Candida colonization as a predictor of invasive candidiasis in non-neutropenic ICU patients with sepsis: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Candida colonization is a risk factor for the development of invasive candidiasis. This study sought to estimate the magnitude of this association, and determine if this information can be used to guide empirical antifungal therapy initiation in critically ill septic patients. METHODS: PubMed/MEDLINE and Embase were systematically reviewed for all published studies evaluating predictors of invasive candidiasis in ICU patients with sepsis. Meta-analysis was used to determine the pooled odds ratio for invasive candidiasis among colonized versus non-colonized patients. Sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV), and positive and negative likelihood ratios (+LR, -LR) were then calculated by considering the presence/absence of Candida colonization as the diagnostic test, and the presence/absence of invasive candidiasis as the disease of interest. RESULTS: Out of 9825 patients in the 10 eligible studies, 3886 (40%) were colonized with Candida and 462 patients (4.7%) developed invasive candidiasis. Meta-analysis indicated that critically ill patients with sepsis who are colonized with candida are more likely to develop invasive candidiasis (odds ratio 3.32; 95% CI 1.68-6.58) compared with non-colonized patients. The pooled SN was 75.2% (95% CI 59.6-86.2%), while the pooled SP was 49.2% (95% CI 33.2-65.3%).The NPV of Candida colonization was high (96.9%; 95% CI 92.0-98.9%), but the PPV was low (9.1%; 95% CI 5.5-14.6%). CONCLUSION: Candida colonization is strongly associated with the likelihood of invasive candidiasis among ICU patients with sepsis. Available data argue against initiating empirical antifungal treatment in non-neutropenic septic patients without prior documented Candida colonization.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".