MétaCan
Menu
Retour à la cohorte
Enregistrement W4389229290 · doi:10.1182/blood-2023-185370

Prospective Assessment of Co-Morbidities and Framingham Risk Score in Newly Diagnosed Chronic Myeloid Leukemia (CML) Patients and Its Impact on Clinical Outcomes Following Frontline TKI Therapy: Toronto CML Genomic Alliance in Greater Toronto Area & Ontario (TCGA-GTA)

2023· article· en· W4389229290 sur OpenAlexaffabout
May Chiu, Gopila Gupta, María Agustina Perusini, Eshrak Al‐Shaibani, Jenny Ho, Dennis Dong Hwan Kim

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensLondon Health Sciences CentrePrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMedicineComorbidityInternal medicineFramingham Risk ScoreFamily medicineDisease

Résumé

récupéré en direct d'OpenAlex

Introduction Front-line (1L) tyrosine kinase inhibitor (TKI) drug selection is a pearl of clinical practice in CML. It requires consideration of multiple aspects including the goal of therapy, potency of the drug as well as comorbidity of the patient (pt). Contemporary CML practice emphasizes the importance of comorbidity for TKI selection. However, such data is scarce particularly the prevalence of comorbidity and its impact on 1L drug selection. We are developing a shared care model with community hematologists in Greater Toronto Area (TCGA-GTA). Newly diagnosed CML pts in the community will be referred to the study core center for prospective enrolment into the CML registry, informed consenting for genetic testing/future research, and evaluation of baseline comorbidity. Pt will return to referring hematologists for management of CML, including 1L TKI treatment as per standard of care, and is scheduled to visit the study center at 6 and 12 months, then annually for 10 years. It gives us a unique opportunity to evaluate the impact of comorbidity on 1L TKI drug selection. Patients and method Adult pts with newly diagnosed CML of any phase within 2 months of diagnosis are eligible for the study unless they are not able to visit study core center. Comorbidities and Framingham risk score (FRS) were assessed at the time of initial evaluation. Mutation profile testing is done by a bar-coded error-corrected sequencing platform for 40 genes developed at the Ontario Institute for Cancer Research. Cumulative incidence of molecular response with 2 log (MR2), 3 log (MR3), 4 log reduction or deeper (MR4), and treatment failure (TF) were calculated using cumulative incidence method considering competing risk. Event-free survival (EFS) was defined as time from start of TKI until TKI stop/switch, TF or death. Failure-free survival (FFS) was defined as time from start of TKI to TF [primary resistance, loss of complete cytogenetic response, new additional cytogenetic abnormalities (ACAs), and progression to accelerated or blasts phase (AP/BP)] or death. Results From November 2020 until June 31, 2023 (data cutoff), a total of 77 pts were enrolled in the study. The median follow-up time was 12 months. The frequency of 1L TKI selection was as follows: IMATINIB (IM) (n=23, 30%), DASATINIB (n=25, 32%), NILOTINIB (n=22, 29%), BOSUTINIB (n=1, 1%), ASCIMINIB (n=6, 8%). Majority of the pts were male (n=51, 66%). Ninety-three percent of pts were in chronic phase at diagnosis, while 4% and 3% were in AP and BP, respectively. Presence of ACAs was detected in 18 pts (23%), 10 of which were high risk, while 6% (n=5) were not evaluable. The baseline clinical and disease characteristics of pts on 1L IM vs other TKIs (2G-TKI) are summarised in Table 1. Of note, pts on IM were significantly older with higher FRS and comorbidities, including coronary artery disease, diabetes mellitus, and hypertension. The OS of all pts at 1 year was 98.5% (95% confidence interval 90%-99.8%). At 1 year, pts with intermediate to high risk FRS had lower FFS (73.2% vs 86.5%, p=0.037) while history of hyperlipidemia was associated with lower EFS (59.3% vs 81.5%, p=0.023) and 6 times more likely to stop or switch therapy due to intolerance or resistance (HR 5.9, p=0.002). Baseline comorbidities did not demonstrate signifincant impact on MR2 and MR3 at 1 year but pts with low FRS were more likely to achieve MR4 within 1 year (22.3% vs 0%, p=0.023). Age did not affect any of the clinical and molecular endpoints. Compared to 2G-TKI, IM had worse EFS (HR 3.93, p=0.003), FFS (HR 5.66, p=0.011), and MR2 (47.3% vs 84.4%, p=0.003) at 1 year. Further, IM pts experienced higher rate of any side effects compared to those on 2G-TKI (69.6% vs 31.5%, p=0.003). Conclusion The TCGA-GTA study provides valuable insights into the impact of comorbidity on selection of 1L TKI and its outcome in a real-world setting. Pts who are older and with higher FRS and comorbidities tend to receive 1L IM. However, pts on 1L 2G TKIs are more likely to reach treatment goals faster with lower probability of TF. The present study did not reach a clear conclusion whether the worse outcomes in the pts receiving IM are from IM treatment or concurrent risk factors such as age and comorbidities, thus further study with an expanded cohort and longer follow-up is required. Elderly pts or those with comorbidities would need alternative treatment with better efficacy and tolerability, such as Asciminib, that can overcome comorbidity issues in the future.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
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 enseignants0,773
Score d'incertitude au seuil0,450

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,036
Tête enseignante GPT0,348
Écart entre enseignants0,312 · 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
Publié2023
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueBloodMême sujetChronic Myeloid Leukemia TreatmentsTravaux en français237 207