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Enregistrement W3097213049 · doi:10.1182/blood-2020-136163

Optimal Duration of Imatinib Treatment / Deep Molecular Response for Treatment-Free Remission after Imatinib Discontinuation from a Canadian Tyrosine Kinase Inhibitor Discontinuation Trial

2020· article· en· W3097213049 sur OpenAlexaffabout
Dennis Dong Hwan Kim, Igor Novitzky‐Basso, Tae-Hyung Kim, Eshetu G. Atenafu, Lynn Savoie, Isabelle Bence‐Bruckler, Donna L. Forrest, Lambert Busque, Robert Delage, Anargyros Xenocostas, Mary‐Margaret Keating, Elena Liew, Kristjan Paulson, Tracy Stockley, Pierre Laneuville, Jeffrey H. Lipton, Suzanne Kamel‐Reid, Brian Leber

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Myeloid Leukemia Treatments
Établissements canadiensMcMaster UniversityMcGill University Health CentreQueen Elizabeth II Health Sciences CentreLondon Health Sciences CentreJuravinski Cancer CentreHôpital de l'Enfant-JésusUniversité de MontréalHôpital Maisonneuve-RosemontPrincess Margaret Cancer CentreUniversity of CalgaryVancouver General HospitalAlberta Hospital EdmontonUniversity of British ColumbiaCancerCare ManitobaUniversity of Alberta HospitalOttawa HospitalUniversity Health NetworkUniversity of TorontoBC Cancer AgencyAlberta Health Services
Organismes subventionnairesnon disponible
Mots-clésDiscontinuationImatinibMedicineTyrosine-kinase inhibitorTyrosine kinaseImatinib mesylateDasatinibInternal medicineOncologyDrug holidayImmunologyCancerReceptorHuman immunodeficiency virus (HIV)

Résumé

récupéré en direct d'OpenAlex

Background: Several clinical factors have been proposed to predict successful tyrosine kinase inhibitor (TKI) discontinuation for treatment-free remission (TFR), among which a longer duration of total TKI or deep molecular response (DMR) duration correlate with increased success of TFR. Although many guidelines have been proposed to safely select patients who would be candidates for TFR attempt, there is some discrepancy regarding which duration of DMR and/or Imatinib (IM) treatment optimally stratifies patients according to their probability of TFR success. These suggested DMR or IM treatment durations are often the result of a panel consensus, and the method of calculation of these thresholds as categorical values is not provided. Thus, there is a practical need for an evidence-based determination of duration of IM therapy while awaiting TFR attempt to establish if the likelihood of successful TKI discontinuation reaches an optimal maximum after a certain duration of treatment and/or DMR. The present study attempted to define the optimal (i.e. shortest) duration of IM treatment or DMR that predicts TFR success at a specified level of confidence. Patients and methods: The Canadian TKI discontinuation study has enrolled 131 patients with the longest Imatinib treatment duration, at a median of 9 years. A Cox's proportional hazard ratio model was applied using molecular relapse-free survival (mRFS) as the endpoint. Continuous variables were initially tested using Cox's proportional hazard model and were converted into categorical variables according to the optimal cut-off values derived from the current analysis. We have evaluated six statistical parameters to determine the optimal cut-off of IM treatment duration and MR4 response duration for TFR prediction: 1) mRFS rate at 12 months between the groups, stratified according to the cut-off value, 2) proportion of patients divided by the cut-off value, 3) negative predictive value (NPV), 4) positive predictive value (PPV), 5) accuracy, and 6) the p-values as a measure of risk stratification power. The optimal cut-off was sought that met the joint criteria of a p-value ≤ 0.05, PPV≥60% and NPV≥60%. Results: Out of 131 patients enrolled, 123 patients completed a planned follow-up of 2.5 years. The mRFS rate was calculated as 56.8% (47.8-64.8%) at 12 months. One additional year of IM therapy increased the chance of TFR success by 5.5%, while one additional year of MR4 duration increased its likelihood by 5.1% by assessing the mRFS rates after 5 versus 9 years. The formula generated from this linear regression analysis is as follows: The probability of mRFS (%) = 0.05146 x (IM duration in year) + 0.08379; or 0.0555 x (MR4 duration in year) + 0.1844 For example, as shown in Figure A, a patient with a total IM treatment duration of less than 6 years has a mRFS rate of 36.0% (n=25), implying a high risk of TFR failure, while those patients with IM duration of above 6 years showed a mRFS rate of 61.8% (n=106). PPV is defined as the probability of TFR failure in a subject at high risk for TFR failure at the proposed cut-off, while NPV is defined as the probability of TFR success in a subject at low risk for TFR failure (i.e. at low chance of TFR success) at the proposed cut-off. An ideal cut-off value should have both a high PPV (68%) and NPV (61.3%), thus defining 6 years as the optimal cut-off. The next parameter evaluated is the p-value of each cut-off value as a measure for risk stratification power; an acceptable p-value is ≤ 0.05. IM duration above 5.6 years and MR4 duration in the range of 4.2-11 years meet these criteria, respectively. Accordingly, the optimal cut-off value for IM treatment duration is ~ 6 years (Figure A), while that for MR4 duration is ~ 4.5 years (Figure B). According to these cut-off values evaluated, patients treated with IM duration of 6 years or longer showed a superior mRFS rate at 12 months (61.8%) than those with less treatment (36.0%; p=0.01). Patients with MR4 duration of 4.5 years or longer showed a higher mRFS rate at 12 months (64.2%) than those with a shorter duration of deep molecular response (41.9%; p=0.003). Conclusion: In summary, we propose 6 years as the cut-off for IM duration with p-value=0.01, 68% PPV and 62% of NPV, while 4.5 years' cut-off value for MR4 duration is proposed with p-value=0.003, 63% of PPV and 61% of NPV. These results can be incorporated into clinical guidelines as optimal IM duration or MR4 duration for IM discontinuation to achieve successful TFR. Disclosures Bence-Bruckler: Merck: Membership on an entity's Board of Directors or advisory committees. Busque:Novartis: Honoraria; Pfizer: Honoraria; BMS: Honoraria. Delage:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Keating:Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Hoffman La Roche: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy; Servier: Membership on an entity's Board of Directors or advisory committees; Shire: Membership on an entity's Board of Directors or advisory committees; Taiho: Membership on an entity's Board of Directors or advisory committees. Lipton:Novartis: Consultancy, Research Funding; Bristol-Myers Squibb: Honoraria; Ariad: Consultancy, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding. Leber:Otsuka Pharmaceutical: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Treadwell: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda/Palladin: Honoraria, Membership on an entity's Board of Directors or advisory committees; Alexion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Lundbeck: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

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)0,004
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai non randomisé · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,185
Score d'incertitude au seuil0,367

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,000
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 Opus0,017
Tête enseignante GPT0,265
Écart entre enseignants0,248 · 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'étudeEssai non randomisé
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

Citations5
Publié2020
Routes d'admission2
Résumé présentoui

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