Epidemiology of candidemia at a tertiary Canadian hospital, 2004–2013
Bibliographic record
Abstract
Introduction: Candidemia carries significant morbidity and mortality for hospitalized patients. Local epidemiology is needed to develop strategies to reduce infections. This article describes the epidemiology of candidemia at a tertiary-care hospital in Edmonton, Canada. Methods: Between 2004 and 2013, 250 episodes of candidemia were identified using an infection control database. Binary logistic regression analysis was used to identify risk factors for non-albicans isolates and for mortality. Results: The candidemia rate increased significantly, from 0.387/10,000 patient days (PD) in 2004 to 1.45/10,000 PD in 2013 (p=0.0061). The 30-day and overall in-hospital mortality rates were 38% and 47%, respectively. Candida albicans and C. glabrata represented 80% of isolates. Overall, 48% of episodes were attributable to central venous catheter (CVC) infections. The ratio of C. albicans to non-albicans isolates and the rate of CVC infections did not change significantly over the study period (p=0.98 and 0.14, respectively). Preceding azole therapy within 30 days of candidemia conferred an increased risk of a non-albicans isolate (odds ratio [OR] 2.59, 95% CI 1.24 to 5.43). Mortality was increased with immunosuppression (OR 3.30, 95% CI 1.74 to 6.24) and age, with an OR of 2.78 (95% CI 1.21 to 6.38) for the 61- to 68-year-old age group and an OR of 4.30 (95% CI 1.84 to 10.04) for the >69-year-old age group. Mortality was similar among C. albicans and non-albicans infections (OR 0.85, 95% CI 0.49 to 1.45). Conclusion: Candidemia at the authors' institution increased during the study period without a significant change in the ratio of C. albicans to other Candida spp. The risk of non-albicans candidemia was affected by previous azole therapy, and the risk of mortality was higher with increased age and immunosuppression. The etiology of the increased candidemia rate is likely complex, and strategies to address this, as well as the high mortality rate seen with candidemia, are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".