risk of mortality. This study aimed to compare various methods for dead space estimation and the ventilatory ratio in patients with acute respiratory distress syndrome (ARDS) and to determine their independent values for predicting death at day 30. The present study is a post hoc analysis of a prospective observational cohort study of ICUs of two tertiary care hospitals in the Netherlands. Results Individual patient data from 940 ARDS patients were analyzed. Estimated dead space fraction and the ventilatory ratio at days 1 and 2 were significantly higher among non-survivors (p VD/VT phys] and the ventilatory ratio at day 2 showed independent association with mortality at 30 days (odds ratio 1.28 [95% CI 1.02–1.61], p p VD/VT HB] and Penn State [VD/VT PS] estimations were not associated with mortality. The predicted validity of the estimated dead space fraction and the ventilatory ratio improved the baseline model based on PEEP, PaO2/FiO2, driving pressure and compliance of the respiratory system at day 2 (AUROCC 0.72 vs. 0.69, p Conclusions Estimated methods for dead space calculation and the ventilatory ratio during the early course of ARDS are associated with mortality at day 30 and add statistically significant but limited improvement in the predictive accuracy to indices of oxygenation and respiratory system mechanics at the second day of mechanical ventilation.

Background To develop a mathematical model to estimate daily evolution of disease severity using routinely available parameters in patients admitted to the intensive care unit (ICU). Methods Over a 3-year period, we prospectively enrolled consecutive adults with sepsis and categorized patients as (1) being at risk for developing (more severe) organ dysfunction, (2) having (potentially still reversible) limited organ failure, or (3) having multiple-organ failure. Daily probabilities for transitions between these disease states, and to death or discharge, during the first 2 weeks in ICU were calculated using a multi-state model that was updated every 2 days using both baseline and time-varying information. The model was validated in independent patients. Results We studied 1371 sepsis admissions in 1251 patients. Upon presentation, 53 (4%) were classed at risk, 1151 (84%) had limited organ failure, and 167 (12%) had multiple-organ failure. Among patients with limited organ failure, 197 (17%) evolved to multiple-organ failure or died and 809 (70%) improved or were discharged alive within 14 days. Among patients with multiple-organ failure, 67 (40%) died and 91 (54%) improved or were discharged. Treatment response could be predicted with reasonable accuracy (c-statistic ranging from 0.55 to 0.81 for individual disease states, and 0.67 overall). Model performance in the validation cohort was similar. Conclusions This prediction model that estimates daily evolution of disease severity during sepsis may eventually support clinicians in making better informed treatment decisions and could be used to evaluate prognostic biomarkers or perform in silico modeling of novel sepsis therapies during trial design. Clinical trial registration ClinicalTrials.gov NCT01905033

Background Cytomegalovirus (CMV) reactivation in previously immunocompetent critically ill patients is associated with increased mortality, which has been hypothesized to result from virus-induced immunomodulation. Therefore, we studied the effects of CMV reactivation on the temporal course of host response biomarkers in patients with sepsis. Methods In this matched cohort study, each sepsis patient developing CMV reactivation between day 3 and 17 (CMV+) was compared with one CMV seropositive patient without reactivation (CMVs+) and one CMV seronegative patient (CMVs−). CMV serostatus and plasma loads were determined by enzyme-linked immunoassays and real-time polymerase chain reaction, respectively. Systemic interleukin-6 (IL-6), IL-8, IL-18, interferon-gamma–induced protein-10 (IP-10), neutrophilic elastase, IL-1 receptor antagonist (RA), and IL-10 were measured at five time points by multiplex immunoassay. The effects of CMV reactivation on sequential concentrations of these biomarkers were assessed in multivariable mixed models. Results Among 64 CMV+ patients, 45 could be matched to CMVs+ or CMVs− controls or both. The two baseline characteristics and host response biomarker levels at viremia onset were similar between groups. CMV+ patients had increased IP-10 on day 7 after viremia onset (symmetric percentage difference +44% versus −15% when compared with CMVs+ and +37% versus +4% when compared with CMVs−) and decreased IL-1RA (−41% versus 0% and −49% versus +10%, respectively). However, multivariable analyses did not show an independent association between CMV reactivation and time trends of IL-6, IP-10, IL-10, or IL-1RA. Conclusion CMV reactivation was not independently associated with changes in the temporal trends of host response biomarkers in comparison with non-reactivating patients. Therefore, these markers should not be used as surrogate clinical endpoints for interventional studies evaluating anti-CMV therapy. Electronic supplementary material The online version of this article (10.1186/s13054-018-2261-0) contains supplementary material, which is available to authorized users.

