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Enregistrement W2559499811 · doi:10.1182/blood.v126.23.4026.4026

Propensity Score Matching Analysis Demonstrates the Use of Statin Enhances Chance of Achieving MR4.5 in Chronic Myeloid Leukemia Patients in Chronic Phase Following Imatinib Therapy Regardless of Other Clinical Features Including Age of the Patients

2015· article· en· W2559499811 sur OpenAlexaff
Dennis Dong Hwan Kim, Feras Alfraih, Tanya Adityan, Jeffrey H. Lipton

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer, Lipids, and Metabolism
Établissements canadiensUniversity of TorontoPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineStatinImatinibInternal medicineMyeloid leukemiaImatinib mesylateOncologyPropensity score matchingClinical trialRandomized controlled trialDosing

Résumé

récupéré en direct d'OpenAlex

Abstract BACKGROUND: HMG-CoAreducatase inhibitor, or statin was suggested to increase therapeutic efficacy of anti-cancer therapy, to improve response rate of chemotherapy and to improve survival of cancer patients. Statin family of drugs is known to trigger tumor specific apoptosis and to result in growth arrest in leukemias (Penn, Leukemia 2002). Our previous study (Kim, ASH 2014) suggested the use of statin could enhance chance of achieving MR4.5 with imatinib therapy by 78.5% in 503 chronic myeloid leukemia (CML) patients in chronic phase. A concern has remained regarding that the clinical features of patients receiving statins are more related to the adherence to medication including age. For example, elderly patient who is known to be more adherent to medication dosing, is more likely to take statins. Thus clinical and demographic characteristics of patients should be taken account into the interpretation of enhancing therapeutic effect of statin in combination with imatinib. Propensity score matching (PSM) analysis is a statistical method to adjust for the clinical factors which affect the choice of treatment between different treatment options. Using PSM analysis, clinical and demographic characteristics between the groups with vs without statin can be balanced out, thus mimicking randomized controlled prospective trial. METHODS: Out of 408 patients treated with imatinib at 400mg daily dose as a frontline for CML in CP, 88 patients was identified as "statin" group, while remaining 320 as "non-statin" group. The statin group was defined as those on statin for cholesterol control at the time of imatinib commencement and remaining on statin while on at least 3 years or longer. There was significant difference in age between the statin vs non-statin group (median 62 vs 49, p<0.001). Other clinical factors did not show an difference between the 2 groups including gender, sokal risk group or additional cytogenetic abnormalities at the time of imatinib commencement. Using PSM analysis, we performed a case-control study with well-balanced pairs of patients treated with vs without statin. Pre-treatment variables included in the PSM were age, gender, Sokal risk group and additional cytogenetic abnormalities. A total of 84 case-control pairs were selected within 0.05 of a difference in propensity score. Paired analysis was adopted throughout the PSM analysis. Treatment outcomes were evaluated for the response to TKI therapy with respect to complete cytogenetic response (CCyR), major molecular response (MMR), molecular response at 4.5 (MR4.5) and for long-term outcomes including treatment failure, progression free- (PFS) and overall survival (OS). Cumulative incidence method considering competing risk was adopted to calculate the incidences of MCyR, CCyR, MMR and MR4.5. Discontinuation of imatinib was accounted as competing risk in the analysis. Treatment failure, PFS and OS was also evaluated. RESULT: After the PSM matching, each of 84 patients was selected, thus a total of 168 patients were included in the final analysis. With a median follow-up duration of 6 years (range 3 months to 14 years), clinical and demographic characteristics between the 2 groups did not show any differences including age (p=0.813), gender (p=0.440), Sokal risk group (p=0.888), and additional cytogenetic abnormalities (p=0.682), thus balancing all the confounding factors related to the use of statin. The statin group showed a higher MR4.5 rate than non-statin groups (p=0.019): 56.8±11.9% vs 47.0±11.6%, MR4.5 at 5 years. The use of statin increased the chance of achieving MR4.5 by 64.3% (hazard ratio 1.643 [1.080-2.501]). There is a trend of better MMR at 18 months in statin group compared to non-statin group: 68.2±10.7% vs 53.1±11.1% (p=0.072). However, the use of statin was not found to be associated with improvement in treatment failure (p=0.953), PFS (p=0.938) or OS (p=0.734). CONCLUSION: The use of statin is suggested to improve deeper molecular response following imatinib therapy in CML-CP patients. After taking account for potential confounding clinical variables using PSM analysis, we confirmed that independent of age or other clinical variables, the use of statin could enhance deeper molecular response following imatinib therapy in CML-CP patients. This approach of adopting statin in CML treatment could potentially increase the proportion of patients eligible for TKI discontinuation attempt. Disclosures Kim: Novartis Pharmaceuticals: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding. Lipton:Teva: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Ariad: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Novartis Pharmaceuticals: Consultancy, Research Funding.

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)0,003
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 enseignants0,005
Score d'incertitude au seuil0,018

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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 Opus0,066
Tête enseignante GPT0,328
Écart entre enseignants0,261 · 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
Publié2015
Routes d'admission1
Résumé présentoui

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