Uncovering Capgras delusion using a large-scale medical records database

Vaughan Bell, Caryl Marshall, Zara Kanji, Sam Wilkinson, Peter Halligan, Quinton Deeley

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
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Abstract

BACKGROUND: Capgras delusion is scientifically important but most commonly reported as single case studies. Studies analysing large clinical records databases focus on common disorders but none have investigated rare syndromes.

AIMS: Identify cases of Capgras delusion and associated psychopathology, demographics, cognitive function and neuropathology in light of existing models.

METHOD: Combined computational data extraction and qualitative classification using 250 000 case records from South London and Maudsley Clinical Record Interactive Search (CRIS) database.

RESULTS: We identified 84 individuals and extracted diagnosis-matched comparison groups. Capgras was not 'monothematic' in the majority of cases. Most cases involved misidentified family members or close partners but others were misidentified in 25% of cases, contrary to dual-route face recognition models. Neuroimaging provided no evidence for predominantly right hemisphere damage. Individuals were ethnically diverse with a range of psychosis spectrum diagnoses.

CONCLUSIONS: Capgras is more diverse than current models assume. Identification of rare syndromes complements existing 'big data' approaches in psychiatry.

DECLARATION OF INTERESTS: V.B. is supported by a Wellcome Trust Seed Award in Science (200589/Z/16/Z) and the UCLH NIHR Biomedical Research Centre. S.W. is supported by a Wellcome Trust Strategic Award (WT098455MA). Q.D. has received a grant from King's Health Partners.

COPYRIGHT AND USAGE: © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.

Original languageEnglish
Pages (from-to)179-185
Number of pages7
JournalBritish Journal of Psychiatry Open
Volume3
Issue number4
DOIs
Publication statusPublished - Jul 2017

Keywords

  • Journal Article

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