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200 - Neuropsychiatric symptoms influence performance of activities of daily living in symptomatic Alzheimer’s Disease

Published online by Cambridge University Press:  01 November 2021

Nikos Giannakis ,
Maria Skondra ,
Suzanna Aligianni ,
Eliza Georgiou ,
Savvina Prapiadou ,
Iliana Lentzari ,
Antonios Politis ,
Nikos Laskaris  and
Panagiotis Alexopoulos
Nikos Giannakis
Affiliation:
Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
Maria Skondra
Affiliation:
Department of Psychiatry, Patras University Hospital, Faculty of Medicine, School of Health Sciences, University of Patras, Patras, Greece Psychogeriatric unit for neurocognitive assessment and caregiver counselling, Patras Office of The Hellenic Red Cross, Patras, Greece
Suzanna Aligianni
Affiliation:
Faculty of Medicine, School of Health Sciences, University of Patras, Patras, Greece
Eliza Georgiou
Affiliation:
Faculty of Medicine, School of Health Sciences, University of Patras, Patras, Greece
Savvina Prapiadou
Affiliation:
Faculty of Medicine, School of Health Sciences, University of Patras, Patras, Greece
Iliana Lentzari
Affiliation:
Department of Psychiatry, Patras University Hospital, Faculty of Medicine, School of Health Sciences, University of Patras, Patras, Greece
Antonios Politis
Affiliation:
First Department of Psychiatry, Eginition Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins Medical School, Baltimore, USA
Nikos Laskaris
Affiliation:
Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
Panagiotis Alexopoulos
Affiliation:
Department of Psychiatry, Patras University Hospital, Faculty of Medicine, School of Health Sciences, University of Patras, Patras, Greece Psychogeriatric unit for neurocognitive assessment and caregiver counselling, Patras Office of The Hellenic Red Cross, Patras, Greece Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Faculty of Medicine, Technical University of Munich, Munich, Germany Patras Dementia Day Care Center, Corporation for Succor and Care of Elderly and Disabled – FRODIZO, Patras, Greece
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Abstract

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Background:

The triad of symptom groups of Alzheimer’s disease (AD) encompasses cognitive impairment (e.g. impaired memory or orientation), neuropsychiatric symptoms like apathy, depressive mood, delusions, hallucinations or anxiety, and functional impairment exclusively in complex activities of daily living (cADL, e.g. preparing meals, managing finances) in minor neurocognitive disorder due to AD and both in complex and basic ADL (bADL, e.g. dressing, toileting) in major neurocognitive disorder due to AD. These functional impairments are widely thought to be exclusively attributable to the cognitive deficits of the disease. Of note, mounting evidence indicates that neuropsychiatric symptoms are very common in AD and pose a heavy burden to both patients and their caregivers.

Research objective:

To unravel potential associations between neuropsychiatric symptoms and cADL and bADL in individuals with neurocognitive disorder due to AD by means of machine learning (ML).

Methods:

The study included 189 cognitively intact older individuals (CI) and 130 with either minor or major neurocognitive disorder due to AD. Neuropsychiatric symptoms were captured with the Neuropsychiatric Inventory (NPI), covering delusions, hallucinations, aggression, depression, anxiety, apathy, elation, disinhibition, irritability, motor disturbance, nighttime behavioural disturbances and appetite disturbances; cognitive function was assessed with the Cognitive Telephone Screening Instrument (COGTEL); The Bristol ADL scale, an informant-rated measure, was employed for tapping performance of ADL. A variety of ML-models was constructed and trained/tested using a 5-fold cross validation, with SMOTE employed as a remedy for class imbalances. In all cases the features had been selected beforehand based on LASSO technique. The dependent variable was either cADL or bADL (after their discretization based on kMeans quantization). Additionally, the modelling of the diagnosis was also attempted within our ML framework.

Results:

Gradient Boosting models performed superiorly. cADL and bADL levels are predicted based on both deficits in cognitive domains and NPI variables with an accuracy of 82.3% and 84.8% respectively.

In addition, diagnosis can be predicted, with an accuracy of 83.5%, based on a model in which NPI and Bristol ADL variables were significant predictors.

Conclusions:

cADL- and bADL performance in patients with AD is influenced by both cognitive deficits and neuropsychiatric symptoms.

Type
Live Free/Oral Communications
Information
International Psychogeriatrics , Volume 33 , Issue S1 , October 2021 , pp. 5 - 6
Copyright
© International Psychogeriatric Association 2021