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Enregistrement W4417016598 · doi:10.1182/blood-2025-6388

The humanistic burden of patients with chronic myeloid leukemia (CML) treated with first line (1L) tyrosine kinase inhibitors (TKIs)

2025· article· en· W4417016598 sur OpenAlexaff
Kelly L. Schoenbeck, Joan Clements, Karen DeMairo, David Wei, Nisha C. Hazra, Cristina Constantinescu, Yan Meng, Dominick Latrémouille-Viau, Gabriel Marquez, Daisy Yang, Andrea Damon, Islam Mohamad Sadek, Annie Guérin, Kathryn E. Flynn

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensGroup for Research in Decision Analysis
Organismes subventionnairesnon disponible
Mots-clésDiscontinuationMyeloid leukemiaAdverse effectQuality of life (healthcare)First lineBosutinibCohortNilotinibPopulation

Résumé

récupéré en direct d'OpenAlex

Abstract INTRODUCTION: While TKIs significantly extend survival in CML, their adverse events (AEs) may lead to treatment discontinuation and poor health-related quality of life (HRQoL). Few studies have described the HRQoL of US patients (pts) on TKIs. This study assessed the humanistic burden of pts with CML treated with first-line (1L) TKIs, including their AE profile, HRQoL, and work productivity, using patient-reported outcomes. We also evaluated communication barriers between pts and treating physicians. METHODS: Cross-sectional online surveys were conducted (June-December 2024) among US CML pts. Adults receiving 1L TKIs (imatinib, dasatinib, nilotinib, bosutinib) for ≥3 months (mos) were eligible to participate; asciminib in 1L was not yet approved at study start. AE data were collected via PRO-CTCAE, and pts completed surveys on patient-physician communication about AEs. HRQoL was evaluated using the PROMIS-Global Health-10 (Global Physical Health [GPH] and Global Mental Health [GMH], general population mean±SD of 50±10, lower T-scores=poorer health), and Work Productivity and Activity Impairment: Specific Health Problem (WPAI:SHP, higher percentages=greater impairment) questionnaires. “Low points,” defined as the time(s) when AEs had the greatest impact on HRQoL, were also reported. RESULTS: A cohort of 162 pts (median age 45 yrs [range 18-82], 60% female, 19% non-White) treated with a 1L TKI (42% imatinib, 38% dasatinib, 11% nilotinib, 9% bosutinib) participated. Half were employed (51%; 21% retired, 15% not employed, 6% on disability) and treated in a community-based setting (54%; 41% academic, 5% other/unsure). Two-thirds were commercially insured (65%; 26% Medicare, 7% Medicaid, 2% military/unsure). Over half had been on 1L TKI for ≥1 yr (59%; 11% 3 to <6 mos, 30% 6 mos to <1 yr). In the last 7 days, pts reported a median of 3 AEs (range 0-14); most commonly, fatigue (51%), pain (45%; joint/muscle pain), and gastrointestinal (33%; nausea, diarrhea, vomiting, constipation). Three-quarters (75%) had ≥1 AE, mostly chronic, in the last 7 days. Most pts (82%) experienced low points since TKI start, most commonly in the first 3 mos of treatment (59%), with 24% reporting ≥1 low point in the last 7 days. Nearly all pts (98%) reported discussing AEs at least once with their physician, most frequently at diagnosis (72%) or at subsequent visits (75%); half (54%) discussed AEs at every visit. Two-thirds (64%) reported being satisfied with their discussions about AEs. However, some delayed or did not report AEs to their physicians (17%), for reasons that they “just had to live with it” (70%), were “afraid the doctor may decide to change treatment” (44%), or did not want to “be a burden” (33%). PROMIS-GH-10 revealed pts with CML had worse health than the general population, with mean±SD GPH T-score of 43.2±7.4 and GMH T-score of 43.9±7.6. Among pts with low points in the last 7 days, GPH and GMH T-scores were even worse (GPH: 37.5±6.5; GMH: 38.0±7.4). WPAI SHP showed over half (54%) of pts reported employment change due to CML (12% retired early, 11% from full- to part-time, 10% from full-time to unemployed, 8% stopped working temporarily or reduced workload), with an overall mean activity impairment of 35.1% (range 0-100%). Among those employed, 80% reported work impairment due to CML, with a mean percent work productivity loss of 29.9%. Mean impairment while working (presenteeism) and work time missed (absenteeism) due to CML were 27.2% and 7.6%, respectively. Pts with low points in the last 7 days had even higher mean activity impairment (52.8%) and work productivity loss (38.8%) due to CML. CONCLUSIONS:Our real-world study demonstrates pts with CML treated with 1L TKIs in the US experience chronic AEs contributing to worse HRQoL, including physical and mental health, as well as work impairment. This finding of impaired work productivity due to CML is particularly significant in the context of employer-provided health insurance in the US. While most patients discussed AEs with physicians early in their disease course, only half discussed AEs at every visit and two-thirds were satisfied with their discussions. In addition, some patients delayed or avoided reporting AEs due to internal barriers. As CML requires lifelong treatment, pts may benefit from greater recognition of AEs and strategies to improve HRQoL and maintain work productivity, including TKIs with better tolerability and more consistent approaches to monitoring.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,006
Tête enseignante GPT0,218
Écart entre enseignants0,212 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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