MétaCan
Menu
← Retour à la cohorte
Enregistrement W4405042676 · doi:10.1182/blood-2024-205524

Trajectories of Frailty Categorization over Time Among Real-World Patients with Multiple Myeloma: A Prospective Cohort Study (MFRAIL)

2024· article· en· W4405042676 sur OpenAlexaffabout
Imran Haider, Darryl P. Leong, Martha Louzada, Arleigh McCurdy, Gregory R. Pond, Ruthanne Cameron, Mohammed A. Aljama, Alissa Visram, Tanya M. Wildes, Hira Mian

Notice bibliographique

RevueBlood · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensOttawa HospitalLondon Health Sciences CentreMcMaster UniversityPopulation Health Research Institute
Organismes subventionnairesnon disponible
Mots-clésMultiple myelomaMedicineProspective cohort studyCategorizationHematologic NeoplasmsCohortInternal medicineCohort studyOncologyCancerComputer scienceArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

Introduction Multiple myeloma (MM) is plasma cell neoplasm of older adults with frail individuals being at an increased risk of poor outcomes including worse survival as well as increased toxicity. Several tools have been developed to assess frailty to categorize patients from fit to frail. However, current tools have been designed to assess frailty at a single timepoint at diagnosis. Given frailty is dynamic in nature, the objective of this analysis was to further understand how frailty categorization may change over time using three commonly utilized MM frailty assessment tools among real-world patients. Methods MFRAIL is an ongoing prospective cohort study conducted in three medical centers in Ontario, Canada. Participant recruitment began in August 2021, targeting patients initiating treatment for newly-diagnosed or relapsed MM. Eligibility criteria required participants to be age > 18, and start treatment within six weeks of study enrolment. Demographic, MM specific, and functional characteristics were assessed at baseline. Frailty was evaluated at baseline and at a 12-month follow-up using the following three frailty assessment tools: 1) the IMWG frailty index (Palumbo et al. 2015), 2) the Simplified Frailty Score (Facon et al. 2020), and 3) the Mayo Frailty Score (Milani et al. 2016). Both the absolute frailty scores (ranging from 0-5) as well as the frailty categorization were calculated. Results 100 patients enrolled, 99 completed baseline assessments and 82 patients completed 12-month follow-up assessments (9 deceased, 2 transitioned to long term care, and 6 withdrew). The baseline characteristics have previously been reported (Haider et al. 2024) including the variable categorization of patients classified as frail. The 12 month follow up data is highlighted below. At the 12 month follow-up period, the IMWG frailty index classified 37 (45%) patients as fit, 21 (26%) as intermediate fit, and 24 (29%) as frail. Of the 41 fit patients at baseline, 33 (80%) had no change in the absolute frailty score and remained fit, while 2 (5%) had a deterioration with 1 patient becoming intermediate fit and the other becoming frail. Amongst the 34 intermediate fit patients at baseline, 17 (50%) had no change in the absolute frailty score and remained intermediate fit, 2 (6%) had an improvement and became fit, while 5 (15%) had a deterioration and became frail. Of the 41 frail patients at baseline, 11 (27%) had no change in absolute frailty score, 11 (27%) had an improvement, and 1 (2%) had a deterioration. This corresponded to 18 (44%) frail patients remaining frail, 2 (5%) becoming fit and 3 (7%) becoming intermediate fit. Spearman's ρ between baseline and 12-month follow-up scores was 0.82. At 12 months, the simplified frailty score classified 42 (51%) patients as non-frail and 40 (49%) as frail. Of the 50 non-frail patients at baseline, 33 (66%) had no change in absolute frailty score remaining non-frail, while 9 (18%) had a deterioration becoming frail. Amongst the 66 frail patients at baseline, 17 (26%) had no change in absolute frailty score, 18 (27%) had an improvement, and 5 (8%) had a deterioration. This corresponded to 31 (47%) frail patients remaining frail, while 9 (14%) became non-frail. Spearman's ρ between baseline and 12-month follow-up scores was 0.74. The Mayo frailty score categorized 15 (18%) patients as Stage I, 35 (43%) patients as Stage II, 24 (29%) as Stage III, 3 (4%) as Stage IV and 5 (6%) as unknown frailty status. Of the 19 Stage I patients at baseline, 11 (58%) remained Stage I, 4 (21%) became Stage II, and 2 (11%) became Stage III. Amongst the 39 Stage II patients at baseline, 18 (46%) remained Stage II, 3 (8%) became Stage I, and 6 (15%) became Stage III. Of the 22 Stage III patients at baseline, 7 (32%) remained Stage III, and 5 (23%) became Stage II. Out of the 18 Stage IV patients, 3 (17%) remained Stage IV, 1 (6%) became Stage II, and 6 (33%) became Stage III. Spearman's ρ between baseline and 12-month follow-up scores was 0.67. Conclusion The distribution of frail vs non-frail patients demonstrated a substantial change over time. This highlights that a one-time, baseline frailty measurement may not be adequate for stratification or prediction of outcomes both in the real-world as well as in clinical trials. Lastly, changes in continuous absolute frailty score may be better suited for dynamic measurements and capture early improvement/deterioration prior to changes observed in frailty classification.

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)0,002
score de la tête « metaresearch » (Gemma)0,003
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 enseignants0,011
Score d'incertitude au seuil0,022

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
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 Opus0,010
Tête enseignante GPT0,270
Écart entre enseignants0,260 · 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'admission2
Résumé présentoui

Explorer davantage

Même revueBlood→Même sujetMultiple Myeloma Research and Treatments→Travaux en français237 207→