Identifying individuals at high risk for dementia in primary care: development and validation of the DemRisk risk prediction model using routinely collected patient data

Introduction

Health policy in the UK and globally regarding dementia, emphasises prevention and risk reduction. These goals could be facilitated by automated assessment of dementia risk in primary care using routinely collected patient data. However, existing applicable tools are weak at identifying patients at high risk for dementia. We set out to develop improved risk prediction models deployable in primary care.

Methods

Electronic health records (EHRs) for patients aged 60–89 from 393 English general practices were extracted from the Clinical Practice Research Datalink (CPRD) GOLD database. 235 and 158 practices respectively were randomly assigned to development and validation cohorts. Separate dementia risk models were developed for patients aged 60–79 (development cohort n = 616,366; validation cohort n = 419,126) and 80–89 (n = 175,131 and n = 118,717). The outcome was incident dementia within 5 years and more than 60 evidence-based risk factors were evaluated. Risk models were developed and validated using multivariable Cox regression.

Results

The age 60–79 development cohort included 10,841 incident cases of dementia (6.3 per 1,000 person-years) and the age 80–89 development cohort included 15,994 (40.2 per 1,000 person-years). Discrimination and calibration for the resulting age 60–79 model were good (Harrell’s C 0.78 (95% CI: 0.78 to 0.79); Royston’s D 1.74 (1.70 to 1.78); calibration slope 0.98 (0.96 to 1.01)), with 37% of patients in the top 1% of risk scores receiving a dementia diagnosis within 5 years. Fit statistics were lower for the age 80–89 model but dementia incidence was higher and 79% of those in the top 1% of risk scores subsequently developed dementia.

Conclusion

Our models can identify individuals at higher risk of dementia using routinely collected information from their primary care record, and outperform an existing EHR-based tool. Discriminative ability was greatest for those aged 60–79, but the model for those aged 80–89 may also be clinical useful.