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Enregistrement W4405049588 · doi:10.1182/blood-2024-207213

Impact of Adverse Events on Health-Related Quality of Life in Patients with Chronic Myeloid Leukemia (CML) Treated with Tyrosine Kinase Inhibitors (TKIs) - Early Results of the Survey on Humanistic Burden of Intolerance to First or Second TKIs (SHIFT) Study

2024· article· en· W4405049588 sur OpenAlexaff
Kelly L. Schoenbeck, Joan Clements, Karen DeMairo, David Wei, Nisha C. Hazra, Cristina Constantinescu, Yan Meng, Dominick Latrémouille-Viau, Kejal Jadhav, Courtney McDermott, Daisy Yang, Andrea Damon, Islam Sadek, Annie Guerin, Kathryn E. Flynn

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensGroup for Research in Decision Analysis
Organismes subventionnairesnon disponible
Mots-clésMedicineMyeloid leukemiaAdverse effectQuality of life (healthcare)Tyrosine kinaseTyrosine-kinase inhibitorLeukemiaImatinibOncologyInternal medicineImmunologyCancerReceptor

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Patients with chronic phase CML (CP-CML) treated with first- (1G) or second-generation (2G) TKIs as their first or second treatment often experience TKI-associated adverse events (AEs) leading to changing medications, treatment cessation, or reduced quality of life (QoL). The SHIFT study aims to provide a better understanding of how AEs impact health-related QoL in patients with CML in US real-world settings. METHODS: The SHIFT study is a cross-sectional web-based survey of patients with CML to collect their perspectives on the humanistic burden of AEs. Eligible participants include adults (≥ 18 years old) receiving a TKI for ≥ 3 months as first or second treatment for CP-CML at the time of the survey. AEs are self-reported using the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Health-related QoL is measured using the Patient-Reported Outcomes Measurement Information System - Global Health (PROMIS-GH10; T-scores, lower scores are worse) and the Work Productivity and Activity Impairment Specific Health Problem (WPAI:SHP; impairment percentages, higher scores are worse) questionnaires. In addition to current state, responses on patients' “low point” are collected, defined as the time(s) when AEs had the biggest impact on QoL since start of the current TKI. Data collection began June 19, 2024 and is ongoing. RESULTS: As of July 8, 2024, 63 patients participated in the survey, with a median age of 52 years (86% female, 95% White, non-Hispanic). Most were commercially insured (62%; 27% Medicare, 8% Medicaid, 3% other/military) and half were employed (52%; 27% on disability, 15% retired, 6% not employed). Patients were evenly distributed between receiving a first (52%) or second TKI (48%), and half were treated in a community-based setting (51%; 37% in an academic center). The majority had been on their current TKI for ≥ 2 years (84%; 8% 1 to < 2 years, 8% < 1 year), with 25% treated with 1G and 70% with 2G TKIs. In the last 7 days prior to the survey, patients experienced a median of 4.0 AEs (range: 0 - 14) with the most common being fatigue (75%), joint pain (51%), problems with memory (41%), diarrhea (32%), and muscle pain (32%). There were no reported hospitalizations in the last 7 days, suggesting these AEs were of low-grade. Persistent AEs were reported by almost all patients (91%). One quarter (24%) experienced a low point in the last 7 days, with 60% experiencing low point(s) before the last 7 days. Low point(s) were mostly experienced in the first 3 months of treatment (40%), with patients reporting multiple low points throughout their treatment with current TKI (23% > 3 to < 6 months, 36% 6 months to < 1 year, 23% 1 years to < 2 years, 32% ≥ 2 years). The mean ± SD PROMIS-GH10 Global Physical Health (GPH) and Global Mental Health (GMH) T-scores were 43.1 ± 8.3 and 44.9 ± 8.3, respectively, reflecting poorer health than the general population (mean T-score 50). Over one-third reported a fair-to-poor rating for GPH (41.3%) and GMH (33.3%). GPH (mean ± SD 37.6 ± 6.6) and GMH (mean ± SD 38.8 ± 7.2) T-scores were worse in patients experiencing a low point in the last 7 days. Based on WPAI, mean percent activity impairment due to CML was 40.0%. Close to half (43%) reported modifying employment due to CML (i.e., changed from full time to part time or unemployed [21%], took early retirement [11%], stopped working temporarily [6%], increased working time to cover CML treatment costs [5%]). Among those employed, 75% reported some productivity loss due to CML, with a mean work productivity loss (percent overall work impairment) of 32.2%. Presenteeism (percent impairment while working) and absenteeism (percent work time missed) due to CML were reported at a mean of 29.1% and 6.6%, respectively. CONCLUSIONS: Findings of the ongoing SHIFT study demonstrate the humanistic burden of AEs associated with TKIs in patients with CML in the US. While survival of CML patients has improved with TKIs, nearly all participants reported persistent low-grade AEs that negatively impact their QoL, resulting in living with poorer global physical and mental health than the general population. Furthermore, nearly half had to modify their employment, and for those who continue to work, the majority reported impaired work productivity. Treatments with a better tolerability profile have the potential to reduce AEs, helping patients with CML to preserve a good QoL and maintain work productivity.

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)0,003
score de la tête « metaresearch » (Gemma)0,007
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 seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,021
Tête enseignante GPT0,288
Écart entre enseignants0,267 · 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

Citations1
Publié2024
Routes d'admission1
Résumé présentoui

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