Physician antimicrobial prescribing and patient outcomes on general medical wards: a multicentre retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Variability in antimicrobial prescribing may indicate an opportunity for improvement in antimicrobial use. We sought to measure physician-level antimicrobial prescribing in adult general medical wards, assess the contribution of patient-level factors to antimicrobial prescribing and evaluate the association between antimicrobial prescribing and clinical outcomes. METHODS: Using the General Medicine Inpatient Initiative (GEMINI) database, we conducted a retrospective cohort study of physician-level volume and spectrum of antimicrobial prescribing in adult general medical wards in 4 academic teaching hospitals in Toronto, Ontario, between April 2010 and December 2019. We stratified physicians into quartiles by hospital site based on volume of antimicrobial prescribing (days of therapy per 100 patient-days and antimicrobial-free days) and antibacterial spectrum (modified spectrum score). The modified spectrum score assigns a value to each antibacterial agent based on the breadth of coverage. We assessed patient-level differences among physician quartiles using age, sex, Laboratory-based Acute Physiology Score, discharge diagnosis and Charlson Comorbidity Index. We evaluated the association of clinical outcomes (in-hospital 30-day mortality, length of stay, intensive care unit [ICU] transfer and hospital readmission) with antimicrobial volume and spectrum using multilevel modelling. RESULTS: The cohort consisted of 124 physicians responsible for 124 158 hospital admissions. The median physician-level volume of antimicrobial prescribing was 56.1 (interquartile range 51.7-67.5) days of therapy per 100 patient-days. We did not find any differences in baseline patient characteristics by physician prescribing quartile. The difference in mean prescribing between quartile 4 and quartile 1 was 15.8 days of therapy per 100 patient-days (95% confidence interval [CI] 9.6-22.0), representing 30% higher antimicrobial prescribing in the fourth quartile than the first quartile. Patient in-hospital deaths, length of stay, ICU transfer and hospital readmission did not differ by physician quartile. In-hospital mortality was higher among patients cared for by prescribers with higher modified spectrum scores (odds ratio 1.13, 95% CI 1.04-1.24). INTERPRETATION: We found that physician-level variability in antimicrobial prescribing was not associated with differences in patient characteristics or outcomes in academic general medicine wards. These findings provide support for considering the lowest quartile of physician antimicrobial prescribing within each hospital as a target for antimicrobial stewardship.
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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 | 0.003 |
| 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".