Cost-Effectiveness Analysis of Using Loss of Heterozygosity to Manage Premalignant Oral Dysplasia in British Columbia, Canada
Notice bibliographique
Résumé
BACKGROUND: Management of low-grade oral dysplasias (LGDs) is complicated, as only a small percentage of lesions will progress to invasive disease. The current standard of care requires patients to undergo regular monitoring of their lesions, with intervention occurring as a response to meaningful clinical changes. Recent improvements in molecular technologies and understanding of the biology of LGDs may allow clinicians to manage lesions based on their genome-guided risk. METHODS: We used a decision-analytic Markov model to estimate the cost-effectiveness of risk-stratified care using a genomic assay. In the experimental arm, patients with LGDs were managed according to their risk profile using the assay, with low- and intermediate-risk patients given longer screening intervals and high-risk patients immediately treated with surgery. Patients in the comparator arm had standard care (biannual follow-up appointments at an oral cancer clinic). Incremental costs and outcomes in life-years gained (LYG) and quality-adjusted life-years (QALY) were calculated based on the results in each arm. RESULTS: The mean cost of assay-guided management was $8,123 (95% confidence interval [CI] $2,973 to $23,062 in 2013 Canadian dollars) less than the cost of standard care. This difference was driven largely by reductions in resource use among people who did not develop cancer. Mean incremental effectiveness was 0.18 LYG (95% CI 0.08 to 0.39) or 0.64 QALY (95% CI 0.46 to 0.89). Sensitivity analysis suggests that these findings are robust to both expected and extreme variation in all parameter values. CONCLUSION: Use of the assay-guided management strategy costs less and is more effective than standard management of LGDs. IMPLICATIONS FOR PRACTICE: The findings of this study strongly suggest that the use of a risk-stratification method such as a genomic assay can result in improved quality-adjusted survival outcomes for patients with low-grade oral dysplasia (LGD). The use of such an assay in this study provides "precision medicine," allowing for a change in follow-up frequency or early intervention as compared with current standard care. As genomic technologies become more common in cancer care, it is hoped that such an assay, once validated, will become part of a new model for the standard management of LGDs in similar health systems.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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) | 0,001 | 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».