The Association Between Serum Vancomycin Level and Clinical Outcome in Patients With Peritoneal Dialysis Associated Peritonitis
Bibliographic record
Abstract
Introduction: Intraperitoneal (IP) vancomycin is often first-line empiric therapy and then maintenance therapy for peritoneal dialysis (PD) peritonitis. However, how vancomycin serum levels correlate with clinical outcomes remains unclear. Methods: We conducted a retrospective single-center adult cohort study of 98 patients with PD peritonitis treated with IP vancomycin between January 2016 and May 2022. The association between nadir vancomycin level and cure was evaluated in a logistic regression model, first unadjusted and then adjusted for age, sex, weight, glomerular filtration rate (GFR), and total number of days on PD. Vancomycin was assessed both as a continuous exposure (per 1 mg/l increase) and as a categorical exposure (<15 mg/l vs. ≥15 mg/l). A receiver operating characteristic curve (ROC) was created to explore nadir vancomycin level thresholds in an attempt to identify an optimal target level during treatment. Results: = 0.008). Weight, GFR, days on PD, sex, and age were not independently associated with outcome. The vancomycin level with the greatest discriminatory capacity for cure on the ROC analysis was 14.4 mg/l. Conclusion: Increasing IP vancomycin serum levels are associated with increased odds of cure; and maintaining vancomycin serum levels above 14-15 mg/l throughout the course of PD peritonitis treatment is likely to improve clinical outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".