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Cited by 144Dementia and Cognitive Impairment ResearchArtificial Intelligence in HealthcarePharmaceutical Practices and Patient Outcomes

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Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data

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Walters, K., Hardoon, S., Petersen, I., Iliffe, S., Omar, R. Z., Nazareth, I., Rait, G.·2016·BMC Medicine
DOI 10.1186/s12916-016-0549-yPMC PMC4722622
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Abstract

BACKGROUND: Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. Routinely collected healthcare data have the potential to assess dementia risk without the need to collect further information. Our objective was to develop and validate a 5-year dementia risk score derived from primary healthcare data. METHODS: We used data from general practices in The Health Improvement Network (THIN) database from across the UK, randomly selecting 377 practices for a development cohort and identifying 930,395 patients aged 60-95 years without a recording of dementia, cognitive impairment or memory symptoms at baseline. We developed risk algorithm models for two age groups (60-79 and 80-95 years). An external validation was conducted by validating the model on a separate cohort of 264,224 patients from 95 randomly chosen THIN practices that did not contribute to the development cohort. Our main outcome was 5-year risk of first recorded dementia diagnosis. Potential predictors included sociodemographic, cardiovascular, lifestyle and mental health variables. RESULTS: Dementia incidence was 1.88 (95% CI, 1.83-1.93) and 16.53 (95% CI, 16.15-16.92) per 1000 PYAR for those aged 60-79 (n = 6017) and 80-95 years (n = 7104), respectively. Predictors for those aged 60-79 included age, sex, social deprivation, smoking, BMI, heavy alcohol use, anti-hypertensive drugs, diabetes, stroke/TIA, atrial fibrillation, aspirin, depression. The discrimination and calibration of the risk algorithm were good for the 60-79 years model; D statistic 2.03 (95% CI, 1.95-2.11), C index 0.84 (95% CI, 0.81-0.87), and calibration slope 0.98 (95% CI, 0.93-1.02). The algorithm had a high negative predictive value, but lower positive predictive value at most risk thresholds. Discrimination and calibration were poor for the 80-95 years model. CONCLUSIONS: Routinely collected data predicts 5-year risk of recorded diagnosis of dementia for those aged 60-79, but not those aged 80+. This algorithm can identify higher risk populations for dementia in primary care. The risk score has a high negative predictive value and may be most helpful in 'ruling out' those at very low risk from further testing or intensive preventative activities.

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  • The analysis adjusted for potential confounding variables identified as risk factors for dementia or indications for anticholinergic drug use, including body mass index, smoking status, alcohol consumption, Townsend deprivation score, ethnic group, comorbidities, and use of other medications.

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    From Anticholinergic Drug Exposure and the Risk of Dementia: A Nested Case-Control Study (2019) · cites this paper as [21]

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Metadata

Authors
Walters, K., Hardoon, S., Petersen, I., Iliffe, S., Omar, R. Z., Nazareth, I., & Rait, G.
Affiliations
University College London (GB)
Corresponding author
Walters, K.
ORCID
Venue
BMC Medicine
Publisher
Springer Science and Business Media LLC
Published
2016-01-21
Volume
14
Issue
1
Pages
6-6
ISSN
1741-7015 (ISSN-L)
DOI
10.1186/s12916-016-0549-y
PMC
PMC4722622
OpenAlex
W2264469274
Cited by
144
References
7
Citations by year
2026: 13 · 2025: 11 · 2024: 24 · 2023: 10 · 2022: 14 · 2021: 15 · 2020: 15 · 2019: 10 · 2018: 16 · 2017: 9 · 2016: 5
FWCI
9.20
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • BMC Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
National Institute for Health Research (3 1_79)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
Keywords
dementia risk prediction, dementia risk score, primary care data, routinely collected healthcare data, dementia incidence, risk algorithm, older adults, cardiovascular risk factors, depression, social deprivation, model validation, cognitive impairment, negative predictive value, positive predictive value
MeSH terms
Aged, Aged, 80 and over, Algorithms, Dementia, Female, Humans, Male, Middle Aged, Primary Health Care, Prognosis, Research Design, Risk Factors, Incidence

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