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Predicting dementia from primary care records: a systematic review and meta-analysis

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Version 2 2023-06-12, 08:51
Version 1 2023-06-09, 12:39
journal contribution
posted on 2023-06-12, 08:51 authored by Elizabeth FordElizabeth Ford, Nicholas Greenslade, Priyamvada Paudyal, Stephen BremnerStephen Bremner, Helen Smith, Sube Banerjee, Shanu Sadhwani, Philip Rooney, Seb OliverSeb Oliver, Jackie Cassell
Introduction Possible dementia is usually identified in primary care by general practitioners (GPs) who refer to specialists for diagnosis. Only two-thirds of dementia cases are currently recorded in primary care, so increasing the proportion of cases diagnosed is a strategic priority for the UK and internationally. Clinical entities in the primary care record may indicate risk of developing dementia, and could be combined in a predictive model to help find patients who are missing a diagnosis. We conducted a meta-analysis to identify clinical entities with potential for use in such a predictive model for dementia in primary care. Methods and Findings We conducted a systematic search in PubMed, Web of Science and primary care database bibliographies. We included cohort or case-control studies which used routinely collected primary care data, to measure the association between any clinical entity and dementia. Meta-analyses were performed to pool odds ratios. A sensitivity analysis assessed the impact of non-independence of cases between studies. From a sift of 3836 papers, 20 studies, all European, were eligible for inclusion, comprising >1 million patients. 75 clinical entities were assessed as risk factors for all cause dementia, Alzheimer’s (AD) and Vascular dementia (VaD). Data included were unexpectedly heterogeneous, and assumptions were made about definitions of clinical entities and timing as these were not all well described. Meta-analysis showed that neuropsychiatric symptoms including depression, anxiety, and seizures, cognitive symptoms, and history of stroke, were positively associated with dementia. Cardiovascular risk factors such as hypertension, heart disease, dyslipidaemia and diabetes were positively associated with VaD and negatively with AD. Sensitivity analyses showed similar results. Conclusions These findings are of potential value in guiding feature selection for a risk prediction tool for dementia in primary care. Limitations include findings being UK-focussed. Further predictive entities ascertainable from primary care data, such as changes in consulting patterns, were absent from the literature and should be explored in future studies.

Funding

ASTRODEM: Using astrophysics to close the 'diagnosis gap' for dementia in UK general practice.; G1895; WELLCOME TRUST; 202133/Z/16/Z

History

Publication status

  • Published

File Version

  • Published version

Journal

PLoS ONE

ISSN

1932-6203

Publisher

Public Library of Science

Issue

3

Volume

13

Page range

1-23

Article number

e0194735

Department affiliated with

  • Primary Care and Public Health Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2018-03-28

First Open Access (FOA) Date

2018-04-06

First Compliant Deposit (FCD) Date

2018-03-27

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