Distribution and prevalence of fungemia: a five-year retrospective multicentric survey in Venetian region, Italy
Bibliographic record
Abstract
Abstract Background Invasive fungal infections, significantly impact hospitalized and immunocompromised populations. Recent trends showed a shift from Candida albicans to non-albicans Candida (NAC) species, raising concerns about antifungal resistance. Objectives Our study focuses on the distribution of fungal species in blood cultures obtained from different healthcare settings, including hospitals, long-term care facilities, and community health centers in the Venetian region of Italy. Methods We retrospectively analyzed all consecutive blood culture isolates across 5 hospitals, 38 long-term care facilities, and 24 sample collection centers (blood exams and culture) from 2019 to 2023. Results Between 2019 and 2023, 11,552 microorganisms were isolated from blood cultures; 693 (6.0%) were fungi. The yearly prevalence ranged from 5.2% in 2019 to 6.1% in 2023. C. albicans isolates decreased significantly, from 60.0% in 2019 to 43.1% in 2023. NAC species showed significant growth, particularly C. parapsilosis sensu stricto (from 23.6% in 2019 to 28.8% in 2023), C. tropicalis (from 0.0% in 2019 to 7.2% in 2023), and N. glabratus (from 9.1% in 2019 to 11.8% in 2023). Medical wards consistently recorded the highest number of cases (429/693, 61.9%), with C. albicans predominating in earlier years. Resistance to amphotericin B rose sharply in C. parapsilosis ss. (22.5% in 2022), while fluconazole resistance in N. glabratus remained high (peaking at 85.7% in 2021). Conclusion The increasing dominance of NAC species and rising resistance trends underscore the necessity for enhanced diagnostics, infection prevention, and antifungal stewardship. Future research should incorporate clinical data to optimize fungemia management strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".