Esther Witteveen

Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study

BACKGROUND Host responses during sepsis are highly heterogeneous, which hampers the identification of patients at high risk of mortality and their selection for targeted therapies. In this study, we aimed to identify biologically relevant molecular endotypes in patients with sepsis. METHODS This was a prospective observational cohort study that included consecutive patients admitted for sepsis to two intensive care units (ICUs) in the Netherlands between Jan 1, 2011, and July 20, 2012 (discovery and first validation cohorts) and patients admitted with sepsis due to community-acquired pneumonia to 29 ICUs in the UK (second validation cohort). We generated genome-wide blood gene expression profiles from admission samples and analysed them by unsupervised consensus clustering and machine learning. The primary objective of this study was to establish endotypes for patients with sepsis, and assess the association of these endotypes with clinical traits and survival outcomes. We also established candidate biomarkers for the endotypes to allow identification of patient endotypes in clinical practice. FINDINGS The discovery cohort had 306 patients, the first validation cohort had 216, and the second validation cohort had 265 patients. Four molecular endotypes for sepsis, designated Mars1-4, were identified in the discovery cohort, and were associated with 28-day mortality (log-rank p=0·022). In the discovery cohort, the worst outcome was found for patients classified as having a Mars1 endotype, and at 28 days, 35 (39%) of 90 people with a Mars1 endotype had died (hazard ratio [HR] vs all other endotypes 1·86 [95% CI 1·21-2·86]; p=0·0045), compared with 23 (22%) of 105 people with a Mars2 endotype (HR 0·64 [0·40-1·04]; p=0·061), 16 (23%) of 71 people with a Mars3 endotype (HR 0·71 [0·41-1·22]; p=0·19), and 13 (33%) of 40 patients with a Mars4 endotype (HR 1·13 [0·63-2·04]; p=0·69). Analysis of the net reclassification improvement using a combined clinical and endotype model significantly improved risk prediction to 0·33 (0·09-0·58; p=0·008). A 140-gene expression signature reliably stratified patients with sepsis to the four endotypes in both the first and second validation cohorts. Only Mars1 was consistently significantly associated with 28-day mortality across the cohorts. To facilitate possible clinical use, a biomarker was derived for each endotype; BPGM and TAP2 reliably identified patients with a Mars1 endotype. INTERPRETATION This study provides a method for the molecular classification of patients with sepsis to four different endotypes upon ICU admission. Detection of sepsis endotypes might assist in providing personalised patient management and in selection for trials. FUNDING Center for Translational Molecular Medicine, Netherlands.

Epidemiology, management and risk-adjusted mortality of ICU-acquired enterococcal bacteremia

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.

Early prediction of intensive care unit-acquired weakness using easily available parameters: a prospective observational study.

Introduction An early diagnosis of Intensive Care Unit–acquired weakness (ICU–AW) using muscle strength assessment is not possible in most critically ill patients. We hypothesized that development of ICU–AW can be predicted reliably two days after ICU admission, using patient characteristics, early available clinical parameters, laboratory results and use of medication as parameters. Methods Newly admitted ICU patients mechanically ventilated ≥2 days were included in this prospective observational cohort study. Manual muscle strength was measured according to the Medical Research Council (MRC) scale, when patients were awake and attentive. ICU–AW was defined as an average MRC score <4. A prediction model was developed by selecting predictors from an a–priori defined set of candidate predictors, based on known risk factors. Discriminative performance of the prediction model was evaluated, validated internally and compared to the APACHE IV and SOFA score. Results Of 212 included patients, 103 developed ICU–AW. Highest lactate levels, treatment with any aminoglycoside in the first two days after admission and age were selected as predictors. The area under the receiver operating characteristic curve of the prediction model was 0.71 after internal validation. The new prediction model improved discrimination compared to the APACHE IV and the SOFA score. Conclusion The new early prediction model for ICU–AW using a set of 3 easily available parameters has fair discriminative performance. This model needs external validation.

Muscle and nerve inflammation in intensive care unit-acquired weakness: a systematic translational review

BACKGROUND Intensive care unit-acquired weakness (ICU-AW) is an important complication of critical illness. The main risk factors, sepsis and the systemic inflammatory response syndrome, suggest an inflammatory pathogenesis. In this systematic translational review we summarize current knowledge on inflammation in muscle and nerve tissue in animal models of ICU-AW and in critically ill patients with ICU-AW. METHODS We conducted a systematic search in the databases of MEDLINE, EMBASE and Web of Science using predefined search and selection criteria. From the included studies we extracted data on study characteristics and on inflammation in muscle and nerve tissue. RESULTS The literature search yielded 349 unique articles, of which 12 animal studies and 20 human studies fulfilled the in- and exclusion criteria. All studies had important shortcomings in methodological quality. In the animal studies, inflammation of muscle tissue was found, represented by cellular infiltration and increased local levels of various inflammatory mediators. In human studies, high levels of various inflammatory mediators were found in muscle and nerve tissue of ICU-AW patients. CONCLUSION This systematic translational review suggests a role for local inflammation in ICU-AW, but the available evidence is limited and studies have severe methodological limitations.

