O-015 YI The Impact of Clinic Visits and Disease Activity on Adherence Rates in Children With Inflammatory Bowel Diseases
Notice bibliographique
Résumé
Medication non-adherence is a challenging issue in pediatric patients with inflammatory bowel diseases. Poor adherence often results in disease flare-ups, disease complications, therapy escalation and need for corticosteroids. Patients who have more frequent follow up appointments have greater medication adherence. By understanding why patients are non-adherent, we can educate patients and overcome these barriers. A retrospective chart review of patients attending the pediatric IBD clinic at the Stollery Children’s Hospital from January 2012 to December 2013 was completed. Disease activity scores were based on PCDAI for Crohn’s Disease (CD) and PUCAI for Ulcerative Colitis (UC). Medication adherence was calculated by the actual number of prescription days filled, compared to days prescribed, as provided by PIN provincial pharmacy database. The number of blood tests performed compared to the clinic’s protocol determined blood work compliance. Association was established between disease activity score at each clinical visit, percentage of prescriptions filled, blood work completed, rural/urban residence and whether patients were in steroid-free remission for the preceding 6 months at the time of data collection. One hundred and thirteen patients were reviewed with 71 patients diagnosed with CD and 42 (include 1 IBDU) were UC. 44% were female. The mean age of diagnosis for CD was 10.7 years, with a mean PCDAI score of 21 and the age for UC was 8.75 years, with a mean PCDAI score of 31. UC patients were diagnosed at a younger age (P = 0.011). Anti-TNF adherence, defined as administration at or before schedule date, was 85%. Immunomodulator adherence rate was 77%. Methotrexate adherence was better than Azathioprine in CD (86.73% versus 74.86%, respectively; P = 0.0523). 5ASA adherence was 74%. No statistical difference was found between a patient’s age group and medication adherence. Frequency of clinic visits showed an association with immunomodulator adherence in CD (P = 0.0150). UC patients with more severity at diagnosis had greater adherence to their medications (P = 0.005), specifically 5ASA (P = 0.0016). Urban patients with UC, when compared to rural patients, were also more likely to adhere to their 5ASA (P = 0.0776). Adherent UC patients, taking > 80% of their immunomodulators or 5ASA were more likely to be in steroid free remission in the last 6 months prior to the chart review (P = 0.015). Overall blood work adherence was 63%. Clinic visit frequency appears to significantly impact a patient’s adherence to immunomodulators in CD. Whether an optimal frequency of clinic visits exists that influence medication and blood work adherence still needs to be established with a larger cohort. Rural UC patients are more likely to be non-adherent to 5ASA. Adherent UC patients are more likely to be in sustained steroid-free remission. More support is needed for the UC rural patients to improve adherence. Qualitative research is needed to discover factors that truly motivate patients’ adherence to medications and blood work.
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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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».