Risk of Bloodstream Infection in Patients With Chronic Kidney Disease Not Treated With Dialysis
Bibliographic record
Abstract
BACKGROUND: Patients with end-stage renal disease requiring dialysis are at high risk for bloodstream infection and infection-related death. Whether patients with chronic kidney disease who are not receiving dialysis are also at increased risk of bloodstream infection is less clear. METHODS: We examined the association between chronic kidney disease not being treated with dialysis and bloodstream infection in a cohort of patients 66 years or older. All patients required at least 1 outpatient serum creatinine measurement enabling estimation of glomerular filtration rate (eGFR) using the Modification of Diet in Renal Disease Study equation. Cox proportional hazards models with censoring at the initiation of renal replacement therapy or death were used to determine associations between eGFR, bloodstream infection, and death within 30 days of community-onset bloodstream infection, adjusting for potential confounders. RESULTS: In 25 675 patients followed up for a median of 3.2 years, 797 developed at least 1 bloodstream infection, of which most (75%) were community-onset infections. Compared with patients with an eGFR of 60 mL/min/1.73 m(2) or higher, adjusted hazard ratios (95% confidence intervals) for bloodstream infection according to eGFR were, respectively, 1.24 (1.01-1.52), 1.59 (1.24-2.04), and 3.54 (2.69-4.69) in those with an eGFR of 45 to 59, 30 to 44, and less than 30 mL/min/1.73 m(2). The associations were consistent for both community-onset and nosocomial infections. Compared with patients with an eGFR of 60 mL/min/1.73 m(2) or higher, the risk of death within 30 days of community-onset bloodstream infection was significantly greater in those with an eGFR less than 30 mL/min/1.73 m(2) (hazard ratio, 4.10; 95% confidence interval, 2.06-8.14). CONCLUSION: Older adults with chronic kidney disease not being treated with dialysis are at increased risk of bloodstream infection and of death following community-onset bloodstream infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".