Acute kidney injury in Staphylococcus aureus bacteraemia: a recurrent events analysis
Bibliographic record
Abstract
OBJECTIVES: To estimate risk factors for acute kidney injury (AKI) and the effect of AKI on mortality in Staphylococcus aureus bacteraemia, while taking into account recurrent AKI episodes, competing risks, time-varying variables, and time-varying effects. METHODS: We performed an unplanned analysis using data from a multicentre cohort study of patients with Staphylococcus aureus bacteraemia (SAB). The primary outcome was cumulative incidence of AKI, according to Kidney Disease Improving Global Outcomes definitions. RESULTS: We included 453 patients in this study of whom 194 (43%) patients experienced one or more AKI episodes. Age (hazard ratio (HR) 1.013, 95% CI 1.001-1.024), Charlson comorbidity index (HR 1.07, 95% CI 1.01-1.14), prior chronic kidney disease (HR 1.76, 95% CI 1.28-2.42), septic shock (HR 3.28, 95% CI 2.31-4.66), persistent bacteraemia (HR 1.53, 95% CI 1.08-2.17), and vancomycin therapy (HR 1.80, 95% CI 1.05-3.09) were independently associated with AKI, but flucloxacillin, cefazolin, rifampicin, and aminoglycoside therapy were not. After adjustment for confounders and immortal time bias, AKI was associated with an increased risk of 90-day mortality (HR 4.26, 95% CI 2.91-6.23). DISCUSSION: The incidence of AKI in SAB is high and a substantial proportion of patients develop recurrent episodes of AKI after recovery. AKI is specifically linked to the use of vancomycin and not to anti-staphylococcal penicillins. The clinical outcome of patients with SAB complicated by AKI is worse than previously estimated.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".