Practice Gaps and Barriers To Optimal Care Among Hematologists and Medical Oncologists Treating Patients With Chronic Myeloid Leukemia (CML), Acute Lymphoblastic Leukemia (ALL), and B-Cell Lymphomas In The United States: Results Of a Two-Phase Qualitative/Quantitative Study
Notice bibliographique
Résumé
Abstract Background Rapid advances in the understanding of the biology of CML, ALL, and B-cell lymphomas have led to novel therapeutic interventions that have increased the clinical complexity of decision making in patient care. This study was designed to quantify professional practice gaps and barriers to optimal care among hematologists and medical oncologists treating patients with CML, ALL, and B-cell lymphomas at academic medical centers and/or community cancer centers and clinics in the United States. Methods In March-June 2013, 250 hematologists or medical oncologists were recruited to participate in a two-phase national educational study focused on CML, ALL, and B-cell lymphomas. Institutional Review Board approvals were obtained to ensure informed consent, confidentiality of participants, and ethically acceptable level of compensation. Eligible participants had to be actively practicing physicians in the United States with a caseload of ≥2 patients/year with CML, ALL or B-cell lymphomas and ≥10 patients/year for all 3 conditions combined. Eligible participants who had fully completed either the qualitative interview and/or the quantitative online survey were included in the analyses (n = 148). In the initial qualitative exploratory phase of the study, participants (n = 27) were asked to complete a brief online case-based survey and complete a 45-minute telephone interview focusing on the personal, contextual, and behavioral factors that influence a provider’s clinical reasoning process in diagnosis and treatment. Selected interviews were transcribed and analyzed through thematic analysis. Findings from this initial phase informed the second, quantitative confirmatory phase of the study. In this phase, eligible participants (n = 121) completed an online survey composed of specific multiple choice questions, semantic differential rating scales, and case vignettes. Respondents’ answers to each of the questions in the quantitative survey were compared with optimal answers, as identified by treatment guidelines and faculty experts. Results A group of 9 core practice gaps were identified through combined analysis of data from the online surveys and in-depth interviews. Of note, 33% of participants agreed with evidence-based expert opinion that early molecular responses to tyrosine kinase inhibitor (TKI) therapy correlate with long-term clinical outcomes for patients with chronic phase (CP) CML. Likewise, only (38%) of participants agreed with the expert faculty that achieving a major molecular response to TKI therapy substantially decreases the patient’s risk of disease progression. A minority of participants’ practice (22%) matched expert recommendations with regard to timing and frequency of cytogenetic analysis by bone marrow biopsy to assess patient response to first-line TKI therapy for CP CML. Finally, fewer than 30% of study participants knew the mechanisms of action of promising agents in phase III clinical trials, including blinatumomab (26%), fostamatinib (18%), idelalisib (22%), inotuzumab ozogamicin (27%), and obinutuzumab (20%). Detailed results from the study will be presented, including group-specific analyses and investigation of the causalities of each of the practice gaps identified. Conclusions The results of this study suggest that a significant proportion of US hematology/oncology specialists are not applying optimal care for patients with CML, AML, and B-cell malignancies. Most notably, study participants did not adequately recognize that early molecular response to TKI therapy is significantly associated with long-term survival outcomes, which could impact clinical decisions for patients with chronic phase CML. The overuse of bone marrow cytogenetic analysis by community oncologists could impact the quality of life of patients with CML. Lack of familiarity with mechanisms of action of new agents under investigation in hematologic malignancies may lead to missed opportunities to enroll eligible patients on clinical trials, and could potentially delay integration of emerging clinical trial data and agents with new indications into clinical practice. These findings provide evidence to support the design of clinical tools, educational programs, and performance improvement interventions. Disclosures: No relevant conflicts of interest to declare.
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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 | 0,022 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».