Modeling costs and outcomes associated with a treatment algorithm for problem bleeding episodes in patients with severe hemophilia a and high-titer inhibitors.
Bibliographic record
Abstract
BACKGROUND: No evidence-based treatment guidelines are currently available for the treatment of problem bleedings in patients with hemophilia who develop clotting factor inhibitors. A treatment algorithm was developed previously to help providers optimize the approach to the treatment of this patient population. The algorithm provides the specific intervals between treatments; however, it does not specify dosing recommendations and does not offer insights into the likelihood of outcome improvements at each time interval. OBJECTIVE: To develop a model to analyze the impact on patient outcomes and costs of adhering to a current treatment algorithm for the 2 available clotting therapies to treat bleeding episodes in patients with hemophilia who develop clotting factor inhibitors. METHODS: A simulation model was developed using a modified Delphi method approach based on a consensus opinion of an expert panel. The model was used to analyze the impact of following the available treatment algorithm on patient outcomes and costs. Treatment patterns and the likelihood of a resolved bleeding episode associated with following the treatment algorithm (ie, adherence) were compared with not following the algorithm (ie, nonadherence). This model assumed 2 scenarios in which treatment was initiated with each of the 2 bypassing agents currently available, and clinical and economic outcomes were mapped for adhering to and not adhering to the consensus treatment algorithm. RESULTS: The simulation model shows that adhering to the treatment algorithm would result in 74.4% of patients improving at 72 hours compared with only 56.7% of patients when not adhering to the algorithm. According to this model, regardless of the bypassing agent used at initiation, adherence to the treatment algorithm would result in fewer patients requiring combined sequential therapy with the 2 bypassing agents for 3 days. In addition, using this analytic model, reducing the percentage of patients with hemophilia who required combined sequential therapy by 17.6% resulted in an average cost-savings of $16,305 per patient. CONCLUSION: Adherence to an algorithm in which treatment is altered at regular intervals based on a patient's clinical response has the potential to improve patient outcomes and reduce the number of nonresponsive patients requiring sequential therapy in patients with hemophilia who have clotting factor inhibitors and are experiencing problem bleeding episodes. >Adherence to the algorithm would also result in reduced costs to patients and payers.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".