Motor signs in Alzheimer's disease and vascular dementia: Detection through natural language processing, co-morbid features and relationship to adverse outcomes

Ahmed M Al-Harrasi; Ehtesham Iqbal; Konstantinos Tsamakis; Judista Lasek; Romayne Gadelrab; Pinar Soysal; Enno Kohlhoff; Dimitrios Tsiptsios; Emmanouil Rizos; Gayan Perera; Dag Aarsland; Robert Stewart; Christoph Mueller

doi:10.1016/j.exger.2020.111223

Motor signs in Alzheimer's disease and vascular dementia: Detection through natural language processing, co-morbid features and relationship to adverse outcomes

Exp Gerontol. 2021 Apr:146:111223. doi: 10.1016/j.exger.2020.111223. Epub 2021 Jan 12.

Authors

Affiliations

¹ King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK; Sultan Qaboos University Hospital, Muscat, Oman.
² King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK.
³ King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK; National and Kapodistrian University of Athens, School of Medicine, Second Department of Psychiatry, University General Hospital 'ATTIKON', Athens, Greece.
⁴ South London and Maudsley NHS Foundation Trust, London, UK.
⁵ Department of Geriatric Medicine, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey.
⁶ Aragon Institute for Health Research (IIS Aragón), Zaragoza, Spain.
⁷ Neurophysiology Department, Sunderland Royal Hospital, South Tyneside & Sunderland NHS Foundation Trust, Sunderland, UK.
⁸ National and Kapodistrian University of Athens, School of Medicine, Second Department of Psychiatry, University General Hospital 'ATTIKON', Athens, Greece.
⁹ King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK; South London and Maudsley NHS Foundation Trust, London, UK; Centre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway.
¹⁰ King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK; South London and Maudsley NHS Foundation Trust, London, UK.
¹¹ King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK; South London and Maudsley NHS Foundation Trust, London, UK. Electronic address: christoph.mueller@kcl.ac.uk.

PMID: 33450346
DOI: 10.1016/j.exger.2020.111223

Abstract

Background: Motor signs in patients with dementia are associated with a higher risk of cognitive decline, institutionalisation, death and increased health care costs, but prevalences differ between studies. The aims of this study were to employ a natural language processing pipeline to detect motor signs in a patient cohort in routine care; to explore which other difficulties occur co-morbid to motor signs; and whether these, as a group and individually, predict adverse outcomes.

Methods: A cohort of 11,106 patients with dementia in Alzheimer's disease, vascular dementia or a combination was assembled from a large dementia care health records database in Southeast London. A natural language processing algorithm was devised in order to establish the presence of motor signs (bradykinesia, Parkinsonian gait, rigidity, tremor) recorded around the time of dementia diagnosis. We examined the co-morbidity profile of patients with these symptoms and used Cox regression models to analyse associations with survival and hospitalisation, adjusting for twenty-four potential confounders.

Results: Less than 10% of patients were recorded to display any motor sign, and tremor was most frequently detected. Presence of motor signs was associated with younger age at diagnosis, neuropsychiatric symptoms, poor physical health and higher prescribing of psychotropics. Rigidity was independently associated with a 23% increased mortality risk after adjustment for confounders (p = 0.014). A non-significant trend for a 15% higher risk of hospitalisation was detected in those with a recorded Parkinsonian gait (p = 0.094).

Conclusions: With the exception of tremor, motor signs appear to be under-recorded in routine care. They are part of a complex clinical picture and often accompanied by neuropsychiatric and functional difficulties, and thereby associated with adverse outcomes. This underlines the need to establish structured examinations in routine clinical practice via easy-to-use tools.

Keywords: Alzheimer's disease; Dementia; Natural language processing; Parkinsonism; Prevalence rate; Vascular dementia.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Alzheimer Disease* / diagnosis
Alzheimer Disease* / epidemiology
Dementia, Vascular*
Humans
Hypokinesia
London
Natural Language Processing

Abstract

Publication types

MeSH terms

Grants and funding