Evidence map›Paper›PMID 31705327›Full record

ArticleAnnals of intensive care2019

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

Andreas Pregernig, Mattia Müller, Ulrike Held, Beatrice Beck-Schimmer

Open access · goldAbstract read
In one paragraph

Article in Annals of intensive care, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 2 pooled it
4.8field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Review
  7. Observational
  8. Article
  9. Article
  10. Article
  11. Whole blood ratio ofFrontiers in medicine · 2024
    Article
  12. Review
  13. Biomarkers in sepsis-looking for the Holy Grail or chasing a mirage!World journal of critical care medicine · 2023
    Review
  14. Review
  15. Article
  16. Blood Lactate in Early Sepsis: A Predictor to "Keep Up" Rather than "Catch Up".Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2023
    Article
  17. Article
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Andreas PregernigInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland.
Mattia MüllerInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland.
Ulrike HeldDepartment of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, 8001, Zurich, Switzerland.
Beatrice Beck-SchimmerInstitute of Anesthesiology, University of Zurich, University Hospital Zurich, Rämistrasse 100, CH-8091, Zurich, Switzerland. beatrice.beckschimmer@uzh.ch.ORCID http://orcid.org/0000-0002-0648-6115
University of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAngiopoietin-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.

methodsA 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.

resultsThe 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 < 0.01); (2) Ang-2: 4.9 ng/ml (95% CI 2.6 to 7.1, p < 0.01); (3) HMGB1: 1.2 ng/ml (95% CI 0.0 to 2.4, p = 0.05); (4) sRAGE: 1003 pg/ml (95% CI 628 to 1377, p < 0.01); (5) sTREM-1: 87 pg/ml (95% CI 2 to 171, p = 0.04); (6) suPAR: 5.2 ng/ml (95% CI 4.5 to 6.0, p < 0.01).

conclusionsAng-1, Ang-2, and suPAR provide beneficial prognostic information about mortality in adult patients with sepsis. The further development of standardized assays and the assessment of their performance when included in panels with other biomarkers may be recommended. Trial registration This study was recorded on PROSPERO, prospective register of systematic reviews, under the registration ID: CRD42018081226.

Indexed as

BiomarkerMeta-analysisMortalityPrognosisSepsisSystematic review

Identifiers

PMID31705327
PMCPMC6841861
OpenAlexW2986845802

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.