Health Care Resource Utilization and Costs in Patients with Chronic Myeloid Leukemia with Better Adherence to Tyrosine Kinase Inhibitors and Increased Molecular Monitoring Frequency
Notice bibliographique
Résumé
BACKGROUND: Frequent molecular monitoring (qPCR tests), as recommended by evidence-based monitoring guidelines, is associated with higher adherence to tyrosine kinase inhibitors (TKIs) in the management of chronic myeloid leukemia (CML); both factors have been associated with better clinical and economic outcomes. OBJECTIVES: To (a) estimate the effect of more frequent qPCR tests on health care resource utilization (HRU) and associated costs, including direct (effect of qPCR test frequency on HRU) and indirect (through TKI adherence) effects, and (b) develop an economic model applicable to multiple clinical practice scenarios. METHODS: Adult patients newly diagnosed with CML who started TKI firstline therapy were identified from U.S. administrative claims data (2010-2015). TKI adherence (medication possession ratio [MPR]), number of inpatient days, emergency room (ER) visits, outpatient service days, and mean costs per HRU event were measured during the first year of CML treatment. Direct and indirect effects of qPCR test frequency were estimated using multivariate regression models. Subsequently, an economic model was developed to assess the overall effect of varying qPCR test frequency on HRU and associated costs during the first year of CML treatment under different clinical practice scenarios; the scenario reported is the increase from 1 to 2 qPCR tests. RESULTS: Of the 1,431 patients included, 36% had no qPCR tests, the average qPCR test frequency was 1.6, and the average MPR was 0.86 during the first year of CML treatment. The direct effect of increasing qPCR test frequency by 1 was associated with 13.0% fewer inpatient days (adjusted incidence rate ratio [adjusted IRR] = 0.87; P = 0.010); 8.3% fewer ER visits (adjusted IRR = 0.92; P = 0.043); and 3.0% more outpatient service days (adjusted IRR = 1.03; P = 0.002). Each increase of 1 test was associated with an increase in TKI adherence by 2.2 percentage points (adjusted MPR difference = 0.022; P < 0.001). When considering the indirect effect of qPCR test frequency through TKI adherence, an increase of 1 qPCR test combined with an increase in TKI adherence by 2.2 percentage points was associated with a greater reduction of inpatient days from 13.0% to 15.2%, ER visits from 8.3% to 8.6%, and a smaller increase of outpatient service days from 3.0% to 2.6%. Based on the economic model, an increase from 1 to 2 qPCR tests, considering the increase in TKI adherence, was associated with a reduction of 0.87 (95% CI = -1.49, -0.18) inpatient days and 0.06 (95% CI = -0.12, 0.05) ER visits, an increase of 0.98 (95% CI = 0.25, 1.60) outpatient service days and a cost savings of $2,918 (95% CI = -5,213, -349) per patient per year. CONCLUSIONS: Closer alignment with the monitoring guidelines' recommended qPCR test frequency and better adherence to TKIs were associated with lower HRU and medical service costs. Managed care initiatives to increase qPCR test frequency and TKI adherence might benefit from an enhanced reduction because of the interaction between both factors. DISCLOSURES: This study was funded by Novartis Pharmaceuticals, which was involved in all stages of the study and in the decision to submit the report for publication. Latremouille-Viau, Guerin, Gagnon-Sanschagrin, and Dea are employees of Analysis Group, which received consulting fees from Novartis Pharmaceuticals for work on this study. Joseph is an employee of Novartis Pharmaceuticals and owns stock in Amgen and Pfizer. Cohen was an employee of Novartis Pharmaceuticals at the time of this study. Portions of this study were presented online (beginning May 20, 2016) as part of the American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago, Illinois, on June 3-7, 2016, and as a poster at the American Society of Hematology (ASH) Annual Meeting in San Diego, California, on December 3-6, 2016. Study concept and design were contributed by Latremouille-Viau and Guerin, along with the other authors. Gagnon-Sanschagrin and Dea took the lead in data collection, assisted by the other authors, and data interpretation was performed by Cohen and Joseph, along with the other authors. The manuscript was written by Latremouille-Viau, along with the other authors, and revised by Joseph, along with the other authors.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».