Motor signs in Alzheimer-s disease and vascular dementia: Detection through natural language processing, co-morbid features and relationship to adverse outcomes
| Autor: | Dimitrios Tsiptsios, Romayne Gadelrab, Enno Kohlhoff, Judista Lasek, Ahmed M. Al-Harrasi, Konstantinos Tsamakis, Robert Stewart, Christoph Mueller, Dag Aarsland, Pinar Soysal, Emmanouil Rizos, Ehtesham Iqbal, Gayan Perera |
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| Přispěvatelé: | SOYSAL, PINAR |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Aging Parkinsonian gait Disease Hypokinesia computer.software_genre Biochemistry 03 medical and health sciences 0302 clinical medicine Endocrinology Alzheimer Disease London Genetics Medicine Dementia Humans Cognitive decline Vascular dementia Molecular Biology Natural Language Processing business.industry Proportional hazards model Parkinsonism Dementia Vascular Cell Biology medicine.disease 030104 developmental biology Cohort Detection through natural language processing co-morbid features and relationship to adverse outcomes.- Experimental gerontology ss.111223 2021 [Al-Harrasi A. M. Iqbal E. Tsamakis K. Lasek J. Gadelrab R. Soysal P. Kohlhoff E. Tsiptsios D. Rizos E. Perera G. et al. -Motor signs in Alzheimer-s disease and vascular dementia] Artificial intelligence medicine.symptom business computer 030217 neurology & neurosurgery Natural language processing |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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