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Enregistrement W3211916615 · doi:10.1182/blood-2021-148731

Impact of Polypharmacy and Potentially Inappropriate Medications Among Older Adults with Blood Cancers

2021· article· en· W3211916615 sur OpenAlexaffabout
Tammy T. Hshieh, Clark DuMontier, Tim Jaung, Nupur E. Bahl, Emily S. Magnavita, Chelsea E. Hawley, Lee Mozessohn, Richard M. Stone, Robert J. Soiffer, Jane A. Driver, Gregory A. Abel

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiquePharmaceutical Practices and Patient Outcomes
Établissements canadiensHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésPolypharmacyMedicinePopulationComorbidityGeriatric oncologyGeriatricsAdverse effectDeprescribingMontreal Cognitive AssessmentGerontologyCancerInternal medicineCognitionPsychiatryCognitive impairment

Résumé

récupéré en direct d'OpenAlex

Abstract Background. Polypharmacy and potentially inappropriate medications (PIMs) are common among older adults with blood cancer and can lead to adverse effects and poor outcomes. Polypharmacy is commonly defined as taking ≥5 or ≥8 medications, depending on the population. PIMs can cause adverse side effects for certain patients, e.g. diphenhydramine and benzodiazepines. We sought to define the prevalence of polypharmacy and PIMs in older adults with blood cancers, and to examine the association between both with cognitive impairment and frailty in this population. Methods. From February 2015 to November 2019, all transplant-ineligible patients ages 75 and older who presented for initial consultation for hematologic malignancy at the Dana-Farber Cancer Institute (Boston, MA) were approached by a research assistant (RA) for a 15-minute screening geriatric assessment. The RA assessed 42 aging-related health deficits using patient-reported and objective performance measures spanning the domains of function, cognition, comorbidity, and mobility. Patients were determined to be frail, pre-frail or robust via two approaches: deficit accumulation approach (Rockwood, JGMedSci 2007) and phenotypic approach (Fried, JGMedSci 2001). Cognition was measured using the delayed recall section of the Montreal Cognitive Assessment (MoCA; Nasreddine, JAGS 2005) and Clock-In-Box test (CIB; Chester, Am J Med 2011). In addition, we collected data via electronic medical record review of all prescribed and over-the-counter medications patients were taking at the time of initial consultation. These data were reconciled and reviewed for quality by two board-certified geriatricians. The geriatricians identified 2 types of PIMs: anticholinergic PIMs per the Anticholinergic Risk Scale (Rudolph, Arch Intern Med 2008) and cancer-specific PIMs per the National Cancer Care Network Medications of Concern (NCCN Older Adult Oncology 2020). For patients recommended for active cancer treatment, the association between polypharmacy and PIMs with frailty was assessed using ordinal logistic regression. The association between polypharmacy and PIMs with cognitive impairment (by MoCA delayed recall and CIB) was assessed using logistic regression. All models controlled for age, gender, and comorbidity (via Charlson Comorbidity Index). Results. In this patient cohort (N=785), 286 (36%) were female with 240 (30%) in the leukemia disease group, 272 (35%) lymphoma and 273 (35%) multiple myeloma. 603 (77%) patients had polypharmacy (≥5 medications) and 421 (54%) were taking ≥8 medications. 201 (25%) patients were taking at least one PIM based on the Anticholinergic Risk Scale (Rudolph) and 343 (44%) based on the NCCN guidelines. Overall, 131 (17%) were frail, 457 (58%) pre-frail and 197 (25%) robust. 541 (69%) patients had Charlson Co-morbidity Index ≥3; 111 (14%) patients had "probable" cognitive impairment by MoCA Delayed Recall and 147 (19%) had "probable" cognitive impairment by CIB. In the 468 (60%) patients on active cancer treatment, there was an association of frailty with polypharmacy defined by a cutoff of ≥8 (adjusted odds ratio [aOR]=2.82, 95% confidence interval [CI] 1.92-4.17), but not ≥5 medications (aOR=1.42, 95% CI 0.91-2.22; Table 1). With each additional medication on a patient's medication list, their odds of being more frail increased by 8% (aOR=1.08, 95% CI 1.04-1.12). With each one-point increase on the Anticholinergic Risk Scale, odds of being more frail increased by 19% (aOR=1.19, 95% CI 1.03-1.39). With each additional PIM based on NCCN guidelines, odds of being more frail increased by 65% (aOR=1.65, 95% CI 1.34-2.04). Polypharmacy and PIMs were not associated with cognitive impairment by either MoCA Delayed Recall or CIB. Conclusion. Polypharmacy and PIMs are prevalent among older patients with blood cancers and are strongly associated with frailty but not cognitive impairment, independent of comorbidity. Increasing number of anticholinergic and especially cancer-specific PIMs have a stronger association with frailty compared to increasing number of medications in general. Our findings highlight that the types of medications contributing to polypharmacy may be more important than number of total medications. This suggests the need for streamlined ways of identifying specific PIMs in practice to deprescribe medications that may be associated with cumulative harm in older adults with cancer. Figure 1 Figure 1. Disclosures Stone: Aprea: Consultancy; Boston Pharmaceuticals: Consultancy; BerGen Bio: Membership on an entity's Board of Directors or advisory committees; Arog: Consultancy, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; OncoNova: Consultancy; Syndax: Membership on an entity's Board of Directors or advisory committees; Syntrix/ACI: Membership on an entity's Board of Directors or advisory committees; Jazz: Consultancy; Macrogenics: Consultancy; Novartis: Consultancy, Research Funding; Glaxo Smith Kline: Consultancy; Innate: Consultancy; Janssen: Consultancy; Elevate Bio: Membership on an entity's Board of Directors or advisory committees; Foghorn Therapeutics: Consultancy; Gemoab: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Research Funding; Actinium: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers Squibb: Consultancy; Celgene: Consultancy; Abbvie: Consultancy; Syros: Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy. Soiffer: Rheos Therapeutics, USA: Consultancy; Kiadis, Netherlands: Membership on an entity's Board of Directors or advisory committees; Juno Therapeutics, USA: Other: Data Safety Monitoring Board; Precision Biosciences, USA: Consultancy; Jazz Pharmaceuticals, USA: Consultancy; Takeda: Consultancy; Jasper: Consultancy; Gilead, USA: Other: Career Development Award Committee; NMPD - Be the Match, USA: Membership on an entity's Board of Directors or advisory committees.

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)0,008
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 enseignants0,012
Score d'incertitude au seuil0,024

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,025
Tête enseignante GPT0,345
Écart entre enseignants0,320 · 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

Citations4
Publié2021
Routes d'admission2
Résumé présentoui

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