ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2022
Use of blood pressure measurements extracted from the electronic health record in predicting Alzheimer's disease: A retrospective cohort study at two medical centers.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- The age-specific comorbidity burden of mild cognitive impairment: a US claims database study.Alzheimer's research & therapy · 2023Article
- Dementia and electronic health record phenotypes: a scoping review of available phenotypes and opportunities for future research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Use of blood pressure measurements extracted from the electronic health record in predicting Alzheimer's disease: A retrospective cohort study at two medical centers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review.Dementia and geriatric cognitive disorders extraReview
- Clinical factors predicting the rate of cognitive decline in a US memory clinic: An electronic health record study.Alzheimer's & dementia (New York, N. Y.)Article
- Estimated Effects of Comorbidities on Risk of All-cause Dementia in Patients with Mild Cognitive Impairment.Sage open agingArticle
- Predicting 5-year dementia conversion in veterans with mild cognitive impairment.Alzheimer's & dementia (Amsterdam, Netherlands)Article
- Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
introductionStudies investigating the relationship between blood pressure (BP) measurements from electronic health records (EHRs) and Alzheimer's disease (AD) rely on summary statistics, like BP variability, and have only been validated at a single institution. We hypothesize that leveraging BP trajectories can accurately estimate AD risk across different populations.
methodsIn a retrospective cohort study, EHR data from Veterans Affairs (VA) patients were used to train and internally validate a machine learning model to predict AD onset within 5 years. External validation was conducted on patients from Michigan Medicine (MM).
resultsThe VA and MM cohorts included 6860 and 1201 patients, respectively. Model performance using BP trajectories was modest but comparable (area under the receiver operating characteristic curve [AUROC] = 0.64 [95% confidence interval (CI) = 0.54-0.73] for VA vs. AUROC = 0.66 [95% CI = 0.55-0.76] for MM).
conclusionApproaches that directly leverage BP trajectories from EHR data could aid in AD risk stratification across institutions.
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Registered trials
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