@article{3077142,
    title = "Motor signs in Alzheimer's disease and vascular dementia: Detection through natural language processing, co-morbid features and relationship to adverse outcomes",
    author = "Al-Harrasi, A.M. and Iqbal, E. and Tsamakis, K. and Lasek, J. and Gadelrab, R. and Soysal, P. and Kohlhoff, E. and Tsiptsios, D. and Rizos, E. and Perera, G. and Aarsland, D. and Stewart, R. and Mueller, C.",
    journal = "Experimental Gerontology",
    year = "2021",
    volume = "146",
    publisher = "W B SAUNDERS CO-ELSEVIER INC",
    issn = "0531-5565",
    doi = "10.1016/j.exger.2020.111223",
    keywords = "antidepressant agent;  cholinergic receptor blocking agent;  cholinesterase inhibitor;  hypnotic agent;  neuroleptic agent;  psychotropic agent, adverse outcome;  age distribution;  aged;  Alzheimer disease;  Article;  bradykinesia;  clinical feature;  clinical practice;  cohort analysis;  comorbidity;  controlled study;  disease association;  England;  female;  health status;  hospitalization;  human;  major clinical study;  male;  mental disease;  mortality risk;  motor dysfunction;  multiinfarct dementia;  muscle rigidity;  natural language processing;  Parkinsonian gait;  prescription;  survival rate;  tremor;  trend study;  Alzheimer disease;  hypokinesia;  multiinfarct dementia;  natural language processing, Alzheimer Disease;  Dementia, Vascular;  Humans;  Hypokinesia;  London;  Natural Language Processing",
    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. © 2021 Elsevier Inc."
}