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Prediction of mortality in adult patients with sepsis using six biomarkers: a systematic review and meta-analysis.

ABSTRACT: BACKGROUND:Angiopoietin-1 (Ang-1) and 2 (Ang-2), high mobility group box 1 (HMGB1), soluble receptor for advanced glycation endproducts (sRAGE), soluble triggering receptor expressed on myeloid cells 1 (sTREM1), and soluble urokinase-type plasminogen activator receptor (suPAR) have shown promising results for predicting all-cause mortality in critical care patients. The aim of our systematic review and meta-analysis was to assess the prognostic value of these biomarkers for mortality in adult patients with sepsis. METHODS:A systematic literature search of the MEDLINE, PubMed, EMBASE, and Cochrane Library databases, for articles in English published from 01.01.1990 onwards, was conducted. The systematic review focused exclusively on observational studies of adult patients with sepsis, any randomized trials were excluded. For the meta-analysis, only studies which provide biomarker concentrations within 24 h of admission in sepsis survivors and nonsurvivors were included. Results are presented as pooled mean differences (MD) between nonsurvivors and survivors with 95% confidence interval for each of the six biomarkers. Studies not included in the quantitative analysis were narratively summarized. The risk of bias was assessed in all included studies using the Quality in Prognosis Studies (QUIPS) tool. RESULTS:The systematic literature search retrieved 2285 articles. In total, we included 44 studies in the qualitative analysis, of which 28 were included in the meta-analysis. The pooled mean differences in biomarker concentration (nonsurvivors - survivors), measured at onset of sepsis, are listed as follows: (1) Ang-1: -?2.9 ng/ml (95% CI -?4.1 to -?1.7, p?

SUBMITTER: Pregernig A

PROVIDER: S-EPMC6841861 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Prediction of mortality in adult patients with sepsis using six biomarkers: a systematic review and meta-analysis.

Pregernig Andreas A Müller Mattia M Held Ulrike U Beck-Schimmer Beatrice B

Annals of intensive care 20191108 1

<h4>Background</h4>Angiopoietin-1 (Ang-1) and 2 (Ang-2), high mobility group box 1 (HMGB1), soluble receptor for advanced glycation endproducts (sRAGE), soluble triggering receptor expressed on myeloid cells 1 (sTREM1), and soluble urokinase-type plasminogen activator receptor (suPAR) have shown promising results for predicting all-cause mortality in critical care patients. The aim of our systematic review and meta-analysis was to assess the prognostic value of these biomarkers for mortality in ...[more]

PMID: 31705327

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Project description:BackgroundSepsis is a leading cause of death in intensive care units (ICUs), but outcomes of individual patients are difficult to predict. The recently developed clinical metabolomics has been recognized as a promising tool in the clinical practice of critical illness. The objective of this study was to identify the unique metabolic biomarkers and their pathways in the blood of sepsis nonsurvivors and to assess the prognostic value of these pathways.MethodsWe searched PubMed, EMBASE, Cochrane, Web of Science, CNKI, Wangfang Data, and CQVIP from inception until July 2019. Eligible studies included the metabolomic analysis of blood samples from sepsis patients with the outcome. The metabolic pathway was assigned to each metabolite biomarker. The meta-analysis was performed using the pooled fold changes, area under the receiver operating characteristic curve (AUROC), and vote-counting of metabolic pathways. We also conducted a prospective cohort metabolomic study to validate the findings of our meta-analysis.ResultsThe meta-analysis included 21 cohorts reported in 16 studies with 2509 metabolite comparisons in the blood of 1287 individuals. We found highly limited overlap of the reported metabolite biomarkers across studies. However, these metabolites were enriched in several death-related metabolic pathways (DRMPs) including amino acids, mitochondrial metabolism, eicosanoids, and lysophospholipids. Prediction of sepsis death using DRMPs yielded a pooled AUROC of 0.81 (95% CI 0.76-0.87), which was similar to the combined metabolite biomarkers with a merged AUROC of 0.82 (95% CI 0.78-0.86) (P > 0.05). A prospective metabolomic analysis of 188 sepsis patients (134 survivors and 54 nonsurvivors) using the metabolites from DRMPs produced an AUROC of 0.88 (95% CI 0.78-0.97). The sensitivity and specificity for the prediction of sepsis death were 80.4% (95% CI 66.9-89.4%) and 78.8% (95% CI 62.3-89.3%), respectively.ConclusionsDRMP analysis minimizes the discrepancies of results obtained from different metabolomic methods and is more practical than blood metabolite biomarkers for sepsis mortality prediction.Trial registrationThe meta-analysis was registered on OSF Registries, and the prospective cohort study was registered on the Chinese Clinical Trial Registry (ChiCTR1800015321).

