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Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach

Published online by Cambridge University Press:  28 August 2019

Sabela C. Mallo Open the ORCID record for Sabela C. Mallo [Opens in a new window] ,
Sonia Valladares-Rodriguez ,
David Facal ,
Cristina Lojo-Seoane Open the ORCID record for Cristina Lojo-Seoane [Opens in a new window] ,
Manuel J. Fernández-Iglesias  and
Arturo X. Pereiro
Sabela C. Mallo*
Affiliation:
Department of Developmental Psychology, University of Santiago de Compostela, Santiago de Compostela, Galicia, Spain
Sonia Valladares-Rodriguez
Affiliation:
Department of Telematics Engineering, University of Vigo, Vigo, Spain
David Facal
Affiliation:
Department of Developmental Psychology, University of Santiago de Compostela, Santiago de Compostela, Galicia, Spain
Cristina Lojo-Seoane
Affiliation:
Department of Developmental Psychology, University of Santiago de Compostela, Santiago de Compostela, Galicia, Spain
Manuel J. Fernández-Iglesias
Affiliation:
Department of Telematics Engineering, University of Vigo, Vigo, Spain
Arturo X. Pereiro
Affiliation:
Department of Developmental Psychology, University of Santiago de Compostela, Santiago de Compostela, Galicia, Spain
*
Correspondence should be addressed to: Sabela C. Mallo, Development Psychology, University of Santiago de Compostela, Xosé María Suárez Núnhez Street, Campus Sur, Santiago de Compostela, ES–15782, Spain. Phone +34 881-813-949; Fax +34 881-813-901; Email: [email protected].
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Abstract

Objectives:

To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not.

Design:

Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated using a 10-fold stratified validation procedure. Performance metrics (accuracy, recall, F-1 score, and Cohen’s kappa) were computed for each algorithm, and graphic metrics (ROC and precision-recall curves) and features analysis were computed for the best-performing algorithm.

Setting:

Primary care health centers.

Participants:

128 participants: 78 cognitively unimpaired and 50 with MCI.

Measurements:

Diagnosis at baseline, months from the baseline assessment until the 3rd follow-up or development of dementia, gender, age, Charlson Comorbidity Index, Neuropsychiatric Inventory-Questionnaire (NPI-Q) individual items, NPI-Q total severity, and total stress score and Geriatric Depression Scale-15 items (GDS-15) total score.

Results:

30 participants developed dementia, while 98 did not. Most of the participants who developed dementia were diagnosed at baseline with amnestic multidomain MCI. The Random Forest Plot model provided the metrics that best predicted conversion to dementia (e.g. accuracy=.88, F1=.67, and Cohen’s kappa=.63). The algorithm indicated the importance of the metrics, in the following (decreasing) order: months from first assessment, age, the diagnostic group at baseline, total NPI-Q severity score, total NPI-Q stress score, and GDS-15 total score.

Conclusions:

ML is a valuable technique for detecting the risk of conversion to dementia in MCI patients. Some NPS proxies, including NPI-Q total severity score, NPI-Q total stress score, and GDS-15 total score, were deemed as the most important variables for predicting conversion, adding further support to the hypothesis that some NPS are associated with a higher risk of dementia in MCI.

Keywords

neuropsychiatric symptomsmild cognitive impairmentdementiabehavioral and psychological symptoms of dementiadiagnosis and classifications
Type
Original Research Article
Information
International Psychogeriatrics , Volume 32 , Issue 3: Issue Theme: Improving Healthcare in Long-Term Care and Nursing Homes , March 2020 , pp. 381 - 392
Copyright
© International Psychogeriatric Association 2019

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