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

Elizabeth Ford, Nicholas Greenslade, Priya Paudyal, Stephen Bremner, Helen E Smith, Sube Banerjee, Shanu Sadhwani, Philip Rooney, Seb Oliver and Jackie Cassell

PLOS ONE, 2018, vol. 13, issue 3, 1-23

Abstract: 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. Variables 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. 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 also be explored in future studies.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0194735

DOI: 10.1371/journal.pone.0194735

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