PRESCRIBING OF MEDICATIONS WITH PHARMACO-GENETIC GUIDANCE IN CHILDREN AND ADOLESCENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS (SLE)
Notice bibliographique
Résumé
PV163 / #410 Poster Topic: AS18 - Pediatric SLE Background/Purpose Pharmacogenetics (PGx) focuses on the effect of genetic variations on metabolism and response to medications. At this moment, over 100 medications have clinical practice guidelines that provide recommendations on how genetic variations can be incorporated into clinical decision making to improve efficacy and prevent adverse drug reactions. This study aims to determine how often medications with PGx guidelines are prescribed within a cohort of children and adolescents diagnosed and followed for systemic lupus erythematosus (SLE) at the Hospital for Sick Children Lupus Clinic. Methods We completed a retrospective cohort study of patients diagnosed and followed for SLE at SickKids between January 1997 and December 2017. All patients met ACR, SLICC, or EULAR/ACR SLE classification criteria and had clinical and medication data in the dedicated lupus database. We created a list of medications with clinical PGx guidelines (eg, Clinical Pharmacogenetics Implementation Consortium [CPIC] and Dutch Pharmacogenetic Working Group [DPWG]) or clinical PGx recommendations in FDA-approved drug labels using the Pharmacogenomics Knowledgebase ( www.pharmgkb.org ). We used descriptive statistics to calculate the number of prescriptions of medications with PGx guidance, the proportion of the total prescriptions to PGx prescriptions, and the time to the first and second instance of PGx prescribing following SLE diagnosis using Kaplan-Meier analysis. Characteristics of the cohort such as age, sex, ethnicity, and SLE manifestations were also extracted and compared between people prescribed and not prescribed PGx medications (P < 0.05). Results Our cohort included 616 children and adolescents with SLE. The cohort was 80% female, with a median age at diagnosis of 14 years (IQR 11-15), with the largest reported ethnic group being Europeans (38%). The most common SLE manifestations were malar rash (79%), lymphopenia (68%), and the presence of anti-dsDNA antibodies (66%). Patients prescribed a PGx medication within the first year after diagnosis showed a statistically increased prevalence of neuropsychiatric, renal, and musculoskeletal manifestations, and hypocomplementemia (Table 1). Patients were prescribed 219 distinct medications, including 43 with PGx guidance. The most commonly prescribed PGX medications were azathioprine (42%), omeprazole (33%), and lansoprazole (15%). Of the 616 patients, 413 (67%) were prescribed at least 1 PGx medication, with most individuals receiving prescriptions within the first year of SLE diagnosis (n=290, 47%). Table 1: Cohort Demographic and Feature Characteristics (a) Sex, SLE features, and ethnicity were analyzed with a Fisher’s exact test, age was analyzed with Wilcoxon rank-sum test. (*) Denotes statistical difference between the groups (p < 0.05) Conclusions In a large cohort study of children and adolescents with SLE, we observed that 68% of patients were prescribed a medication with PGx guidance during the course of their disease, with nearly half of these patients being prescribed within the first year of SLE diagnosis. Future plans include expanding the cohort and follow-up by 5 years and completing analyses of genetic variants for the most commonly prescribed PGx medications using Stargazer, a star allele calling software. Our research aims to provide a rationale for the use of PGx in individualized care for children and adolescents with SLE.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».