Methods to identify dementia in the electronic health record: Comparing cognitive test scores with dementia algorithms.
Aged
Aged, 80 and over
Algorithms
Cognition
Dementia
/ diagnosis
Electronic Health Records
/ standards
Female
Humans
Male
Mass Screening
/ instrumentation
Mental Status and Dementia Tests
/ statistics & numerical data
Middle Aged
Retrospective Studies
United States
/ epidemiology
United States Department of Veterans Affairs
/ organization & administration
Algorithms
Cognitive screening
Dementia
Electronic health record
Journal
Healthcare (Amsterdam, Netherlands)
ISSN: 2213-0772
Titre abrégé: Healthc (Amst)
Pays: Netherlands
ID NLM: 101622189
Informations de publication
Date de publication:
Jun 2020
Jun 2020
Historique:
received:
26
11
2019
revised:
27
03
2020
accepted:
27
04
2020
entrez:
20
6
2020
pubmed:
20
6
2020
medline:
15
12
2020
Statut:
ppublish
Résumé
Epidemiologic studies often use diagnosis codes to identify dementia outcomes. It remains unknown to what extent cognitive screening test results add value in identifying dementia cases in big data studies leveraging electronic health record (EHR) data. We examined test scores from EHR data and compared results with dementia algorithms. This retrospective cohort study included patients 60+ years of age from Kaiser Permanente Washington (KPWA) during 2013-2018 and the Veterans Health Affairs (VHA) during 2012-2015. Results from the Mini Mental State Examination (MMSE) and the Saint Louis University Mental Status Examination (SLUMS) cognitive screening exams, were classified as showing dementia or not. Multiple dementia algorithms were created using combinations of diagnosis codes, pharmacy records, and specialty care visits. Correlations between test scores and algorithms were assessed. 3,690 of 112,917 KPWA patients and 2,981 of 102,981 VHA patients had cognitive test results in the EHR. In KPWA, dementia prevalence ranged from 6.4%-8.1% depending on the algorithm used and in the VHA, 8.9%-12.1%. The algorithm which best agreed with test scores required ≥2 dementia diagnosis codes in 12 months; at KPWA, 14.8% of people meeting this algorithm had an MMSE score, of whom 65% had a score indicating dementia. Within VHA, those figures were 6.2% and 77% respectively. Although cognitive test results were rarely available, agreement was good with algorithms requiring ≥2 dementia diagnosis codes, supporting the accuracy of this algorithm. These scores may add value in identifying dementia cases for EHR-based research studies.
Sections du résumé
BACKGROUND
BACKGROUND
Epidemiologic studies often use diagnosis codes to identify dementia outcomes. It remains unknown to what extent cognitive screening test results add value in identifying dementia cases in big data studies leveraging electronic health record (EHR) data. We examined test scores from EHR data and compared results with dementia algorithms.
METHODS
METHODS
This retrospective cohort study included patients 60+ years of age from Kaiser Permanente Washington (KPWA) during 2013-2018 and the Veterans Health Affairs (VHA) during 2012-2015. Results from the Mini Mental State Examination (MMSE) and the Saint Louis University Mental Status Examination (SLUMS) cognitive screening exams, were classified as showing dementia or not. Multiple dementia algorithms were created using combinations of diagnosis codes, pharmacy records, and specialty care visits. Correlations between test scores and algorithms were assessed.
RESULTS
RESULTS
3,690 of 112,917 KPWA patients and 2,981 of 102,981 VHA patients had cognitive test results in the EHR. In KPWA, dementia prevalence ranged from 6.4%-8.1% depending on the algorithm used and in the VHA, 8.9%-12.1%. The algorithm which best agreed with test scores required ≥2 dementia diagnosis codes in 12 months; at KPWA, 14.8% of people meeting this algorithm had an MMSE score, of whom 65% had a score indicating dementia. Within VHA, those figures were 6.2% and 77% respectively.
CONCLUSIONS
CONCLUSIONS
Although cognitive test results were rarely available, agreement was good with algorithms requiring ≥2 dementia diagnosis codes, supporting the accuracy of this algorithm.
IMPLICATIONS
CONCLUSIONS
These scores may add value in identifying dementia cases for EHR-based research studies.
Identifiants
pubmed: 32553526
pii: S2213-0764(20)30029-4
doi: 10.1016/j.hjdsi.2020.100430
pmc: PMC7363308
mid: NIHMS1596980
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
100430Subventions
Organisme : NIA NIH HHS
ID : R21 AG055604
Pays : United States
Organisme : HSRD VA
ID : SDR 02-237
Pays : United States
Organisme : NHLBI NIH HHS
ID : T32 HL007828
Pays : United States
Informations de copyright
Copyright © 2020 Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest Dr. Floyd has consulted for Shionogi Inc. Other authors have no conflicts of interest to disclose.
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