ArticleFrontiers in neurology2025
Predictive regression models for cognitive impairment, dementia, and Alzheimer's disease using real-world electronic health records.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The aim of this non-interventional, case-control pilot study was to identify factors associated with cognitive impairment, dementia, and Alzheimer's disease (AD) using a real-world dataset from Quirónsaludmadrid's database. Based on Global Deterioration Scale score, 4 models of regression aimed to predict cognitive impairment and dementia (model 1), mild cognitive impairment (MCI, model 2), AD (model 3) and progression (model 4) were created. Age [odds ratio (OR) = 1.721], apathy (OR = 34.952), anxiety (OR = 0.223) and higher education (OR = 0.026) were associated with model 1 with an area under the curve (AUC) of 0.796 and a sensitivity of 0.60 and specificity of 0.86. For model 2, the selected variables were: age (OR = 1.222), apathy (OR = 2.650), depression (OR = 0.318) and higher education (OR = 0.232) with an AUC of 0.657 and a sensitivity of 0.82 and specificity of 0.45. For model 3, variables included were age (OR = 1.490), first-degree family history (OR = 4.147), apathy (OR = 8.247), anxiety (OR = 0.302), and higher education (OR = 0.119) with an AUC of 0.852 and a sensitivity of 0.84 and specificity of 0.73. Model 4 had an AUC of 0.532 and a sensitivity of 0.59 and specificity of 0.65. In conclusion, age and apathy were risk factors for the development of cognitive impairment, MCI and AD, while high education level was a protective factor in the three main models. Family history of dementia was a risk factor for developing AD. Models 3 and 1 had the best selection capacity and could be recommended to predict the diagnosis of AD and cognitive impairment and dementia in individuals with suspicious symptoms or presymptomatic.
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