Abstract
BackgroundExisting 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.MethodsWe 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.ResultsDementia 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.ConclusionsRoutinely 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-016-0549-y) contains supplementary material, which is available to authorized users.
Highlights
Existing dementia risk scores require collection of additional data from patients, limiting their use in practice
Risk factor measurements Based on potential risk factors for dementia [3, 4, 32] available in The Health Improvement Network (THIN), we examined the following as predictor variables in the risk model: Analysis For both the development and validation cohort studies the study population was divided into two groups: those aged 60–79 years and aged 80–95 at baseline
This study developed risk algorithms for predicting a new recorded dementia diagnosis in two age groups in primary care
Summary
Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. 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. More than 115 million people are predicted to have dementia by 2050 [1], with huge associated health and social care costs [2]. There is both epidemiological [3, 4] and policy [5] support for the identification and management of modifiable risk factors for dementia to delay dementia onset. There is, a limited evidence base for current approaches to dementia screening and casefinding [8, 9] and further work needs to be completed to validate new methods across different settings, including primary care [9]
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