MO516A STRUCTURED EXPERT ELICITATION TO INFORM AND VALIDATE MORTALITY EXTRAPOLATIONS FOR A COST-EFFECTIVENESS ANALYSIS OF DAPAGLIFLOZIN
Notice bibliographique
Résumé
Abstract Background and Aims Elevated albuminuria in patients with chronic kidney disease (CKD) is associated with increased risks of CKD progression, cardiovascular events and all-cause death. In the DAPA-CKD study, dapagliflozin significantly reduced the risk of all-cause death in patients with elevated albuminuria compared with placebo (hazard ratio: 0.69; 95% confidence interval 0.53–0.88). To assess the cost-effectiveness of new treatments, decision makers require survival estimates over a longer period than that of a typical clinical trial, usually over a lifetime time horizon. A formal elicitation process is currently underway to obtain estimates of long-term survival of patients with albuminuric CKD from clinical experts. Their responses will be used to validate extrapolations of all-cause mortality data from DAPA-CKD, which could inform cost-effectiveness analyses for dapagliflozin. Method Targeted literature searches were conducted to collate data on all-cause mortality in patients with CKD and elevated albuminuria. Clinical trials and observational studies were included if they involved non-dialysis-dependent patients with CKD aged 18 years and over, had more than 500 participants per study arm and reported incidence of all-cause death and/or all-cause mortality/survival Kaplan–Meier (KM) curves. To estimate long-term survival, KM curves were extrapolated to 20 years by calculating standard mortality ratios (SMRs) using age- and sex-adjusted general-population lifetable data. Study and patient characteristics and mortality data from relevant studies were provided to clinical experts to inform their judgements in a formal elicitation process. After receiving training on the elicitation process, six leading disease area experts were invited to complete the elicitation survey using an Excel-based tool, which consisted of 10 calibration questions, and three questions regarding the survival of patients in the placebo arm of the DAPA-CKD study at 10 and 20 years. The elicited estimates will be weighted and aggregated using Cooke’s method. Results Literature searches identified 13 relevant articles (seven clinical trials and six observational studies), with a range of 1094 to 5674 participants. Mean age varied across studies (range: 55–70 years). Where reported, median follow-up was 9–144 months, and mean estimated glomerular filtration rate (eGFR) at baseline was 22.4–56.3 mL/min/1.73 m2. Five studies exclusively included patients with type 2 diabetes (T2D). The incidence of all-cause death was reported in nine studies and was 1.5–9.4 deaths per 100 patient-years, with the highest incidence observed in a study reporting data for patients with CKD stage 4 and 5 (8.0 and 9.4 deaths per 100 patient-years, respectively). Nine studies provided KM curves; from these, estimated survival at 2 years ranged from 86% (study population mean age 67 years, eGFR < 15 mL/min/1.73 m2) to 98% (study population mean age 58 years, mean eGFR 46.2 mL/min/1.73 m2). The SMR-extrapolated survival at 10 and 20 years was 36–80% and 2–69%, respectively. The ranges defined by the expert judgements collected to date for survival at 10 and 20 years are in line with the variability of the extrapolated KM survival curves. The elicitation process is ongoing and therefore, to avoid biasing the judgements that remain to be collected, preliminary results are not reported here. Results of the expert elicitation will be presented in full at the congress. Conclusion Initial results from the survey calibration questions suggest that the expert elicitation process provides expert judgements that are both informative and precise. The elicitation of survival estimates for patients with CKD and elevated albuminuria at 10 and 20 years will provide greater insight than extrapolated data alone, and will increase the validity of long-term survival projections for dapagliflozin cost-effectiveness analyses.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,100 | 0,241 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».