Evidence map›Paper›PMID 24349403›Full record

Trial reportPloS one2013

Utility of sepsis biomarkers and the infection probability score to discriminate sepsis and systemic inflammatory response syndrome in standard care patients.

Franz Ratzinger, Michael Schuardt, Katherina Eichbichler, Irene Tsirkinidou, Marlene Bauer, Helmuth Haslacher, Dieter Mitteregger, Michael Binder, Heinz Burgmann

Abstract readClinical TrialComparative Study
In one paragraph

Trial report in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

33 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Biomarkers of sepsis: time for a reappraisal.Critical care (London, England) · 2020
    Review
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  12. Observational
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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

9 authors.

Franz RatzingerDepartment of Laboratory Medicine, Division of Medical and Chemical Laboratory Diagnostics, Medical University of Vienna, Vienna, Austria.
Michael SchuardtDepartment of Medicine I, Division of Infectious Diseases and Tropical Medicine, Medical University of Vienna, Vienna, Austria.
Katherina EichbichlerDepartment of Medicine I, Division of Infectious Diseases and Tropical Medicine, Medical University of Vienna, Vienna, Austria.
Irene TsirkinidouDepartment of Medicine I, Division of Infectious Diseases and Tropical Medicine, Medical University of Vienna, Vienna, Austria.
Marlene BauerDepartment of Medicine I, Division of Infectious Diseases and Tropical Medicine, Medical University of Vienna, Vienna, Austria.
Helmuth HaslacherDepartment of Laboratory Medicine, Division of Medical and Chemical Laboratory Diagnostics, Medical University of Vienna, Vienna, Austria.
Dieter MittereggerDepartment of Laboratory Medicine, Division of Clinical Microbiology, Medical University of Vienna, Vienna, Austria.
Michael BinderDepartment of Dermatology, Division of General Dermatology, Medical University of Vienna, Vienna, Austria.
Heinz BurgmannDepartment of Medicine I, Division of Infectious Diseases and Tropical Medicine, Medical University of Vienna, Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Physicians are regularly faced with severely ill patients at risk of developing infections. In literature, standard care wards are often neglected, although their patients frequently suffer from a systemic inflammatory response syndrome (SIRS) of unknown origin. Fast identification of patients with infections is vital, as they immediately require appropriate therapy. Further, tools with a high negative predictive value (NPV) to exclude infection or bacteremia are important to increase the cost effectiveness of microbiological examinations and to avoid inappropriate antibiotic treatment. In this prospective cohort study, 2,384 patients with suspected infections were screened for suffering from two or more SIRS criteria on standard care wards. The infection probability score (IPS) and sepsis biomarkers with discriminatory power were assessed regarding their capacity to identify infection or bacteremia. In this cohort finally consisting of 298 SIRS-patients, the infection prevalence was 72%. Bacteremia was found in 25% of cases. For the prediction of infection, the IPS yielded 0.51 ROC-AUC (30.1% sensitivity, 64.6% specificity). Among sepsis biomarkers, lipopolysaccharide binding protein (LBP) was the best parameter with 0.63 ROC-AUC (57.5% sensitivity, 67.1% specificity). For the prediction of bacteremia, the IPS performed slightly better with a ROC-AUC of 0.58 (21.3% sensitivity, 65% specificity). Procalcitonin was the best discriminator with 0.78 ROC-AUC, 86.3% sensitivity, 59.6% specificity and 92.9% NPV. Furthermore, bilirubin and LBP (ROC-AUC: 0.65, 0.62) might also be considered as useful parameters. In summary, the IPS and widely used infection parameters, including CRP or WBC, yielded a poor diagnostic performance for the detection of infection or bacteremia. Additional sepsis biomarkers do not aid in discriminating inflammation from infection. For the prediction of bacteremia procalcitonin, and bilirubin were the most promising parameters, which might be used as a rule for when to take blood cultures or using nucleic acid amplification tests for microbiological diagnostics.

Indexed as

Acute-Phase ProteinsAdultAgedBacteremiaBiomarkersCalcitoninCalcitonin Gene-Related PeptideCarrier ProteinsCohort StudiesFemaleHumansLipopolysaccharide-Binding ProteinMaleMembrane GlycoproteinsMiddle AgedProspective StudiesAcute-Phase ProteinsBiomarkersCALCA protein, humanCalcitoninCalcitonin Gene-Related PeptideCarrier ProteinsLipopolysaccharide-Binding ProteinMembrane GlycoproteinsProtein Precursors

Identifiers

PMID24349403
PMCPMC3859603

What OpenQuestion holds

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Registered trials

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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.