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Transcriptional instability during evolving sepsis may limit biomarker based risk stratification.

ABSTRACT: Sepsis causes extensive morbidity and mortality in children worldwide. Prompt recognition and timely treatment of sepsis is critical in reducing morbidity and mortality. Genomic approaches are used to discover novel pathways, therapeutic targets and biomarkers. These may facilitate diagnosis and risk stratification to tailor treatment strategies.To investigate the temporal gene expression during the evolution of sepsis induced multi-organ failure in response to a single organism, Neisseria meningitidis, in previously healthy children.RNA was extracted from serial blood samples (6 time points over 48 hours from presentation) from five critically ill children with meningococcal sepsis. Extracted RNA was hybridized to Affymetrix arrays. The RNA underwent strict quality control and standardized quantitation. Gene expression results were analyzed using GeneSpring software and Ingenuity Pathway Analysis.A marked variability in differential gene expression was observed between time points and between patients revealing dynamic expression changes during the evolution of sepsis. While there was evidence of time-dependent changes in expected gene networks including those involving immune responses and inflammatory pathways, temporal variation was also evident in specific "biomarkers" that have been proposed for diagnostic and risk stratification functions. The extent and nature of this variability was not readily explained by clinical phenotype.This is the first study of its kind detailing extensive expression changes in children during the evolution of sepsis. This highlights a limitation of static or single time point biomarker estimation. Serial estimations or more comprehensive network approaches may be required to optimize risk stratification in complex, time-critical conditions such as evolving sepsis.

SUBMITTER: Kwan A

PROVIDER: S-EPMC3609793 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Transcriptional instability during evolving sepsis may limit biomarker based risk stratification.

Kwan Antonia A Hubank Mike M Rashid Asrar A Klein Nigel N Peters Mark J MJ

PloS one 20130327 3

<h4>Background</h4>Sepsis causes extensive morbidity and mortality in children worldwide. Prompt recognition and timely treatment of sepsis is critical in reducing morbidity and mortality. Genomic approaches are used to discover novel pathways, therapeutic targets and biomarkers. These may facilitate diagnosis and risk stratification to tailor treatment strategies.<h4>Objective</h4>To investigate the temporal gene expression during the evolution of sepsis induced multi-organ failure in response ...[more]

PMID: 23544148

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Project description:OBJECTIVES:The original Pediatric Sepsis Biomarker Risk Model and revised (Pediatric Sepsis Biomarker Risk Model-II) biomarker-based risk prediction models have demonstrated utility for estimating baseline 28-day mortality risk in pediatric sepsis. Given the paucity of prediction tools in pediatric acute respiratory distress syndrome, and given the overlapping pathophysiology between sepsis and acute respiratory distress syndrome, we tested the utility of Pediatric Sepsis Biomarker Risk Model and Pediatric Sepsis Biomarker Risk Model-II for mortality prediction in a cohort of pediatric acute respiratory distress syndrome, with an a priori plan to revise the model if these existing models performed poorly. DESIGN:Prospective observational cohort study. SETTING:University affiliated PICU. PATIENTS:Mechanically ventilated children with acute respiratory distress syndrome. INTERVENTIONS:Blood collection within 24 hours of acute respiratory distress syndrome onset and biomarker measurements. MEASUREMENTS AND MAIN RESULTS:In 152 children with acute respiratory distress syndrome, Pediatric Sepsis Biomarker Risk Model performed poorly and Pediatric Sepsis Biomarker Risk Model-II performed modestly (areas under receiver operating characteristic curve of 0.61 and 0.76, respectively). Therefore, we randomly selected 80% of the cohort (n = 122) to rederive a risk prediction model for pediatric acute respiratory distress syndrome. We used classification and regression tree methodology, considering the Pediatric Sepsis Biomarker Risk Model biomarkers in addition to variables relevant to acute respiratory distress syndrome. The final model was comprised of three biomarkers and age, and more accurately estimated baseline mortality risk (area under receiver operating characteristic curve 0.85, p < 0.001 and p = 0.053 compared with Pediatric Sepsis Biomarker Risk Model and Pediatric Sepsis Biomarker Risk Model-II, respectively). The model was tested in the remaining 20% of subjects (n = 30) and demonstrated similar test characteristics. CONCLUSIONS:A validated, biomarker-based risk stratification tool designed for pediatric sepsis was adapted for use in pediatric acute respiratory distress syndrome. The newly derived Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model demonstrates good test characteristics internally and requires external validation in a larger cohort. Tools such as Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model have the potential to provide improved risk stratification and prognostic enrichment for future trials in pediatric acute respiratory distress syndrome.

