Estimated Impact of Deemed Consent Legislation for Organ Donation on Individuals With Kidney Failure: A Dynamic Decision Analytic Model
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Résumé
Background:There is little data modeling the impact of deemed consent legislation (eligible individuals who do not register their decision to decline to be a donor are presumed to consent after death) on outcomes for individuals with kidney failure.Objective:To estimate the change in life-years (LYs) and quality-adjusted life-years (QALYs) resulting from different changes in the rate of deceased donor kidney transplantation associated with deemed consent legislation and health system transformation.Design:Dynamic Decision Analytic Model.Setting:This modeling study included kidney failure patients in Atlantic Canada (all of whom receive their kidney transplants in Halifax, Nova Scotia). The adoption of deemed consent legislation was the intervention, and opt-in (the status quo) was the reference comparator.Patients:Prevalent kidney failure patients at the end of 2019 in all of Atlantic Canada (N = 3615) served as the starting population.Methods:We compared expected outcomes between the intervention and comparator. Changes in QALYs and total LYs were modeled under different changes to the proportion of patients receiving a deceased donor kidney transplant (from –10% to 20%) resulting from deemed consent relative to the status quo. Changes in QALYs and LYs were reported for 3 different time horizons (5, 10, and 30 years). Uncertainty around QALYs and total LYs was reported using 95% confidence intervals (CIs) constructed from a probabilistic sensitivity analysis using 1000 Monte Carlo Simulations.Results:The increase in QALYs ranged from 7 QALYs (95% CI: 5-10) with a 5% increase using a 5-year time frame to 882 QALYs (95% CI: 619-1144) with a 20% increase over a 30-year time frame. Parallel changes in total LYs were also observed. In contrast, decreases in deceased donor kidney transplantation resulted in a loss of QALYs (for example, –463 QALYs; 95% CI: –633 to –306 for a 10% decrease over a 30-year time frame). Using the most optimistic scenario (a 20% increase), there was an 18% increase in the cumulative number of deceased donor kidney transplant recipients over a 30-year observation period.Limitations:The results are subject to uncertainty depending on changes to the dialysis or transplant population that were not modeled and that may not be fully captured with probabilistic sensitivity analysis.Conclusions:Deemed consent legislation will lead to variable changes in QALYs and total LYs for the kidney failure population, depending on the degree to which deceased donor transplantation rates change and the time horizon of observation. This modeling study may serve as a baseline to monitor the future impact of deemed consent legislation.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,281 | 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 découle