Evidence map›Paper›PMID 25255212›Full record

ArticlePloS one2014

Angiopoietin-1, angiopoietin-2 and bicarbonate as diagnostic biomarkers in children with severe sepsis.

Kun Wang, Vineet Bhandari, John S Giuliano, Corey S O Hern, Mark D Shattuck, Michael Kirby

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

17 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Review
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  8. Observational
  9. Article
  10. Observational
  11. Observational
  12. Review
  13. Could Biomarkers Direct Therapy for the Septic Patient?The Journal of pharmacology and experimental therapeutics · 2016
    Review
  14. Article
  15. Review
  16. The Role of Angiopoietine-2 in the Diagnosis and Prognosis of Sepsis.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2015
    Article
  17. Use of biomarkers in pediatric sepsis: literature review.Revista Brasileira de terapia intensiva
    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

6 authors at 3 institutions in 1 country.

Kun WangDepartment of Mathematics, Colorado State University, Fort Collins, Colorado, United States of America; Department of Mechanical Engineering & Materials Science, Yale University, New Haven, Connecticut, United States of America.
Vineet BhandariDepartment of Pediatrics, Yale University School of Medicine, New Haven, Connecticut, United States of America.
John S GiulianoDepartment of Pediatrics, Yale University School of Medicine, New Haven, Connecticut, United States of America.
Corey S O HernDepartment of Mechanical Engineering & Materials Science, Yale University, New Haven, Connecticut, United States of America; Department of Applied Physics, Department of Physics, and Graduate Program in Computational Biology & Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
Mark D ShattuckBenjamin Levich Institute and Physics Department, The City College of New York, New York, New York, United States of America.
Michael KirbyDepartment of Mathematics, Colorado State University, Fort Collins, Colorado, United States of America.
Yale University · USColorado State University · USCity College of New York · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe pediatric sepsis continues to be associated with high mortality rates in children. Thus, an important area of biomedical research is to identify biomarkers that can classify sepsis severity and outcomes. The complex and heterogeneous nature of sepsis makes the prospect of the classification of sepsis severity using a single biomarker less likely. Instead, we employ machine learning techniques to validate the use of a multiple biomarkers scoring system to determine the severity of sepsis in critically ill children. The study was based on clinical data and plasma samples provided by a tertiary care center's Pediatric Intensive Care Unit (PICU) from a group of 45 patients with varying sepsis severity at the time of admission. Canonical Correlation Analysis with the Forward Selection and Random Forests methods identified a particular set of biomarkers that included Angiopoietin-1 (Ang-1), Angiopoietin-2 (Ang-2), and Bicarbonate (HCO[Formula: see text]) as having the strongest correlations with sepsis severity. The robustness and effectiveness of these biomarkers for classifying sepsis severity were validated by constructing a linear Support Vector Machine diagnostic classifier. We also show that the concentrations of Ang-1, Ang-2, and HCO[Formula: see text] enable predictions of the time dependence of sepsis severity in children.

Indexed as

Age FactorsAngiopoietin-1Angiopoietin-2BicarbonatesBiomarkersCluster AnalysisHumansIntensive Care Units, PediatricPrognosisReproducibility of ResultsSensitivity and SpecificitySepsisSeverity of Illness IndexSupport Vector MachineAngiopoietin-1Angiopoietin-2BicarbonatesBiomarkers

Identifiers

PMID25255212
PMCPMC4178003
OpenAlexW2040932826

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.