Neurofilaments as a plasma biomarker for ICU-acquired weakness: an observational pilot study

IntroductionEarly diagnosis of intensive care unit – acquired weakness (ICU-AW) using the current reference standard, that is, assessment of muscle strength, is often hampered due to impaired consciousness. Biological markers could solve this problem but have been scarcely investigated. We hypothesized that plasma levels of neurofilaments are elevated in ICU-AW and can diagnose ICU-AW before muscle strength assessment is possible.MethodsFor this prospective observational cohort study, neurofilament levels were measured using ELISA (NfHSMI35 antibody) in daily plasma samples (index test). When patients were awake and attentive, ICU-AW was diagnosed using the Medical Research Council scale (reference standard). Differences and discriminative power (using the area under the receiver operating characteristic curve; AUC) of highest and cumulative (calculated using the area under the neurofilament curve) neurofilament levels were investigated in relation to the moment of muscle strength assessment for each patient.ResultsBoth the index test and reference standard were available for 77 ICU patients. A total of 18 patients (23%) fulfilled the clinical criteria for ICU-AW. Peak neurofilament levels were higher in patients with ICU-AW and had good discriminative power (AUC: 0.85; 95% CI: 0.72 to 0.97). However, neurofilament levels did not peak before muscle strength assessment was possible. Highest or cumulative neurofilament levels measured before muscle strength assessment could not diagnose ICU-AW (AUC 0.59; 95% CI 0.37 to 0.80 and AUC 0.57; 95% CI 0.32 to 0.81, respectively).ConclusionsPlasma neurofilament levels are raised in ICU-AW and may serve as a biological marker for ICU-AW. However, our study suggests that an early diagnosis of ICU-AW, before muscle strength assessment, is not possible using neurofilament levels in plasma.

Is gentamicin affecting the neuromuscular system of critically ill patients

Dear Editor, Intensive care unit-acquired weakness (ICU-AW) is a common and important neuromuscular complication of critical illness [1]. No recognized treatment for ICU-AW is available [1], increasing the need for prevention whenever possible. Currently advocated preventive measures are aimed at controlling risk factors, like avoiding hyperglycemia and minimizing use of corticosteroids [1]. Aminoglycosides may be another modifiable risk factor, although findings differ across studies [1]. In a previous study, we identified early aminoglycoside treatment as a predictor of ICU-AW [2]. In that study, we did not assess the association between aminoglycoside serum levels, which is a more reliable indicator of actual exposure, and ICU-AW or correct for the influence of possible confounders. Therefore, we conducted this post hoc analysis to investigate if there is an exposure-dependent association, independent of possible confounders, between cumulative gentamicin serum exposure in the first 2 days after ICU admission and ICU-AW. Study methodology is described in the electronic supplementary material. A cohort of 212 critically patients, 103 diagnosed with ICU-AW, were included. Clinical characteristics are described elsewhere [2]. Gentamicin was administered in 51/103 (50 %) of the patients with ICU-AW compared to 30/109 (28 %; p \ 0.01) patients without ICU-AW within the first 2 days after ICU admission. Distributions of cumulative gentamicin dosage and the area under the concentration–time curve until 48 h after administration (AUC0–48h) levels for all patients and for patients treated with gentamicin are shown in Fig. E1 in the electronic supplementary material. Table 1 displays the univariable and multivariable analyses. Treatment with gentamicin was independently associated with ICUAW. Both cumulative gentamicin dosage and AUC0–48h levels had a positive and exposure-dependent association with ICU-AW. No other antibiotics were associated with ICU-AW, except for vancomycin (Table E1 in the electronic supplementary material). When both cumulative serum gentamicin and vancomycin exposure were included in the model, the odds ratio for developing ICU-AW for every 100 mg/L increase in AUC0–48h was 1.36 (95 % CI 1.06–1.77; p = 0.02) for gentamicin and 1.11 (95 % CI 1.01–1.24; p = 0.04) for vancomycin. Results were similar when missing data was imputed (Table E2 in the electronic supplementary material). Important limitations of our analysis are that no causal relation can be deduced from this observational study, with the inherent risk of residual confounding. Also, electrophysiological studies and muscle biopsies were not used to differentiate between the disorders causing ICU-AW. The underlying mechanisms by which gentamicin may cause or contribute to development of ICU-AW

Early Prediction of Intensive Care Unit–Acquired Weakness: A Multicenter External Validation Study

