Prediction of Cognitive Decline for Enrichment of Alzheimer's Disease Clinical Trials
Notice bibliographique
Résumé
BACKGROUND: A key issue to Alzheimer's disease clinical trial failures is poor participant selection. Participants have heterogeneous cognitive trajectories and many do not decline during trials, which reduces a study's power to detect treatment effects. Trials need enrichment strategies to enroll individuals who are more likely to decline. OBJECTIVES: To develop machine learning models to predict cognitive trajectories in participants with early Alzheimer's disease and presymptomatic individuals over 24 and 48 months respectively. DESIGN: Prognostic machine learning models were trained from a combination of demographics, cognitive tests, APOE genotype, and brain imaging data. SETTING: Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), National Alzheimer's Coordinating Center (NACC), Open Access Series of Imaging Studies (OASIS-3), PharmaCog, and a Phase 3 clinical trial in early Alzheimer's disease were used for this study. PARTICIPANTS: A total of 2098 participants who had demographics, cognitive tests, APOE genotype, and brain imaging data, as well as follow-up visits for 24-48 months were included. MEASUREMENTS: Baseline magnetic resonance imaging, cognitive tests, demographics, and APOE genotype were used to separate decliners, defined as individuals whose CDR-Sum of Boxes scores increased during a predefined time window, from stable individuals. A prognostic model to predict decline at 24 months in early Alzheimer's disease was trained on 1151 individuals who had baseline diagnoses of mild cognitive impairment and Alzheimer's dementia from ADNI and NACC. This model was validated on 115 individuals from a placebo arm of a Phase 3 clinical trial and 76 individuals from the PharmaCog dataset. A second prognostic model to predict decline at 48 months in presymptomatic populations was trained on 628 individuals from ADNI and NACC who were cognitively unimpaired at baseline. This model was validated on 128 individuals from OASIS-3. RESULTS: The models achieved up to 79% area under the curve (cross-validated and out-of-sample). Power analyses showed that using prognostic models to recruit enriched cohorts of predicted decliners can reduce clinical trial sample sizes by as much as 51% while maintaining the same detection power. CONCLUSIONS: Prognostic tools for predicting cognitive decline and enriching clinical trials with participants at the highest risk of decline can improve trial quality, derisk endpoint failures, and accelerate therapeutic development in Alzheimer's disease.
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 | 0,019 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».