Patterns and predictors of electronically measured oral anticancer medication (OAM) adherence among patients with multiple myeloma (MM).
Notice bibliographique
Résumé
7547 Background: Adherence to costly, long-term OAM is a mainstay of life-extending therapy for patients with MM and can dramatically affect cancer outcomes, but little is known about adherence in patients with MM. The purpose was to describe temporal patterns and predictors of OAM adherence among patients with MM via electronic event monitoring (EEM) adherence data. Methods: This was a six-month prospective study of OAM adherence, symptoms, and quality of life among n=70 patients prescribed OAM maintenance therapy for MM who used EEM. Patient reported measures of symptoms (Edmonton Symptom Assessment Scale; Patient Health Questionnaire-9; PROMIS Fatigue; Brief Pain Inventory; Comprehensive Score for financial Toxicity), sociodemographic data, and medical record clinical data were collected at enrollment and 3 and 6 months. Group-based trajectory modeling (GBTM) was applied to EEM data based on AARDEX MEMS smart pill bottles, aggregated monthly (i.e., 30-day intervals) over 6 months of monitoring. Adherence indices were % of prescribed dosestaken and % of days with correct intake. Predictors of adherence trajectory group membership were explored and summarized as bivariate correlations as effect sizes. Results: Participants were on average 63.9 y/o (SD=10.6) and predominantly male (55.9%) and non-Hispanic white (86.8%) or Black (10.3%). At enrollment, participants had been prescribed lenalidomide (68.6%) or pomalidomide (31.4%) for a median 11.5 (IQR: 22, range: 0-100) months. For mean dose adherence, GBTM revealed 3 distinct trajectories: 62.9% were in the high (~97% adherence) and slightly linear decreasing adherence group (π 3 =.627); 27.1% were in the high/moderate and curvilinear decreasing group (π 2 =.272), representing 85% adherence at start, dropping to <70% by 6 months; and 10% had a low and curvilinear (π 1 =.100) pattern, representing only ~40% adherence over time. For mean days adherence, 2 distinct trajectories were identified: high and linear decreasing (81.4%, π 2 =.801), representing adherence starting at 90%, dropping to 85%; and low and stable (18.6%, π 1 =.199), representing ~40% adherence over time. Effect sizes for baseline predictors of the low trajectory group ranged from .02 to .37 (median r = .20, small), In particular, participants who self-identified as non-Hispanic Black or Hispanic “other” race being more likely to be in the low trajectory groups for both dose (p=.009) and days (p=.010) adherence. Conclusions: OAM adherence measured with EEM data was dynamic and suggests potential mechanisms of health inequities by race and ethnicity and a need for interventions to monitor for and address disparate adherence. Larger studies with longer observation and more frequent assessments with in-depth social determinants of health are needed to better understand OAM adherence patterns and correlates over time.
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 | 0,002 | 0,002 |
| 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 ».