Barbara N Harding1, James S Floyd2, Jeffrey F Scherrer3, Joanne Salas3, John E Morley4, Susan A Farr5, Sascha Dublin6. 1. Department of Medicine, University of Washington, 1959 North East Pacific Street, Seattle, WA, 98195, USA; Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA, 98101, USA. Electronic address: hardingb@uw.edu. 2. Department of Medicine, University of Washington, 1959 North East Pacific Street, Seattle, WA, 98195, USA; Department of Epidemiology, University of Washington, 1959 North East Pacific Street, Seattle, WA, 98195, USA; Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Seattle, WA, 98195, USA. 3. Department of Family and Community Medicine, Saint Louis University School of Medicine, 1402 South Grand Blvd, St. Louis, MO, 63104, USA; Harry S. Truman Veterans Administration Medical Center, Research Service, 800 Hospital Drive, Columbia, MO, 65201, USA. 4. Division of Geriatric Medicine, Saint Louis University School of Medicine, 1402 South Grand Blvd, St. Louis, MO, 63104, USA. 5. Division of Geriatric Medicine, Saint Louis University School of Medicine, 1402 South Grand Blvd, St. Louis, MO, 63104, USA; Saint Louis Veterans Affairs Medical Center, Research Service, John Cochran Division, 915 North Grand Blvd, St. Louis, MO, 63106, USA. 6. Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Seattle, WA, 98101, USA; Department of Epidemiology, University of Washington, 1959 North East Pacific Street, Seattle, WA, 98195, USA.
Abstract
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: 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: 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: Although cognitive test results were rarely available, agreement was good with algorithms requiring ≥2 dementia diagnosis codes, supporting the accuracy of this algorithm. IMPLICATIONS: These scores may add value in identifying dementia cases for EHR-based research studies.
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: 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: 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: Although cognitive test results were rarely available, agreement was good with algorithms requiring ≥2 dementia diagnosis codes, supporting the accuracy of this algorithm. IMPLICATIONS: These scores may add value in identifying dementia cases for EHR-based research studies.
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