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Investigating predictive models for earlier diagnosis of cognitive impairment using multimodal eye biomarkers
Author(s) -
DeBuc Delia Cabrera,
Arthur Edmund,
Feuer William,
Persad Patrice,
Somfai Gabor Mark,
Kostic Maja,
Oropesa Susel,
MendozaSantiesteban Carlos
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.040000
Subject(s) - receiver operating characteristic , logistic regression , statistics , youden's j statistic , psychology , artificial intelligence , medicine , mathematics , computer science
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.