IL-1R2-based biomarker models predict melioidosis mortality independent of clinical data
Melioidosis is an often-fatal tropical infectious disease caused by the Gram-negative bacillus , but few studies have identified promising biomarker candidates to predict outcome. In 78 prospectively enrolled patients hospitalized with melioidosis, six candidate protein biomarkers, identified from t...
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Veröffentlicht in: | Frontiers in medicine 2023-06, Vol.10, p.1211265-1211265 |
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Hauptverfasser: | , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Melioidosis is an often-fatal tropical infectious disease caused by the Gram-negative bacillus
, but few studies have identified promising biomarker candidates to predict outcome.
In 78 prospectively enrolled patients hospitalized with melioidosis, six candidate protein biomarkers, identified from the literature, were measured in plasma at enrollment. A multi-biomarker model was developed using least absolute shrinkage and selection operator (LASSO) regression, and mortality discrimination was compared to a clinical variable model by receiver operating characteristic curve analysis. Mortality prediction was confirmed in an external validation set of 191 prospectively enrolled patients hospitalized with melioidosis.
LASSO regression selected IL-1R2 and soluble triggering receptor on myeloid cells 1 (sTREM-1) for inclusion in the candidate biomarker model. The areas under the receiver operating characteristic curve (AUC) for mortality discrimination for the IL-1R2 + sTREM-1 model (AUC 0.81, 95% CI 0.72-0.91) as well as for an IL-1R2-only model (AUC 0.78, 95% CI 0.68-0.88) were higher than for a model based on a modified Sequential Organ Failure Assessment (SOFA) score (AUC 0.69, 95% CI 0.56-0.81,
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ISSN: | 2296-858X 2296-858X |
DOI: | 10.3389/fmed.2023.1211265 |