Non-Adherence to Imatinib in Chronic Myeloid Leukemia Patients Is Associated with a Short Term and Long Term Negative Impact On Healthcare Utilization and Costs.
Notice bibliographique
Résumé
Abstract Abstract 4270 Introduction This study compared the healthcare resource utilization and costs associated with long-term imatinib treatment adherence versus non-adherence in patients with chronic myelogenous leukemia (CML). Methods Two large administrative claims databases were combined (MarketScan and Ingenix Impact, 01/2002-07/2008) to identify patients diagnosed with CML (ICD-9 code 205.1x). Patients with ≥2 imatinib prescriptions and continuous enrollment ≥6 months prior to and ≥1month following the first observed imatinib prescription filled (i.e., the index date) were selected. Patients were followed for up to 3 years from the index date to the earliest of the termination of healthcare plan enrollment, end of data availability, imatinib treatment discontinuation for ≥90 consecutive days, switch to another drug (i.e., dasatinib or nilotinib), or a CML remission diagnosis (ICD-9 code 205.11). A longitudinal retrospective open-cohort design was used to measure patients' adherence to imatinib repeatedly over time. Imatinib treatment periods were divided into 90-day intervals. Using the medication possession ratio (MPR), treatment intervals were categorized as adherent (MPR≥85%) or non-adherent (MPR<85%). Patients' healthcare utilization and costs were compared between adherent and non-adherent intervals. Multivariate regression models were used to compare rates of inpatient admissions, outpatient visits, emergency room visits, and total urgent care visits. Regression models controlled for age, gender, CML complexity, treatment duration, prior chemotherapies, prior adverse events, Charlson comorbidity index, and prior resource utilization. Additional regression models including past cumulative MPR were used to assess the long term impact of non-adherence. Results For the 1,877 CML patients who met the selection criteria, there were 6,175 adherent and 3,163 non-adherent intervals. Only 34% of patients were completely adherent throughout their observation period. During non-adherent intervals, patients incurred significantly more frequent total urgent care visits (IRR=1.82, p<.001), including inpatient visits (IRR=2.76, p<.001) and emergency room visits (IRR=1.25, p=.021), and more frequent outpatient visits (IRR=1.09 p=.001) compared to adherent intervals. Though non-adherence was associated with lower pharmacy cost by $3,053 (p<.001) over 90 days, this difference was outweighed by a $4,531 higher medical cost (p<.001), resulting in a net cost increase of $1,477 (p<.001) over adherent intervals. Patients who were adherent throughout their observation period incurred an average cost of $11,759 per quarter, compared to $13,773 for patients who were not always adherent. When extrapolated to the 3-year study, health care costs were $24,168 less per patient for patients who were adherent at each of the studied quarters. In models where both the current adherence status and the long-term cumulative impact of past adherence was taken into account, for patients who had always been adherent (past cumulative MPR≥85%), total cost was $883 (p=.084) higher in a non-adherent interval (current MPR<85%) compared to an adherent interval (current MPR≥85%). In patients who had not always been adherent (past cumulative MPR<85%) an adherent interval cost (current MPR≥85%) $1,239 (p=.002) more, while another non-adherent interval (current MPR<85%) cost $2,122 (p<.001) more compared to an adherent interval in patients who had always been adherent (both current and past cumulative MPR≥85%). Conclusions Our analysis indicates that imatinib non-adherence is associated with significant negative economic consequences, while continuous adherence to imatinib in CML patients was associated with lower healthcare resource utilization and costs. Disclosures: Wu: Novartis: Consultancy, I am working for Analysis Group Inc and Analysis Group Inc received funds from Novartis to conduct the analysis. Bollu:Novartis Oncology: Employment. Guo:Novartis Pharmaceuticals Corporation: Employment. Guerin:Novartis: Consultancy, I am working for Analysis Group Inc and Analysis Group Inc received funds from Novartis to conduct the analysis. Yu:Novartis: Consultancy, I am working for Analysis Group Inc and Analysis Group Inc received funds from Novartis to conduct the analysis. Sirulnik:Novartis Pharmaceutical Corporation: Employment. Griffin:Novartis Pharmaceutical Corporation: Consultancy, I have.
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 | 0,004 |
| 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 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».