Predictive regression models for cognitive impairment, dementia, and Alzheimer’s disease using real-world electronic health records

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.