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Predicting the risk of incident dementia in older adults: The ADNI‐dementia risk score
Author(s) -
Ezzati Ali,
Thompson Paul M,
Davatzikos Christos,
Harvey Danielle J,
Lipton Richard B
Publication year - 2021
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.055466
Subject(s) - dementia , geriatric depression scale , medicine , hazard ratio , proportional hazards model , demographics , marital status , gerontology , cognition , psychiatry , disease , demography , population , confidence interval , depressive symptoms , environmental health , sociology
Background There are many risk factors associated with incident dementia in older adults, however predicting the timeframe of progression to dementia is difficult. We aimed to develop a novel, machine learning‐derived model to predict the risk of incident dementia among non‐demented older adults. Method Using data from 1837 non‐demented participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we used random survival forest (RSF) methods, a nonparametric decision tree machine learning approach, to identify predictors of incident dementia. Three sets of features were used: Model 1: Demographics (age, sex, race/ethnicity, years of education, BMI, marital status); Model 2: Demographics + Neuropsychological test scores and depression (NP; Alzheimer's Disease Assessment Scale – Cognitive (ADAS‐cog), Mini‐Mental State Examination (MMSE), geriatric depression scale (GDS)); and Model 3: Demographics + NP + Biomarkers (APOE4 status, hippocampal volume, global β‐amyloid PET‐SUVR). An integer‐based score was developed to estimate the 10‐year risk of incident dementia using regression coefficients from Cox Proportional Hazard models. Result Over up to 10 years of follow up (mean = 3.19), 377 participants (20.5%) developed incident dementia. The RSF models 2 and 3 demonstrated good discrimination (C‐index for Model 1, 0.58 [95% CI 0.53–0.59]; model 2, 0.68 [95% CI 0.65–0.71]; and model 3, 0.66 [0.62–0.69]. Performance of RSF models was similar to Cox‐based models, which used the same features. From the identified top‐performing predictors (listed in figure 1), an integer‐based risk score for 10‐year dementia incidence was created: ADNI‐Dementia risk score 1, 2, and 3. The cumulative 10‐year incidence of dementia increased in a graded fashion from quartile 1 to quartile 4 for all risk scores (Figure 2). Risk score 3, which included biomarkers, was the most specific model for detecting individuals unlikely to progress to dementia. Conclusion We developed a new risk score that integrates readily available demographic, clinical, and biomarker data to predict the risk of dementia among non‐demented older adults. Using this simple tool is practical in clinical setting.