Patient Preferences for Stopping Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia
Notice bibliographique
Résumé
Abstract Abstract 4274 Introduction: After the introduction of tyrosine kinase inhibitors (TKIs), chronic myeloid leukemia (CML) became the first cancer with a medical treatment that affords patients a normal lifespan. First-line treatment includes one of the three approved TKIs with regular molecular monitoring. Several reports have described individuals stopping imatinib and remaining in complete molecular response (CMR). There are currently several ongoing randomized clinical trials evaluating the safety of stopping TKI treatment in patients with a sustained CMR. In 2010 the preliminary results from the STIM (STop IMatinib) trial (Mahon, Lancet Oncology), were published. Results showed that 38% of patients had a sustained CMR after 2 years off TKI treatment and the remaining 62% who relapsed responded to restarting their previous TKI treatment. As this research will potentially influence clinical practice in the near future, we aimed to explore patient reactions, preferences and risk acceptability of stopping TKI treatment. With that in mind we conducted an interview-assisted survey of CML patients seen at a single tertiary care centre. Methods: We included CML patients with cytogenetic and molecular Ph+ chromosome confirmation currently being treated with a TKI. Patients were approached during regular follow-up appointments. A survey was conducted through structured interviews using a standard form. Patients' preferences were explored through a case-based scenario using Visual Analog Scales ranging from 0 to 100% or 5-point Likert scales ranging from “absolutely stop” (1) to “absolutely not stop” (5). A trained interviewer asked the survey questions and was able to clarify questions that were unclear to the participant. Data was analyzed using proportions for dichotomous variables and medians and interquartile ranges for continuous variables. 95% confidence intervals for the proportions were calculated using the normal approximation interval. Results: Interviews were conducted between June and August 2012 at the London Regional Cancer Program (LRCP) in London, Ontario. 38 out of 40 (95%) CML patients approached completed the survey. Mean age of participants was 51 years old and 47% were male. 37% of participants had not finished high school and another 37% had completed college/university/trade school. Participants had a diagnosis of CML for an average of 50 months prior to enrollment. The majority (21/38 participants, 55%) were taking imatinib, with 11 (29%) on nilotinib and 6 (16%) on dasatinib. 71% (95% CI ± 14%) of the participants said that taking their medications daily was “simple and easy and they were able to remember 100% of the time.” 26% reported daily side effects while 24% reported never experiencing side effects from their TKI. 79% (95% CI ± 13%) of the participants said that they have never considered stopping the drug based on the side effects that they experience. 61% of participants responded that fear of the disease going out of control keeps them taking their TKI (95% CI ± 16%), whereas 34% responded that it is their doctor's strong recommendation that motivates them (95% CI ± 15%). When asked what risk of relapse after stopping the TKI they would be willing to accept the median response was a 25% relapse rate (interquartile range 20–50). When responding to the same question after informing the participant that all patients have responded to restarting TKIs the median response increased to a 35% relapse rate (interquartile range 20–60). When given a relapse rate of 20% and a likert scale ranging from “absolutely stop” to “absolutely not stop,” the median response was “likely stop” with the 25th and 75th interquartile ranges being “absolutely stop” and “likely not stop” respectively. When the published relapse rate of 60% was given, however, the median was “likely not stop” with the 25th interquartile range at “neutral to stopping” and 75thinterquartile range at “absolutely not stop.” Discussion: This data suggests that the majority of participants perceive little difficulty with taking their TKI regularly and have never considered stopping it. Two major determinants on participant's decisions are fear of the disease going out of control and their physician's influence. Further, with the published rate of relapse after stopping TKI treatment the majority of individuals would choose to continue taking their medications for CML. Disclosures: Lazo-Langner: LeoPharma: Honoraria; Pfizer: Honoraria. Hsia:Novartis: Participant in Advisory Board Other.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».