Physician Factors Determining Treatment Decisions in Selecting Second Line Agents for Pediatric ITP
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".