Risk factors for mortality among patients with Staphylococcus aureus bacteremia: a single-centre retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Staphylococcus aureus bacteremia is associated with significant morbidity and mortality. Given the paucity of recent Canadian data, we estimated the mortality rate associated with S. aureus bacteremia in a tertiary care hospital and identified risk factors associated with mortality. METHODS: We retrospectively reviewed the records of adults with S. aureus bacteremia admitted to a tertiary care centre in southwestern Ontario between 2008 and 2012. Cox regression analysis was used to evaluate associations between predictor variables and all-cause, in-hospital, and 90-day postdischarge mortality. RESULTS: Of the 925 patients involved in the study, 196 (21.2%) died in hospital and 62 (6.7%) died within 90 days after discharge. Risk factors associated with in-hospital and all-cause mortality included age, sepsis (adjusted hazard ratio [adjusted HR] 1.49, 95% confidence interval [CI] 1.08-2.06, p = 0.02), admission to the intensive care unit (adjusted HR 3.78, 95% CI 2.85-5.02, p < 0.0001), hepatic failure (adjusted HR 3.36, 95% CI 1.91-5.90, p < 0.0001) and metastatic cancer (adjusted HR 2.58, 95% CI 1.77-3.75, p < 0.0001). Methicillin resistance, hepatic failure, cerebrovascular disease, chronic obstructive pulmonary disease and metastatic cancer were associated with postdischarge mortality. INTERPRETATION: The all-cause mortality rate in our cohort was 27.9%. Identification of predictors of mortality may guide empiric therapy and provide prognostic clarity for patients with S. aureus bacteremia.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".