The Utility of a Thiopurine Monitoring Program to Detect Adverse Reactions in Inflammatory Bowel Disease Patients
Notice bibliographique
Résumé
Thiopurines, Azathioprine (AZA) and 6-mercaptopurine (6MP), are common therapies for inflammatory bowel disease (IBD) patients. These drugs, with complex metabolic pathways, have significant adverse reactions including leukopenia, hepatitis and infectious complications. Due to these reactions, it is recommended that routine labs be obtained during induction and maintenance thiopurine treatment. Unfortunately, poor adherence to these lab draw requirements may lead to a delay in identifying complications. Our IBD clinic implemented a thiopurine monitoring program (TMP) on July 1st 2011 in an attempt to increase lab compliance. The aim of this study was to compare lab draw compliance and complication rates in patients enrolled in thiopurine monitoring program (TMP+) vs. patients not enrolled (TMP−). All patients who received thiopurines from our pharmacy from July 1st 2011 to July 1st 2012 were identified (N = 192). All pediatric and non-IBD patients were excluded (N = 62). From the list of adult IBD patients on thiopurines, we constructed and compared two cohorts of patients (TMP+, TMP−). As part of the program, TMP+ patients were contacted by medical staff at time of recommended lab draws. Dedicated IBD staff interpreted labs and intervened upon abnormal results. Induction compliance was defined as at least 4 out of 5 expected lab draws (at 2, 4, 6, 8, 12 weeks) in the first 3 months of therapy. Maintenance compliance was defined as at least 3 out of 4 expected lab draws (every 3 months per year). Complications were defined as leukopenia, hepatitis, pancreatitis, GI intolerance, infection/fever, rash, and arthralgias. Patient demographics and clinical data were extracted using our center's electronic medical record. 130 patients were identified for analysis (63 patients TMP+ and 67 patients TMP−). There was no significant difference in demographics, type of IBD (UC, CD) or phenotype of IBD (Montreal classification) between the 2 groups. A significantly greater percentage of TMP+ patients were compliant with lab draws compared to the TMP− patients (79% vs. 49%, P = 0.000). Specifically, TMP+ patients were more compliant than TMP- patients with lab draws during induction (74% vs. 30%, P = 0.001) compared to maintenance monitoring (88% vs. 65%, P = 0.074). However, there was no difference in overall rates of adverse reactions between TMP+ and TMP− patients (43% vs. 33%, P = 0.197). Specifically, TMP+ and TMP− patients had similar rates of leukopenia (19% vs. 13%, P = 0.477) and hepatitis (19% vs. 21%, P = 0.829), but more non-laboratory related adverse reactions were noted in TMP+ patients compared to TMP- patients (11% vs. 2%, P = 0.029). Our study suggests that while a thiopurine monitoring program increased lab draw compliance, it did not significantly impact complication rates. This finding suggests either the recommended lab intervals are unnecessarily rigorous, or more likely that our study is underpowered to show a true difference between the groups. Interestingly, this program increased the detection of non-laboratory related adverse reactions, which may be attributed to increased contact with routine outreach to these patients. We hope to continue this program and hypothesize that with more patients enrolled, the true impact of this program will be seen.
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 | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».