Investigating predictive models for earlier diagnosis of cognitive impairment using multimodal eye biomarkers
Notice bibliographique
Résumé
Abstract Background The purpose of this study is to investigate predictive models for earlier diagnosis of cognitive impairment (CI) using the eye. Method Prospective age‐matched subjects (n = 69, 55+ years) w/o CI and the presence of any ophthalmic history were recruited. The Montreal Cognitive Assessment scores, retinal images (EasyScan, iOptics) and full‐field electroretinogram (RETeval TM , LKC Technologies, Inc.) were obtained. The multifractal behavior in the skeletonized optic‐disc region was analyzed using the generalized dimensions (D 0 , D 1 & D 2 ) and singularity spectrum f(α) vs. α, both calculated with the ImageJ program. The lacunarity (Λ) was also calculated by measuring the gap dispersion inside each retinal image. Logistic regression was used to construct predictive models to discriminate between phenotypes obtained from individuals w/o CI. Independent variables were divided into sets (see Table 1). Then, five hierarchical set models were fitted with all independent variables forced in. For each model, predicted probabilities were used to construct Receiver Operating Characteristic (ROC) curves. In a separate analysis, all independent variables were allowed inclusion in a parsimonious forward stepwise fashion, and a ROC curve was also constructed with its model‐predicted probabilities. Efficacy of discrimination was summarized with the area under the ROC curve (AUROC) and Youden’s index. Result Of the 69 participants, 32 had CI (46%). Figures 1 and Table 1 shows that the overall predictive accuracy of the model 5 in discriminating patients with CI from cognitive healthy subjects may be better (AUROC∼0.95) than that of the other combined measurements AUROC range∼[0.73 ‐ 0.88]. In the separated analysis with all independent variables, the singularity exponent a 2 was the most significant predictor of CI. Once this was accounted for, none of the other parameters was statistically significant except Flicker IT. Therefore, a 2 and Flicker IT were included in a single model to obtain a powerful predictive index: X linear = 18.387 + (0.736) × (Flicker IT)‐(26.887) x (a 2 ) being the Predictive probability of CI = exp Xlinear /(1+exp Xlinear ). The AUROC for this predictive model was 0.897 (SE = 0.050) and was highly significant (p < 0.001). Conclusion Our results showed that the predictive model using multimodal eye biomarkers has potential to target cognitive screening toward individuals at increased risk of CI.
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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 | 0,000 | 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 | 0,000 | 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 ».