Project description:ObjectiveCorticosteroids are a common option used in sepsis treatment. However, the efficacy and potential risk of corticosteroids in septic patients have not been well assessed. This review was performed to assess the efficacy and safety of corticosteroids in patients with sepsis.MethodsPubMed, Embase, and Cochrane library databases were searched from inception to March 2021. Randomized controlled trials (RCTs) that evaluated the effect of corticosteroids on patients with sepsis were included. The quality of outcomes in the included articles was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation methodology. The data were pooled by using risk ratio (RR) and mean difference (MD). The random-effects model was used to evaluate the pooled MD or RR and 95% confidence intervals (CIs).ResultsFifty RCTs that included 12,304 patients with sepsis were identified. Corticosteroids were not associated with the mortality in 28-day (RR, 0.94; 95% CI, 0.87-1.02; evidence rank, moderate) and long-term mortality (>60 days) (RR, 0.96; 95% CI, 0.88-1.05) in patients with sepsis (evidence rank, low). However, corticosteroids may exert a significant effect on the mortality in the intensive care unit (ICU) (RR, 0.9; 95% CI, 0.83-0.97), in-hospital (RR, 0.9; 95% CI, 0.82-0.99; evidence rank, moderate) in patients with sepsis or septic shock (evidence rank, low). Furthermore, corticosteroids probably achieved a tiny reduction in the length of hospital stay and ICU. Corticosteroids were associated with a higher risk of hypernatremia and hyperglycemia; furthermore, they appear to have no significant effect on superinfection and gastroduodenal bleeding.ConclusionsCorticosteroids had no significant effect on the 28-day and long-term mortality; however, they decreased the ICU and hospital mortality. The findings suggest that the clinical corticosteroids may be an effective therapy for patients with sepsis during the short time.Systematic review registrationhttps://inplasy.com/wp-content/uploads/2021/05/INPLASY-Protocol-1074-4.pdf.

Project description:PurposePregnancy-related critical illness leads to death for 3-14% of affected women. Although identifying patients at risk could facilitate preventive strategies, guide therapy, and help in clinical research, no prior systematic review of this literature exploring the validity of risk prediction models for maternal mortality exists. Therefore, we have systematically reviewed and meta-analyzed risk prediction models for maternal mortality.MethodsSearch strategy: MEDLINE, EMBASE and Scopus, from inception to May 2017. Selection criteria: Trials or observational studies evaluating risk prediction models for maternal mortality. Data collection and analysis: Two reviewers independently assessed studies for eligibility and methodological quality, and extracted data on prediction performance.ResultsThirty-eight studies that evaluated 12 different mortality prediction models were included. Mortality varied across the studies, with an average rate 10.4%, ranging from 0 to 41.7%. The Collaborative Integrated Pregnancy High-dependency Estimate of Risk (CIPHER) model and the Maternal Severity Index had the best performance, were developed and validated from studies of obstetric population with a low risk of bias. The CIPHER applies to critically ill obstetric patients (discrimination: area under the receiver operating characteristic curve (AUC) 0.823 (0.811-0.835), calibration: graphic plot [intercept-0.09, slope 0.92]). The Maternal Severity Index applies to hospitalized obstetric patients (discrimination: AUC 0.826 [0.802-0.851], calibration: standardized mortality ratio 1.02 [0.86-1.20]).ConclusionsDespite the high heterogeneity of the study populations and the limited number of studies validating the finally eligible prediction models, the CIPHER and the Maternal Severity Index are recommended for use among critically ill and hospitalized pregnant and postpartum women for risk adjustment in clinical research and quality improvement studies. Neither index has sufficient discrimination to be applicable for clinical decision making at the individual patient level.

Project description:Background: Sepsis is among the leading causes of death in intensive care units (ICUs) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis. Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU. Data Sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU. Study Eligibility Criteria: All peer-reviewed articles using machine learning for the prediction of sepsis onset in adult ICU patients were included. Studies focusing on patient populations outside the ICU were excluded. Study Appraisal and Synthesis Methods: A systematic review was performed according to the PRISMA guidelines. Moreover, a quality assessment of all eligible studies was performed. Results: Out of 974 identified articles, 22 and 21 met the criteria to be included in the systematic review and quality assessment, respectively. A multitude of machine learning algorithms were applied to refine the early prediction of sepsis. The quality of the studies ranged from "poor" (satisfying ≤ 40% of the quality criteria) to "very good" (satisfying ≥ 90% of the quality criteria). The majority of the studies (n = 19, 86.4%) employed an offline training scenario combined with a horizon evaluation, while two studies implemented an online scenario (n = 2, 9.1%). The massive inter-study heterogeneity in terms of model development, sepsis definition, prediction time windows, and outcomes precluded a meta-analysis. Last, only two studies provided publicly accessible source code and data sources fostering reproducibility. Limitations: Articles were only eligible for inclusion when employing machine learning algorithms for the prediction of sepsis onset in the ICU. This restriction led to the exclusion of studies focusing on the prediction of septic shock, sepsis-related mortality, and patient populations outside the ICU. Conclusions and Key Findings: A growing number of studies employs machine learning to optimize the early prediction of sepsis through digital biomarker discovery. This review, however, highlights several shortcomings of the current approaches, including low comparability and reproducibility. Finally, we gather recommendations how these challenges can be addressed before deploying these models in prospective analyses. Systematic Review Registration Number: CRD42020200133.

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Prediction of mortality in adult patients with sepsis using six biomarkers: a systematic review and meta-analysis.

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Prediction of mortality in adult patients with sepsis using six biomarkers: a systematic review and meta-analysis.

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