ArticleThe Journal of antimicrobial chemotherapy2025
Classifying patients with invasive fungal disease: towards a unified case definition?
Article in The Journal of antimicrobial chemotherapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Invasive Aspergillosis Complicating Severe COVID-19: Cumulative Incidence, Risk Factors, and Outcomes Between the First Wave of the Pandemic and Subsequent Waves, According to the ECMM/ISHAM Classification and a Local Case Assessment.Journal of fungi (Basel, Switzerland) · 2026Article
- The future of fungal diagnostics: What's next in laboratory testing for invasive fungal infection?Medical mycology · 2026Review
- Comparative clinical features and outcomes of invasive pulmonary aspergillosis following influenzaFrontiers in medicine · 2026Article
- Attributable Mortality to Candidiasis in Non-Neutropenic Critically Ill Patients in the ICU and a Post-Mortem Study.Journal of fungi (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Management of invasive fungal disease (IFD) is increasingly challenging due to recognition of novel at-risk groups, emergence of new fungal pathogens and antifungal drug resistance. Together with the availability of new diagnostic tests and treatment modalities, robust and broadly applicable IFD case definitions are critical to support research. However, the ability to classify IFDs with the current definitions has decreased, prompting the development of new case definitions. Furthermore, current case definitions rely on a single positive test as mycological evidence, while not considering discordant evidence. We propose to explore the development of a machine learning (ML)-based IFD classification model, which uses algorithms to automatically 'learn' from observed data to consistently and accurately classify IFDs. Although developing and validating an ML-based IFD classification model is a significant undertaking, such an endeavour should be considered a worthwhile investment by the mycology community to standardize and reduce the ambiguity in the diagnosis of non-proven IFD.
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Registered trials
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