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

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Reeves, David; Morgan, Catharine; Stamate, Daniel; Ford, Elizabeth; Ashcroft, Darren M.; Kontopantelis, Evangelos; Van Marwijk, Harm and McMillan, Brian. 2024. Identifying individuals at high risk for dementia in primary care: Development and validation of the DemRisk risk prediction model using routinely collected patient data. PLoS ONE, 19(10), e0310712. ISSN 1932-6203 [Article]

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Official URL: https://doi.org/10.1371/journal.pone.0310712

Abstract or Description

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.

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.

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.

Our prediction 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.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1371/journal.pone.0310712

Additional Information:

Funding: This study was funded by Alzheimer’s Research UK (ARUK: https://urldefense.com/v3/__https://www.alzheimersresearchuk.org/__;!!PDiH4ENfjr2_Jw!EYvKRuQgEmAfSpu73NKZ9jFnv44lWSDi-DodiJn9z_CAPzwpwkGdpEhCdC5A9MucvhCkwNsfNGwqW3bPUo6qP-1t7g$ [alzheimersresearchuk[.]org]), awarded to DR and DS (grant reference: ARUKPRRF2017- 012).

Data Access Statement:

The data used in this study was obtained via the Clinical Practice Research Datalink (CPRD). CPRD provides a research service which provides representative, longitudinal real-time anonymized patient electronic health records data from primary care and other health services across the UK. The licensing agreement between University of Manchester and CPRD, and the data governance of CPRD prevent the sharing or distribution of patient data to other individuals. Hence any requests for access to data from the study should be addressed to cprdenquiries@mhra.gov.uk. All researchers requiring access will require approval of their proposals from CPRD before data release.

Departments, Centres and Research Units:

Computing

Dates: