#566 Global home medication practices in dialysis patients – applying NDC-to-ATC mapping
Notice bibliographique
Résumé
Abstract Background and Aims Comparing global medication prescribing practices is a major challenge in international healthcare research due to differing drug coding systems, such as the Anatomical Therapeutic Chemical (ATC) and National Drug Code (NDC). As global patterns of medication use in dialysis remain undefined, this study applies a publicly available NDC-to-ATC mapping algorithm to a global dialysis dataset, ApolloDialDbTM, covering 40 countries. This approach enables the inclusion of US data in global analyses. To offer a real-world perspective, we assessed the most commonly prescribed medications administered at home by dialysis patients across regions. Method Apollo DialDb includes anonymized data from over 540,000 adult dialysis patients in a global kidney network (Jan 2018–Mar 2021, Fresenius Medical Care, Bad Homburg, DE). Data anonymization was performed in alignment with recommendations from a re-identification risk determination (Privacy Analytics, Ontario, CA). Home medications were analyzed based on the top (most common) five pharmacological classes defined by the first 4-digits of ATC code and stratified by region. A publicly available NDC-to-ATC mapping algorithm, based on FDA logic, queried the RxNorm API to assign ATC classes to NDCs. Results In the US dataset, 41% of NDCs had no ATC match, 36% matched to a single ATC, and 23% matched to multiple ATC codes. Analyses included only NDCs with a single ATC match (5,781,841). A total of 7,377,688 ATC code classes for home prescriptions were analyzed: USA (78%), EMEA (Europe, Middle East, Africa, 14%), LA (Latin America, 6%), and AP (Asia Pacific, 2%). Each ATC code entry represents a documented prescription of varying duration. Across all regions, the top ATC classes of home medications were ‘All other therapeutic products’ (11.52%), ‘Beta blocking agents’ (7.11%), ‘Lipid modifying agents, plain’ (5.21%), ‘Insulins and analogues’ (4.90%), and ‘Peptic ulcer and gastro-oesophageal reflux disease’ (4.18%). In the US, where 78% of ATC entries originated, the first four ATC classes were identical, but ‘Other analgesics and antipyretics’ (4.47%) replaced the fifth and was just included in the US top 5 list. ‘Angiotensin II receptor blockers (ARBs), plain’ (6.34%) as well as ‘Vitamin B12 and folic acid’ (5.07%) were just listed in LA. In AP, phosphate binders, antithrombotics, and antihypertensives were among the top 5. In EMEA, these medications were also in the top 5, with the addition of vitamin A&D (Fig. 1). Conclusion A publicly available NDC-to-ATC mapping algorithm enabled integration of US NDC-coded data into global analyses of medication patterns in dialysis based on ATC classes using ApolloDialDb. Approximately 60% of NDCs were mapped to at least one ATC class. Medication use in dialysis varies regionally: phosphate binders and antihypertensives are top home prescriptions globally, while lipid-modifying agents, antidiabetics, and analgesics and antipyretics dominate in the US. In EMEA, vitamin A&D and antithrombotics are more prevalent. These insights provide benchmarks for the community, highlighting the need for further research on treatment duration and real-world use of emerging drugs (e.g., GLP-1 drugs, HIF-PH inhibitors).
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».