OP30 Impact Of Patient Reported Outcomes Data On Health Technology Assessments Of Acute Myeloid Leukemia
Bibliographic record
Abstract
Introduction Patient-reported outcomes (PRO) data are important in understanding patients’ experience of disease and treatment; however, PRO data are not universally collected or consistently included as part of a Health Technology Assessment (HTA) submission. Additionally, the HTA bodies’ response to PRO data vary, making the impact unclear. To understand the impact of PRO data on reimbursement decisions for Acute Myeloid Leukemia (AML) indications, an in-depth analysis of HTA bodies’ appraisals of AML and analogous indications was conducted. Methods This analysis was conducted using IQVIA's HTA Accelerator, which contains HTA appraisals from ≥100 HTA agencies in thirty-nine countries. Included in the analysis were single-technology assessments (original submissions, resubmissions, extensions of original indications, and renewals); relevant regulatory approvals and pivotal trials were also analyzed. Results Of the 185 AML appraisals from sixteen HTA bodies, 66 (36%) included PRO data. Within these, thirteen different PRO instruments were identified, none of which have been validated in patients with AML. For seven of twenty in-scope products, PRO evidence positively impacted ≥1 of the HTA decisions. Although the same HTA bodies (i.e., Scottish Medicines Consortium, pan-Canadian Oncology Drug Review, and the National Institute of Health and Care Excellence) generally accepted the PRO evidence, others were critical of the evidence (i.e., Haute Autorité de Santé and the Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen). The most common concerns raised by the HTA bodies regarding the PRO evidence included trial design and low patient response rate. Conclusions Of the products that included PRO evidence in their HTA submissions, 35% received positive feedback from ≥1 HTA body on their submitted PRO evidence. Attention to PRO data collection is key to demonstrate the value of AML products to HTA bodies. Without these data, a clear gap in the understanding of patients’ experience is evident.
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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 | 0.241 | 0.537 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".