P45 Translating AWaRe-based antibiotic use quality indicators into practice: an automated framework and dashboard leveraging Global-PPS data
Notice bibliographique
Résumé
Abstract Background The WHO AWaRe Antibiotic Book provides concise, evidence-based guidance for antibiotic choice, dose and duration across more than 30 clinical infections in primary care and hospital settings. Building on this guidance, an international consensus process refined a set of 118 AWaRe-based quality indicators (QIs) to assess prescribing quality in these settings. While Global-PPS has reported real-time antimicrobial prescribing practices using the AWaRe classification since 2019, linking the AWaRe Book guidance to Global-PPS data has not yet been explored. Objectives To develop an analytics framework and R Shiny dashboard that computes AWaRe QIs based on condition-specific Global-PPS prescribing data according to the AWaRe Book recommendations in inpatient and outpatient settings. Methods AWaRe QIs measurable within Global-PPS, using diagnostic/indication codes for inpatients and symptom fields as proxy diagnoses for outpatients, were retained. These cover empirical treatment of common community-acquired infections and surgical prophylaxis; in this analysis, application was initially limited to adult prescriptions. Each QI measures specific prescribing dimensions: whether patients received oral (outpatient) or IV (inpatient) antibiotics; whether the agent was Access or Watch; and concordance with AWaRe recommended drug choice, dose and duration. For each included condition, AWaRe Book treatment recommendations were compiled into a structured reference table and mapped to Global-PPS variables capturing the corresponding fields (diagnosis/symptom codes, antibiotic, route, dose and frequency, prescribed duration (outpatient) and patient demographics). Algorithms written in R (RStudio) deterministically link structured Global-PPS Excel exports to the reference table, apply QI definitions and eligibility criteria and calculate numerators and denominators. Outputs are aggregated by ward/specialty and facility levels. The analytic pipeline was implemented and validated in R Markdown before being embedded in an R Shiny dashboard giving condition-specific tables and visualizations. Results Thirty-eight AWaRe QIs were compatible with Global-PPS: 17 for inpatient care (covering sepsis, meningitis, pneumonia, intra-abdominal infections, upper urinary tract infections, complicated skin/soft tissue and osteoarticular infections and surgical prophylaxis) and 21 for outpatient care (respiratory tract, ear/nose/throat, dental, diarrhoeal, lymph-node, mild skin/soft tissue and lower urinary tract infections). Reports include case eligibility checks, hospital antimicrobial use profiles and QI-specific evaluations. The R Markdown prototype was piloted with retrospective Global-PPS datasets from inpatient and outpatient facilities in Africa and Asia. Discussions with Fleming Fund country partners highlighted the value of reusing existing PPS data to measure QIs and of condition-specific outputs that reveal deviations from WHO recommendations as potential stewardship targets. The dashboard processes de-identified Global-PPS exports uploaded by sites for temporary, session-based analysis; neither datasets nor outputs are stored or shared externally. Guidance will be provided for hospitals using other PPS methodologies (e.g. WHO-PPS) to adapt their data structure for use with the dashboard. Conclusions Based on the AWaRe Antibiotic Book, this new framework generated automated, condition-specific quality assessments of antibiotic prescribing in both inpatient and outpatient settings. It provides guidance for stewardship priorities and enables tracking progress. Future work will extend to paediatrics and deepen integration with the Global-PPS system to strengthen antimicrobial stewardship globally.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».