Project description:BackgroundSepsis is now operationally defined as life-threatening organ dysfunction caused by an infection, identified by an acute change in SOFA-Score of at least two points, including clinical chemistry such as creatinine or bilirubin concentrations. However, little knowledge exists about organ-specific microRNAs as potentially new biomarkers. Accordingly, we tested the hypotheses that micro-RNA-122, the foremost liver-related micro-RNA (miR), 1) discriminates between sepsis and infection, 2) is an early predictor for mortality, and 3) improves the prognostic value of the SOFA-score.MethodsWe analyzed 108 patients with sepsis (infection + increase SOFA-Score ≥2) within the first 24h of ICU admission and as controls 20 patients with infections without sepsis (infection + SOFA-Score ≤1). Total circulating miR was isolated from serum and relative miR-122 expression was measured (using spiked-in cel-miR-54) and associated with 30-day survival.Results30-day survival of the sepsis patients was 63%. miR-122 expression was 40-fold higher in non-survivors (p = 0.001) and increased almost 6-fold in survivors (p = 0.013) compared to controls. miR-122 serum-expression discriminated both between sepsis vs. infection (AUC 0.760, sensitivity 58.3%, specificity 95%) and survivors vs. non-survivors (AUC 0.728, sensitivity 42.5%, specificity 94%). Multivariate Cox-regression analysis revealed miR-122 (HR 4.3; 95%-CI 2.0-8.9, p<0.001) as independent prognostic factor for 30-day mortality. Furthermore, the predictive value for 30-day mortality of the SOFA-Score (AUC 0.668) was improved by adding miR-122 (AUC 0.743; net reclassification improvement 0.37, p<0.001; integrated discrimination improvement 0.07, p = 0.007).ConclusionsIncreased miR-122 serum concentration supports the discrimination between infection and sepsis, is an early and independent risk factor for 30-day mortality, and improves the prognostic value of the SOFA-Score, suggesting a potential role for miR-122 in sepsis-related prediction models.

Project description:IntroductionThe intrinsic heterogeneity of clinical septic shock is a major challenge. For clinical trials, individual patient management, and quality improvement efforts, it is unclear which patients are least likely to survive and thus benefit from alternative treatment approaches. A robust risk stratification tool would greatly aid decision-making. The objective of our study was to derive and test a multi-biomarker-based risk model to predict outcome in pediatric septic shock.MethodsTwelve candidate serum protein stratification biomarkers were identified from previous genome-wide expression profiling. To derive the risk stratification tool, biomarkers were measured in serum samples from 220 unselected children with septic shock, obtained during the first 24 hours of admission to the intensive care unit. Classification and Regression Tree (CART) analysis was used to generate a decision tree to predict 28-day all-cause mortality based on both biomarkers and clinical variables. The derived tree was subsequently tested in an independent cohort of 135 children with septic shock.ResultsThe derived decision tree included five biomarkers. In the derivation cohort, sensitivity for mortality was 91% (95% CI 70 - 98), specificity was 86% (80 - 90), positive predictive value was 43% (29 - 58), and negative predictive value was 99% (95 - 100). When applied to the test cohort, sensitivity was 89% (64 - 98) and specificity was 64% (55 - 73). In an updated model including all 355 subjects in the combined derivation and test cohorts, sensitivity for mortality was 93% (79 - 98), specificity was 74% (69 - 79), positive predictive value was 32% (24 - 41), and negative predictive value was 99% (96 - 100). False positive subjects in the updated model had greater illness severity compared to the true negative subjects, as measured by persistence of organ failure, length of stay, and intensive care unit free days.ConclusionsThe pediatric sepsis biomarker risk model (PERSEVERE; PEdiatRic SEpsis biomarkEr Risk modEl) reliably identifies children at risk of death and greater illness severity from pediatric septic shock. PERSEVERE has the potential to substantially enhance clinical decision making, to adjust for risk in clinical trials, and to serve as a septic shock-specific quality metric.

Project description:ObjectivesThere has been renewed interest in lactate as a risk biomarker in sepsis and septic shock. However, the ability of the odds ratio (OR) and change in the area under the receiver operator characteristic curve (AUC-ROC) to assess biomarker added-value has been questioned.Design, setting and participantsA sepsis cohort was identified from the ICU database of an Australian tertiary referral hospital using APACHE III diagnostic codes. Demographic information, APACHE III scores, 24-hour post-admission patient lactate levels, and hospital mortality were accessed.Measurements and main resultsHospital mortality was modelled using a base predictive logistic regression model and sequential addition of admission lactate, lactate clearance ([lactateadmission-lactatefinal]/lactateadmission), and area under the lactate-time curve (LTC). Added-value was assessed using lactate index OR; AUC-ROC difference (base-model versus lactate index addition); net (mortality) reclassification index (NRI; range -2 to +2); and net benefit (NB), the number of true positives per patient adjusted for the number of false positives. The data set comprised 717 patients with mean(SD) age and APACHE III score 61.1(16.5) years and 68.3(28.2) respectively; 59.2% were male. Admission lactate was 2.3(2.5) mmol/l; with lactate of ? 4 mmol/L (37% hospital mortality) in 17% and patients with lactate < 4 mmol/L having 18% hospital mortality. The admission base-model had an AUC-ROC = 0.81 with admission lactate OR = 1.127 (95%CI: 1.038, 1.224), AUC-ROC difference of 0.0032 (-0.0037, 0.01615; P = 0.61), and NRI 0.240(0.030, 0.464). The over-time model had an AUC-ROC = 0.86 with (i) clearance OR = 0.771, 95%CI: 0.578, 1.030; P = 0.08; AUC-ROC difference 0.001 (-0.003, 0.014; P = 0.78), and NRI 0.109(-0.193, 0.425) and (ii) LTC OR = 0.997, 95%CI: 0.989, 1.005, P = 0.49; AUC-ROC difference 0.004 (-0.002, 0.004; P = 0.34), and NRI 0.111(-0.222, 0.403). NB was not incremented by any lactate index.ConclusionsLactate added-value assessment is dependent upon the performance of the underlying predictive model and should incorporate risk reclassification and net benefit measures.

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Transcriptional instability during evolving sepsis may limit biomarker based risk stratification.

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Transcriptional instability during evolving sepsis may limit biomarker based risk stratification.

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