Physician Factors Determining Treatment Decisions in Selecting Second Line Agents for Pediatric ITP
Notice bibliographique
Résumé
Abstract Background: Decision-making in selecting second-line therapies for pediatric patients with immune thrombocytopenia (ITP) has not been studied. Using data collected from physicians experienced in treating ITP, we developed a conceptual model of factors that informed treatment choice. This study was a component of ICON1, a prospective, observational, longitudinal cohort study of second line treatments for childhood ITP performed by the Pediatric ITP Consortium of North America (ICON). Methods: Physicians who enrolled patients in ICON1 were asked to rank the top three reasons they chose a specific treatment so as to weight each factor. Those choices that were ranked 1, 2, or 3 were weighted to develop a propensity scored decisional model. In other questions, physicians were asked about all factors that may have influenced decisions to begin a particular second line therapy and this data was then used to examine additional factors that influenced therapy choices and to compare between therapies. Results: ICON1 enrolled 118 patients; 101 had primary ITP and 53 were receiving their first second line therapy. The majority of physicians in the study saw eleven or more patients per year, and the time since completion of fellowship ranged from 1 to 44 years (mean 12.5 (SD 11)). The most important factors guiding treatment decisions in propensity weighted modeling were "patient preference factors": patient/parental preference (40%), and treatment-related factors: possibility of remission (38%), side effect profile (36%), efficacy (27%), long-term toxicity (33%), and ease of administration (30%). Physician factors, such as experience and adhering to published guidelines, rarely influenced decision-making with only 2% of physicians giving published guidelines as a reason for choice of therapy, and there being no difference in choice based on years since fellowship or experience in treating ITP patients. However, 28% of physicians stated their comfort level with a treatment strongly influenced their choice. Additionally, 38% of physicians did not endorse any patient clinical factors (e.g. frequency of bleeding, expected compliance, response to other therapies, age, comorbidities) as key in their decision-making. Health system factors, such as insurance approval or distance from the closest medical center, rarely influenced treatment choice. Treatments could be categorized into five groups: oral immunosuppressive agents, rituximab, romiplostim, eltrombopag or splenectomy. A significant determinant of choosing splenectomy or rituximab was the "possibility of long-term remission" (p<0.001). A high percentage of treatment factors impacted the decision to prescribe: for rituximab 92% of physicians endorsed at least one treatment factor; for oral immunosuppressants, and romiplostim and eltrombopag, 100% of physicians endorsed at least one treatment factor. Among the top 3 choices for each medication, treatment related factors were major determinants (Table). Oral agents, were significantly more likely to be chosen for ease of administration and expected adherence (p<0.001). When examining the reasons physicians chose particular therapies, physicians indicated "this agent is most efficacious" most frequently for romiplostim, but not for eltrombopag (p<0.001) and were more likely to choose rituximab in patients in whom there was lower expected compliance (p=0.017). Conclusions: This first analysis of physician decision making regarding second line therapies shows that patient preference and physician perception of treatment characteristics (efficacy, side effects, possibility of long term remission) are primary drivers of physician choice in contrast to guidelines, clinical characteristics and health system factors. Future comparative effectiveness studies are necessary to better inform patient and physician choice. Disclosures Lambert: Novartis: Consultancy. Grace:Agios Pharmaceuticals: Other: Scientific Advisor, Research Funding. Bussel:Cangene: Research Funding; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Ligand: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sysmex: Research Funding; Symphogen: Membership on an entity's Board of Directors or advisory committees; BiologicTx: Research Funding; Shionogi: Membership on an entity's Board of Directors or advisory committees; Physicians Education Resource: Speakers Bureau; Protalex: Membership on an entity's Board of Directors or advisory committees, Research Funding; Immunomedics: Research Funding; Rigel Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Boehringer Ingelheim: Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; UpToDate: Patents & Royalties; Momenta Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Prophylix Pharma: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genzyme: Research Funding; Eisai: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Haley:Baxalta: Membership on an entity's Board of Directors or advisory committees; CSL Behring: Honoraria. Neufeld:Novartis: Consultancy.
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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 | 0,005 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».