ArticleMycopathologia2019
Prognostic Potential of the Panfungal Marker (1 → 3)-β-D-Glucan in Invasive Mycoses Patients.
Article in Mycopathologia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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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.
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Who cites it
5 citing papers in PubMed.
- Current diagnostic approach to fungal infection in the critically ill patient.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2025Review
- Exploring the Siderophore Portfolio for Mass Spectrometry-Based Diagnosis of Scedosporiosis and Lomentosporiosis.ACS omega · 2024Article
- Prognostic value of serial (1,3)-β-D-glucan measurements in ICU patients with invasive candidiasis.Critical care (London, England) · 2024Observational
- Duration of posaconazole therapy for Aspergillus fumigatus osteomyelitis dictated by serial monitoring of 1,3-beta-D glucan.Infection · 2020Article
- Biomarkers of fungal infection: Expert opinion on the current situation.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We analyze the prognostic potential of (1 → 3)-β-D-glucan (BG) levels in predicting clinical outcomes in patients with invasive fungal infections, on a population undergoing 253 episodes (177 with positive and 76 with negative outcome). Using linear regression analysis, we assessed the prognostic potential of kinetically evaluated BG levels and we found an overall sensitivity and specificity of 68 and 82%, respectively. Moreover, using an interpretative algorithm based on two distinct cutoff values, we were able to predict the outcome in 84% of the studied population with a diagnostic accuracy of 82%.
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Registered trials
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