DEVELOPMENT OF A NOVEL FRAMEWORK TO INFORM COST-EFFECTIVENESS MODELS IN SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
PV094 / #450 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose Cost-effectiveness models (CEMs) play a significant role in health technology assessments (HTAs). However, existing CEMs for systemic lupus erythematosus (SLE) are often complex and have limited face validity. Evolving clinical practices and new therapeutic regimens with different modes of action necessitate profound changes to prior publicly available models in SLE and the development of a new conceptual modeling framework.[1-5] Methods A targeted literature review was conducted to identify treatment guidelines, standard of care, disease progression, outcome measures, drivers of health-related quality of life (HRQoL), costs, and existing CEMs in SLE. Following this, 1-on-1 virtual interviews were conducted with 3 international expert rheumatologists specializing in treating patients with SLE, to validate critical clinical components and their relationships identified in the literature review for use in the CEM. Additionally, 2 rounds of virtual advisory board meetings were convened with the same rheumatologists, 2 HTA experts, and 2 SLE patients to reach consensus on key assumptions and the conceptual model structure. Results Seven key components were identified as important for developing a CEM for SLE: disease activity, organ damage, flares and remission, oral glucocorticoid (GC) use, mortality, HRQoL, and healthcare resource use. An individual patient simulation approach was deemed necessary to reflect disease heterogeneity in patient disease trajectories and to facilitate interactions between different components of the disease process, such as disease activity, oral GC dosage and organ damage. Existing individual simulation models have used a regression equation to reflect disease activity over time that poorly captures patient heterogeneity. This new model structure categorizes disease activity into discrete levels (3-5 levels, including remission and low disease activity state). Existing models simulated organ damage by organ class, which adds complexity but poorly captures treatment benefits in slowing organ damage. The new model structure simplifies organ damage progression into 6 levels. Furthermore, severe flares are captured in this model, simulated as a function of disease activity, and influence the risk of organ damage (Figure 1). The face validity was confirmed by clinical, HTA and patient experts. The inclusion of distinct clinical states such as remission, low disease activity and severe flare are an important improvement of this new model framework as they are considered relevant to clinical practice and HTA decision making. Figure 1: Proposed SLE cost-effectiveness model structure GC, glucocorticoid; SDI, Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index; SLE, systemic lupus erythematosus. Conclusions The new SLE CEM framework better reflects the impact of treatment on patient disease trajectories to facilitate improved technology assessment. This CEM concept can be utilized to assess the value of investigational treatments for SLE in the future, ensuring that they adequately address key unmet needs in SLE. References: [1.] Pierotti F. PLoS ONE 2015;10(10):e0140843. [2.] Ottawa ON. Canadian Agency for Drugs and Technologies in Health 2020. Pharmacoeconomic Review. [3.] Ottawa ON. Canadian Agency for Drugs and Technologies in Health 2023. Pharmacoeconomic Review. [4.] National Institute for Health and Care Excellence. TA397; 2016 Jun. [5.] van Oostrum I. Value in Health 2016;19(7):A374. Funding: The healthcare business of Merck KGaA, Darmstadt, Germany (CrossRef Funder ID: 10.13039/100009945) funded the study, which was conducted by Source Health Economics, and editorial support by Bioscript Group.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".