Profile of Antimicrobial Use in the Pediatric Population of a University Hospital Centre, 2015/16 to 2018/19
Bibliographic record
Abstract
Background: Antimicrobial stewardship is a standard practice in health facilities to reduce both the misuse of antimicrobials and the risk of resistance. Objective: To determine the profile of antimicrobial use in the pediatric population of a university hospital centre from 2015/16 to 2018/19. Methods: In this retrospective, descriptive, cross-sectional study, the pharmacy information system was used to determine the number of days of therapy (DOTs) and the defined daily dose (DDD) per 1000 patient-days (PDs) for each antimicrobial and for specified care units in each year of the study period. For each measure, the ratio of 2018/19 to 2015/16 values was also calculated (and expressed as a proportion); where the value of this proportion was ≤ 0.8 or ≥ 1.2 (indicating a substantial change over the study period), an explanatory rating was assigned by consensus. Results: Over the study period, 94 antimicrobial agents were available at the study hospital: 70 antibiotics (including antiparasitics and antituberculosis drugs), 14 antivirals, and 10 antifungals. The total number of DOTs per 1000 PDs declined from 904 in 2015/16 to 867 in 2018/19. The 5 most commonly used antimicrobials over the years, expressed as minimum/maximum DOTs per 1000 PDs, were piperacillin-tazobactam (78/105), trimethoprim-sulfamethoxazole (74/84), ampicillin (51/69), vancomycin (53/68), and cefotaxime (55/58). In the same period, the care units with the most antimicrobial use (expressed as minimum/ maximum DOTs per 1000 PDs) were hematology-oncology (2529/2723), pediatrics (1006/1408), and pediatric intensive care (1328/1717). Conclusions: This study showed generally stable consumption of antimicrobials from 2015/16 to 2018/19 in a Canadian mother-and-child university hospital centre. Although consumption was also stable within drug groups (antibiotics, antivirals, and antifungals), there were important changes over time for some individual drugs. Several factors may explain these variations, including disruptions in supply, changes in practice, and changes in the prevalence of infections. Surveillance of antimicrobial use is an essential component of an antimicrobial stewardship program. RÉSUMÉ Contexte : La gestion des antimicrobiens est une pratique courante dans les centres hospitaliers afin de réduire l’utilisation inappropriée des antimicrobiens et le risque de résistance. Objectif : Décrire l’évolution de l’utilisation des antimicrobiens dans un centre hospitalier universitaire de 2015-16 à 2018-19. Méthodes : Dans cette étude rétrospective, descriptive et transversale, les dossiers pharmacologiques ont servi à déterminer le nombre de jours de traitement (NJT) et la dose définie journalière (DDD) par 1000 jours-présence (JP) pour chaque antimicrobien et pour chaque unité de soins par année de l’étude. Pour chaque mesure, on a également comparé le ratio de 2018-19 à celui de 2015-16, qui est exprimé en proportion; lorsque la valeur de cette proportion était ≤ 0,8 ou ≥ 1,2, ce qui indiquait un changement important durant la période de l’étude, une note explicative a été attribuée par consensus. Résultats : Durant la période à l’étude, 94 antimicrobiens ont été disponibles dans notre centre : 70 antibiotiques (dont les antiparasitaires et les antituberculeux), 14 antiviraux et 10 antifongiques. Le nombre total de NJT par 1000 JP a diminué de 904 en 2015-16 à 867 en 2018-19. Les cinq antimicrobiens utilisés le plus fréquemment et présentés en minimum / maximum de NJT par 1000 JP étaient les suivants : piperacilline-tazobactam (78/105), trimethoprim-sulfamethoxazole (74/84), ampicilline (51/69), vancomycine (53/68) et cefotaxime (55/58). Pendant la même période, les unités de soins qui faisaient la plus grande utilisation d’antimirobiens (exprimée en minimum / maximum de NJT par 1000 JP) étaient hématologie-oncologie (2529/2723), pédiatrie (1006/1408) et soins intensifs pédiatriques (1328/1717). Conclusions : Cette étude démontre une consommation stable d’antimicrobiens entre 2015-16 et 2018-19 dans un centre hospitalier universitaire mère-enfant canadien. Malgré le fait que la consommation entre les groupes d’antimicrobiens (antibiotiques, antiviraux, antifongiques) était stable, on a constaté d’importantes variations concernant certains médicaments individuels. Plusieurs facteurs peuvent expliquer cette variation, notamment des ruptures d’approvisionnement, des changements de pratique et des changements dans la prévalence d’infections. La surveillance de la consommation des antimicrobiens est une partie essentielle de tout programme d’antibiogouvernance.
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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) | 0.000 | 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".