Evaluating the safety and effectiveness of a nurse-led outpatient virtual IV vancomycin monitoring clinic: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Outpatient parenteral antimicrobial therapy (OPAT) with vancomycin is a common treatment modality for certain Gram-positive infections. Data regarding the safety of various models of delivery are limited. OBJECTIVES: To review outcomes of a nurse-led OPAT vancomycin monitoring service. METHODS: This was a retrospective cohort study of consecutive patients referred to a nurse-led OPAT vancomycin clinic from December 2015 to March 2018. Patients were administered IV vancomycin in the home with active laboratory monitoring of vancomycin trough levels, renal function and complete blood count using an integrated electronic database linked with community laboratories (virtual vancomycin clinic, VVC). Monitoring was coordinated by nurses with physician approval of recommended dosing changes. Data were extracted from the electronic medical record. Demographics; clinical indication; microbial aetiology; culture source; antimicrobial regimen(s); serum creatinine and vancomycin trough values; initiation, discharge and completion dates; hospitalizations; adverse events; and outcomes were all evaluated. RESULTS: Two hundred and seventy-five patients underwent a total of 301 courses of OPAT with vancomycin; 285 courses were completed. The rate of treatment discontinuation due to adverse effects was 33/301 (11.0%), with 15/33 (45.5%) being due to renal adverse effects (15/301 [5.0%] of episodes). Two of 15 (18.2%) patients developed stage 2 acute kidney injury (AKI), and no patients had stage 3 AKI or required haemodialysis. Nine of 301 (3.0%) required readmission for treatment failure. Nursing costs associated with monitoring were $63.93 CAD/patient ($48.43 USD). CONCLUSIONS: A nurse-led VVC was a safe, effective and inexpensive modality for administering outpatient vancomycin.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.004 | 0.017 |
| 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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".