Evidence map›Paper›PMID 37252836›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2023

Dementia and electronic health record phenotypes: a scoping review of available phenotypes and opportunities for future research.

Anne M Walling, Joshua Pevnick, Antonia V Bennett, V G Vinod Vydiswaran, Christine S Ritchie

Abstract readScoping Review
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Validating ICD code case definitions for condition case ascertainment in multimorbidity measurement: a retrospective chart review.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Detection of emergency department patients at risk of dementia through artificial intelligence.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Observational
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Paving the way for Alzheimer's disease blood-based biomarkers in primary care.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Anne M WallingDepartment of Medicine, VA Greater Los Angeles Health System, Los Angeles, California, USA.
Joshua PevnickDepartment of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Antonia V BennettDepartment of Health Policy and Management, University of North Carolina, Chapel Hill, North Carolina, USA.
V G Vinod VydiswaranDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0002-3122-1936
Christine S RitchieDepartment of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

Funding

Training Core (I)U54AG063546 · NIA · BROWN UNIVERSITY · PI JOSEPH E. GAUGLER · 2019 to 2026
$125.9M
NIA NIH HHS U54 AG063546
6 · The paper itself

Abstract

objectiveWe performed a scoping review of algorithms using electronic health record (EHR) data to identify patients with Alzheimer's disease and related dementias (ADRD), to advance their use in research and clinical care. MATERIALS AND

methodsStarting with a previous scoping review of EHR phenotypes, we performed a cumulative update (April 2020 through March 1, 2023) using Pubmed, PheKB, and expert review with exclusive focus on ADRD identification. We included algorithms using EHR data alone or in combination with non-EHR data and characterized whether they identified patients at high risk of or with a current diagnosis of ADRD.

resultsFor our cumulative focused update, we reviewed 271 titles meeting our search criteria, 49 abstracts, and 26 full text papers. We identified 8 articles from the original systematic review, 8 from our new search, and 4 recommended by an expert. We identified 20 papers describing 19 unique EHR phenotypes for ADRD: 7 algorithms identifying patients with diagnosed dementia and 12 algorithms identifying patients at high risk of dementia that prioritize sensitivity over specificity. Reference standards range from only using other EHR data to in-person cognitive screening.

conclusionA variety of EHR-based phenotypes are available for use in identifying populations with or at high-risk of developing ADRD. This review provides comparative detail to aid in choosing the best algorithm for research, clinical care, and population health projects based on the use case and available data. Future research may further improve the design and use of algorithms by considering EHR data provenance.

Indexed as

Alzheimer DiseaseElectronic Health RecordsHumansPhenotypeSensitivity and Specificitydementiaelectronic health record phenotype

Identifiers

PMID37252836
PMCPMC10280354

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.