Incremental Healthcare Resource Utilization and Costs Associated with Non-Optimal Treatment in Patients with Chronic Myeloid Leukemia in Chronic Phase (CML-CP) Treated with Tyrosine Kinase Inhibitors (TKI) in Early Lines of Therapy in the United States (US)
Notice bibliographique
Résumé
Introduction: TKIs are the standard of care for CML-CP. Although effective TKI options are available in early lines of therapy of CML, patients may experience intolerance or resistance to TKI leading to non-optimal treatment (NOPT). This retrospective cohort study assessed the incremental healthcare resource utilization (HRU) and medical costs associated with NOPT in patients with CML treated in first-line (1L) or second-line (2L) TKI therapy in the US. Methods: Adult patients with CML who received 1L TKI therapy with imatinib, dasatinib, nilotinib, or bosutinib (1L cohort), and those who received 2L TKI therapy with imatinib, dasatinib, nilotinib, bosutinib, or ponatinib (2L cohort), in 2012 or later, were selected from the OptumInsight Clinformatics database (01/2007-06/2022). Patients were required to have ≥2 years of continuous health plan coverage post-1L/2L initiation (1L/2L index date). Patients were classified in NOPT subgroup if they met one of the following criteria: 1) treatment discontinuation/switch within the first 6 months post-index, or 2) temporary treatment interruption and/or dose reduction within the first 6 months post-index followed by treatment discontinuation within the first 12 months post-index, or 3) low treatment adherence (proportion of days covered [PDC] ≤50%). Patients were classified in the reference (REF) subgroup if they met one of the following criteria: 1) high treatment adherence (PDC >90%) and no treatment discontinuation, 2) high treatment adherence (PDC >90%) and treatment discontinuation occurring more than 12 months post-index. All-cause HRU (inpatient [IP] admissions, outpatient [OP] visits, and emergency department [ED] visits) and associated medical costs (2022 USD) were measured during the 2 years post-index and reported per-patient-per-year (PPPY). Incremental HRU and costs of NOPT (vs. REF) were estimated using multivariable Poisson regressions and two-part models (adjusted for age, gender, race, region, health plan type, time from CML diagnosis to index date, and Darkow Disease Complexity Index), respectively. Adjusted incidence rate ratios (IRR) and mean cost differences (Δ) were reported along with p-value, estimated using bootstrap technique. All analyses were conducted separately in the 1L and 2L cohorts. Results: Of 2,043 patients that initiated 1L TKI therapy in 2012 or later, 197 patients were included in NOPT subgroup (median age: 69 years; 52.3% female; 68.5% White) and 284 in REF subgroup (median age: 62 years; 40.1% female; 74.6% White). Patients in NOPT subgroup had 80% more IP admissions (adjusted IRR=1.8; p <0.001) with twice more IP days (adjusted IRR=2.0; p=0.024), 30% more OP visits (adjusted IRR=1.3; p=<0.001) in the first 2 years post-index, as compared to REF subgroup. Consistently, patients in NOPT subgroup had higher medical costs (adjusted Δ=$13,551 PPPY; p=0.012) mainly driven by higher IP costs (adjusted Δ =$5,989 PPPY; p=0.028) along with numerically higher OP costs (adjusted Δ=$5,483 PPPY; p=0.060) in the first 2 years post-index, as compared to REF subgroup ( Figure 1). Of 586 patients that initiated 2L TKI therapy in 2012 or later, 69 patients were included in NOPT subgroup (median age: 67 years; 60.9% female; 76.8% White) and 72 in REF subgroup (median age: 68 years; 51.4% female; 69.4% White). Patients in NOPT subgroup had 3 times more IP admissions (adjusted IRR=3.1; p <0.001) with almost 7 times more IP days (adjusted IRR=6.7; p<0.001), 80% more ED visits (adjusted IRR=1.8; p=0.036), and numerically more OP visits (adjusted IRR=1.2; p=0.056) in the first 2 years post-index, as compared to REF subgroup. Consistently, patients in NOPT subgroup had higher medical costs (adjusted Δ=$30,962 PPPY; p=0.008) mainly driven by higher OP costs (adjusted Δ=$8,380 PPPY; p=0.016) and higher ED costs (adjusted Δ=$4,332 PPPY; p=0.012) along with numerically higher IP costs (adjusted Δ=$14,063 PPPY; p=0.100) in the first 2 years post-index, as compared to REF subgroup ( Figure 1). Conclusions: In this study of patients with CML-CP non-optimal treatment resulting in early treatment discontinuation/switch or dose interruptions/reduction in the first 6 months from treatment initiation or low adherence with the currently approved TKIs in 1L and 2L therapy for CML was associated with significant incremental economic burden. This highlights the need for more tolerable therapeutic options early in the treatment course.
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 | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».