Background: Anemia occurring as a result of inflammatory processes (anemia of inflammation, AI) has a high prevalence in critically ill patients. Knowledge on changes in iron metabolism during the course of AI is limited, hampering the development of strategies to counteract AI. This case control study aimed to investigate iron metabolism during the development of AI in critically ill patients. Methods: Iron metabolism in 30 patients who developed AI during ICU stay was compared with 30 septic patients with a high Hb and 30 non-septic patients with a high Hb. Patients were matched on age and sex. Longitudinally collected plasma samples were analyzed for levels of parameters of iron metabolism. A linear mixed model was used to assess the predictive values of the parameters. Results: In patients with AI, levels of iron, transferrin and transferrin saturation showed an early decrease compared to controls with a high Hb, already prior to the development of anemia. Ferritin, hepcidin and IL-6 levels were increased in AI compared to controls. During AI development, erythroferrone decreased. Differences in iron metabolism between groups were not influenced by APACHE IV score. Conclusions: The results show that in critically ill patients with AI, iron metabolism is already altered prior to the development of anemia. Levels of iron regulators in AI differ from septic controls with a high Hb, irrespective of disease severity. AI is characterized by high levels of hepcidin, ferritin and IL-6 and low levels of iron, transferrin and erythroferrone.

BACKGROUND: Enterococcal bacteremia has been associated with high case fatality, but it remains unknown to what extent death is caused by these infections. We therefore quantified attributable mortality of intensive care unit (ICU)-acquired bacteremia caused by enterococci. METHODS: From 2011 to 2013 we studied consecutive patients who stayed >48 hours in 2 tertiary ICUs in the Netherlands, using competing risk survival regression and marginal structural modeling to estimate ICU mortality caused by enterococcal bacteremia. RESULTS: Among 3080 admissions, 266 events of ICU-acquired bacteremia occurred in 218 (7.1%) patients, of which 76 were caused by enterococci (incidence rate, 3.0 per 1000 patient-days at risk; 95% confidence interval [CI], 2.3-3.7). A catheter-related bloodstream infection (CRBSI) was suspected in 44 (58%) of these, prompting removal of 68% of indwelling catheters and initiation of antibiotic treatment for a median duration of 3 (interquartile range 1-7) days. Enterococcal bacteremia was independently associated with an increased case fatality rate (adjusted subdistribution hazard ratio [SHR], 2.68; 95% CI, 1.44-4.98). However, for patients with CRBSI, case fatality was similar for infections caused by enterococci and coagulase-negative staphylococci (CoNS; adjusted SHR, 0.91; 95% CI, .50-1.67). Population-attributable fraction of mortality was 4.9% (95% CI, 2.9%-6.9%) by day 90, reflecting a population-attributable risk of 0.8% (95% CI, .4%-1.1%). CONCLUSIONS: ICU-acquired enterococcal bacteremia is associated with increased case fatality; however, the mortality attributable to these infections is low from a population perspective. The virulence of enterococci and CoNS in a setting of CRBSI seems comparable.

Additional file 1. Â Methods - Detailed description of the two study protocols Methods - Collected data and definitions Methods - Sample collection and assays. Table S1. Predisposing factors. Figure S1. Markers of inflammation, endothelial activation and epithelial activation in bronchoalveolar lavage fluid of ARDS patients. Table S2. Correlation between ACE, ACE2 activity, ACE2:ACE ratio and biomarkers of inflammation, endothelial activation and epithelial damage. Table S3. Association between age-groups and biomarkers in bronchoalveolar lavage fluid of ARDS patients.