Objectives: An early diagnosis of intensive care unit–acquired weakness (ICU-AW) is often not possible due to impaired consciousness. To avoid a diagnostic delay, we previously developed a prediction model, based on single-center data from 212 patients (development cohort), to predict ICU-AW at 2 days after ICU admission. The objective of this study was to investigate the external validity of the original prediction model in a new, multicenter cohort and, if necessary, to update the model. Methods: Newly admitted ICU patients who were mechanically ventilated at 48 hours after ICU admission were included. Predictors were prospectively recorded, and the outcome ICU-AW was defined by an average Medical Research Council score <4. In the validation cohort, consisting of 349 patients, we analyzed performance of the original prediction model by assessment of calibration and discrimination. Additionally, we updated the model in this validation cohort. Finally, we evaluated a new prediction model based on all patients of the development and validation cohort. Results: Of 349 analyzed patients in the validation cohort, 190 (54%) developed ICU-AW. Both model calibration and discrimination of the original model were poor in the validation cohort. The area under the receiver operating characteristics curve (AUC-ROC) was 0.60 (95% confidence interval [CI]: 0.54-0.66). Model updating methods improved calibration but not discrimination. The new prediction model, based on all patients of the development and validation cohort (total of 536 patients) had a fair discrimination, AUC-ROC: 0.70 (95% CI: 0.66-0.75). Conclusions: The previously developed prediction model for ICU-AW showed poor performance in a new independent multicenter validation cohort. Model updating methods improved calibration but not discrimination. The newly derived prediction model showed fair discrimination. This indicates that early prediction of ICU-AW is still challenging and needs further attention.

Causes of Mortality in ICU-Acquired Weakness

Background: Intensive care unit–acquired weakness (ICU-AW) is a common complication of critical illness and is associated with increased mortality, longer mechanical ventilation and longer hospital stay. Little is known about the causes of mortality in patients with ICU-AW. In this study, we aimed to give an overview of the causes of death in a population diagnosed with ICU-AW during hospital admission. Methods: Data from a prospective cohort study in the mixed medical–surgical ICU of the Academic Medical Center in Amsterdam were used. Patients were included when mechanically ventilated for more than 48 hours. Intensive care unit–acquired weakness was defined as a mean medical research council score <4. Baseline data and data on the time of death were collected. Results: Fifty-three patients were included. Irreversible shock with multiple organ failure (MOF) was the most common cause of death (28/53 of patients; 26 patients with septic shock and 2 patients with hypovolemic shock). Most common site of sepsis was abdominal (38.5%) and pulmonary (19.2%). On admission to the ICU, 53% had a do-not-resuscitate code. In 74% of the patients, further treatment limitations were implemented during their ICU stay. Conclusion: In this cohort of patients with ICU-AW, most patients died of irreversible shock with MOF, caused by sepsis.

Assessment of intensive care unit‐acquired weakness in young and old mice: An E. coli septic peritonitis model

Introduction: There are few reports of in vivo muscle strength measurements in animal models of ICU‐acquired weakness (ICU‐AW). In this study we investigated whether the Escherichia coli (E. coli) septic peritonitis mouse model may serve as an ICU‐AW model using in vivo strength measurements and myosin/actin assays, and whether development of ICU‐AW is age‐dependent in this model. Methods: Young and old mice were injected intraperitoneally with E. coli and treated with ceftriaxone. Forelimb grip strength was measured at multiple time points, and the myosin/actin ratio in muscle was determined. Results: E. coli administration was not associated with grip strength decrease, neither in young nor in old mice. In old mice, the myosin/actin ratio was lower in E. coli mice at t = 48 h and higher at t = 72 h compared with controls. Conclusions: This E. coli septic peritonitis mouse model did not induce decreased grip strength. In its current form, it seems unsuitable as a model for ICU‐AW. Muscle Nerve 53: 127–133, 2016

Assessment of ICU‐acquired weakness in young and old mice: an E. coli septic peritonitis model

Introduction: There are few reports of in vivo muscle strength measurements in animal models of ICU‐acquired weakness (ICU‐AW). In this study we investigated whether the Escherichia coli (E. coli) septic peritonitis mouse model may serve as an ICU‐AW model using in vivo strength measurements and myosin/actin assays, and whether development of ICU‐AW is age‐dependent in this model. Methods: Young and old mice were injected intraperitoneally with E. coli and treated with ceftriaxone. Forelimb grip strength was measured at multiple time points, and the myosin/actin ratio in muscle was determined. Results: E. coli administration was not associated with grip strength decrease, neither in young nor in old mice. In old mice, the myosin/actin ratio was lower in E. coli mice at t = 48 h and higher at t = 72 h compared with controls. Conclusions: This E. coli septic peritonitis mouse model did not induce decreased grip strength. In its current form, it seems unsuitable as a model for ICU‐AW. Muscle Nerve 53: 127–133, 2016