Evidence map›Paper›PMID 39624382›Full record

ArticleERJ open research2024

Biomarkers troponin and procalcitonin in addition to CRB-65 enhance risk stratification in patients with community-acquired pneumonia.

Imrana Farhat, Maciej Rosolowski, Katharina Ahrens, Jasmin Lienau, Peter Ahnert, Mathias Pletz, Gernot Rohde, Jan Rupp, Markus Scholz, Martin Witzenrath and 1 more

Abstract read
In one paragraph

Article in ERJ open research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

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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

11 authors.

Imrana FarhatUniversity of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany.
Maciej RosolowskiUniversity of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany.
Katharina AhrensCharité - Universitaetsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany.
Jasmin LienauCharité - Universitaetsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany.
Peter AhnertUniversity of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany.ORCID https://orcid.org/0000-0002-1771-0856
Mathias PletzJena University Hospital, Institute of Infectious Diseases and Infection Control, Jena, Germany.ORCID https://orcid.org/0000-0001-8157-2753
Gernot RohdeCAPNETZ STIFTUNG, Hannover, Germany.
Jan RuppCAPNETZ STIFTUNG, Hannover, Germany.ORCID https://orcid.org/0000-0001-8722-1233
Markus ScholzUniversity of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, Germany.
Martin WitzenrathCharité - Universitaetsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany.
CAPNETZ Study Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Community-acquired pneumonia (CAP) remains a leading cause of infectious disease mortality globally, necessitating intensive care unit (ICU) admission for ∼10% of hospitalised patients. Accurate prediction of disease severity facilitates timely therapeutic interventions. Methods: Our study aimed to enhance the predictive capacity of the clinical CRB-65 score by evaluating eight candidate biomarkers: troponin T high-sensitive (TnT-hs), procalcitonin (PCT), N-terminal pro-brain natriuretic peptide, angiopoietin-2, copeptin, endothelin-1, lipocalin-2 and mid-regional pro-adrenomedullin. We utilised a machine-learning approach on 800 samples from the German CAPNETZ network (competence network for CAP) to refine risk prediction models combining these biomarkers with the CRB-65 score regarding our defined end-point: death or ICU admission during the current CAP episode within 28 days after study inclusion. Results: Elevated levels of biomarkers were associated with the end-point. TnT-hs exhibited the highest predictive performance among individual features (area under the receiver operating characteristic curve, AUC=0.74), followed closely by PCT (AUC=0.73). Combining biomarkers with the CRB-65 score significantly improved prediction accuracy. The combined model of CRB-65, TnT-hs and PCT demonstrated the best balance between high predictive value and parsimony, with an AUC of 0.77 (95% CI: 0.72-0.82), while CRB-65 alone achieved an AUC of 0.67 (95% CI: 0.64-0.73). Conclusion: Our findings suggest that augmenting the CRB-65 score with TnT-hs and PCT enhances the prediction of death or ICU admission in hospitalised CAP patients. Validation of this improved risk score in additional CAP cohorts and prospective clinical studies is warranted to assess its broad clinical utility.

Identifiers

PMID39624382
PMCPMC11609962

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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.