ArticleJournal of the American Geriatrics Society2026
Assigning Probable Dementia Status Using Routinely Collected Electronic Health Record Data.
Article in Journal of the American Geriatrics Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Assigning Probable Dementia Status Using Routinely Collected Electronic Health Record Data.Journal of the American Geriatrics Society · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
introductionMore than half of older adults with Alzheimer's Disease and Related Dementias (ADRD) are undiagnosed, limiting timely access to person-centered care. Therefore, clinicians, researchers, and population health managers need scalable, reproducible approaches to monitor both prevalence and diagnostic gaps. We evaluated whether a decision-analytic modeling framework can translate a limited number of clinician-adjudicated cases of ADRD into a probabilistic computational phenotype for accurate, population-level assignments of probable ADRD in the emergency department (ED) setting using routinely collected electronic health record (EHR) data.
methodsRetrospective cohort study of 5000 adults aged ≥ 65 years from nine EDs within a large integrated health system (2014-2022). We randomly selected 500 individuals for clinician adjudication of dementia status (reference cohort), reserving the remaining 4500 as a phenotyping cohort. We developed the phenotype as a logistic regression model trained on adjudicated cases, embedding pattern-mixture multiple imputation to address information bias. We applied decision-curve analysis to evaluate clinical utility across probabilistic thresholds. We applied the phenotype to assign dementia status to 4500 unadjudicated patients and compared clinical characteristics to adjudicated cases.
resultsThe mean (SD) age was 77.4 (9.0) years; 55.4% were women; 102 individuals (20.4%) had clinician-adjudicated ADRD. The model demonstrated good discrimination (AUROC 0.87; 95% CI 0.82-0.91). Decision-curve analysis revealed net clinical benefit across examined thresholds (predicted probabilities 12%-32%), identifying an additional 16-18 probable ADRD cases per 100 older adults. Among those without ADRD-related diagnosis codes, net benefit ranged from 8 to 13 additional correct identifications per 100. Phenotype-assigned cases closely resembled clinician-adjudicated cases (standardized mean differences ≤ 0.20).
conclusionsA probabilistic computational phenotype derived from routinely collected EHR data accurately reproduced clinician-adjudicated ADRD status and demonstrated net clinical benefit, including among ED patients whose ADRD was not captured by diagnosis codes. Adoption of this replicable framework may enable healthcare organizations to strengthen ADRD surveillance and reduce underdiagnosis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.