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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*
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: sabelacarme.mallo@usc.es.

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.

Type
Original Research Article
Copyright
© International Psychogeriatric Association 2019

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References

Abu-Mostafa, Y.S., Magdon-Ismail, M. and Lin, H.-T. (2012). Learning from Data: A Short Course. United States: AMLBook.Google Scholar
Acosta, I., Borges, G., Aguirre-Hernandez, R., Sosa, A.L., Prince, M. and 66 Dementia Research Group. (2018). Neuropsychiatric symptoms as risk factors of dementia in a Mexican population: a 10/66 dementia research group study. Alzheimers and Dementia, 14, 271279. doi: 10.1016/j.jalz.2017.08.015.CrossRefGoogle Scholar
American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental Disorders – Text Revision (4th ed.). Washington, DC: American Psychistric Association.Google Scholar
Banks, S.J.et al. (2014). The Alzheimer’s Disease cooperative study prevention instrument project: longitudinal outcome of behavioral measures as predictors of cognitive decline. Dementia and Geriatric Cognitive Disorders Extra, 4, 509516. doi: 10.1159/000357775.CrossRefGoogle ScholarPubMed
Benedet, M.J. and Seisdedos, N. (1996). Evaluación clínica de las quejas de memoria en la vida cotidiana. Madrid: Editorial Médica Panamericana.Google Scholar
Brambati, S.M., Belleville, S., Kergoat, M.J., Chayer, C., Gauthier, S. and Joubert, S. (2009). Single-and multiple-domain amnestic mild cognitive impairment: two sides of the same coin? Dementia and Geriatric Cognitive Disorders, 28, 541549. doi: 10.1159/000255240.CrossRefGoogle ScholarPubMed
Brodaty, H., Woodward, M., Boundy, K., Ames, D. and Balshaw, R. (2011). Patients in Australian memory clinics: baseline characteristics and predictors of decline at six months. International Psychogeriatrics, 23, 10861096. doi: 10.1017/S1041610211000688.CrossRefGoogle ScholarPubMed
Charlson, M.E., Pompei, P., Ales, K.L. and MacKenzie, C.R. (1987). A new method of classifying prognostic in longitudinal studies: development and validation. Journal of Chronic Diseases, 40, 373383.CrossRefGoogle ScholarPubMed
Cooper, C., Sommerlad, A., Lyketsos, C.G. and Livingston, G. (2015). Modifiable predictors of dementia in mild cognitive impairment: a systematic review and meta-analysis. American Journal of Psychiatry, 172, 323334. doi: 10.1176/appi.ajp.2014.14070878.CrossRefGoogle ScholarPubMed
Corrada, M.M., Brookmeyer, R., Paganini‐Hill, A., Berlau, D. and Kawas, C.H. (2010). Dementia incidence continues to increase with age in the oldest old the 90 study. Annals of Neurology, 67, 114121. doi: 10.1002/ana.21915.CrossRefGoogle ScholarPubMed
Cummings, J.L., Mega, M., Gray, K., Rosenberg-Thompson, S., Carusi, D.A. and Gornbein, J. (1994). The neuropsychiatric inventory: comprehensive assessment of psychopathology in dementia. Neurology, 44, 23082314. doi: 10.1212/WNL.44.12.2308.CrossRefGoogle ScholarPubMed
Delis, D.C., Kramer, J.H., Kaplan, E. and Ober, B.A. (1987). The California Verbal Learning Test: Research edition, Adult version. San Antonio, TX: The Psychological Corporation.Google Scholar
Facal, D., Guàrdia-olmos, J. and Juncos-Rabadán, O. (2015). Diagnostic transitions in mild cognitive impairment by use of simple Markov models. International Journal of Geriatric Psychiatry, 30, 669676. doi: 10.1002/gps.4197.CrossRefGoogle ScholarPubMed
Facal, D., Valladares‐Rodriguez, S., Lojo‐Seoane, C., Pereiro, A.X., Anido‐Rifon, L. and Juncos‐Rabadán, O. (2019). Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia. International Journal of Geriatric Psychiatry, 34, 941949. doi: 10.1002/gps.5090.CrossRefGoogle Scholar
Feldman, H.et al. (2004). Behavioral symptoms in mild cognitive impairment. Neurology, 63, 11991201. doi: 10.1212/01.WNL.0000118301.92105.EE.CrossRefGoogle Scholar
Fischer, C.E., Ismail, Z. and Schweizer, T.A. (2012a). Delusions increase functional impairment in Alzheimer’s disease. Dementia and Geriatric Cognitive Disorders, 33, 393399. doi: 10.1159/000339954.CrossRefGoogle ScholarPubMed
Fischer, C.E, Ismail, Z. and Schweizer, T.A. (2012b). Impact of neuropsychiatric symptoms on caregiver burden in patients with Alzheimers disease. Neurodegenerative Disease Management, 2, 269277. doi: 10.2217/nmt.12.19.CrossRefGoogle Scholar
Folstein, M.F., Folstein, S.E. and McHugh, P.R. (1975). ‘Mini-mental state.’ A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189198. doi: 10.1016/0022-3956(75)90026-6.CrossRefGoogle ScholarPubMed
Forrester, S.N., Gallo, J.J., Smith, G.S. and Leoutsakos, J.M.S. (2016). Patterns of neuropsychiatric symptoms in mild cognitive impairment and risk of dementia. The American Journal of Geriatric Psychiatry, 24, 117125. doi: 10.1016/j.jagp.2015.05.007.CrossRefGoogle ScholarPubMed
Gauthier, S.et al. (2006). Mild cognitive impairment. The Lancet, 367, 12621270. doi: 10.1016/S0140-6736(06)68542-5.CrossRefGoogle ScholarPubMed
Gonzales, M.M.et al. (2017). Cortical atrophy is associated with accelerated cognitive decline in mild cognitive impairment with subsyndromal depression. American Journal of Geriatric Psychiatry, 25, 980991. doi: 10.1016/j.jagp.2017.04.011.CrossRefGoogle ScholarPubMed
Guercio, B.J.et al. (2015). The apathy evaluation scale: a comparison of subject, informant, and clinician report in cognitively normal elderly and mild cognitive impairment. Journal of Alzheimer’s Disease, 47, 421432. doi: 10.3233/JAD-150146.CrossRefGoogle ScholarPubMed
Ismail, Z.et al. (2016). Neuropsychiatric symptoms as early manifestations of emergent dementia: provisional diagnostic criteria for mild behavioral impairment. Alzheimers and Dementia, 12, 195202. doi: 10.1016/j.jalz.2015.05.017.CrossRefGoogle ScholarPubMed
Ismail, Z.et al. (2017a). Prevalence of depression in patients with mild cognitive impairment: a systematic review and meta-analysis. JAMA Psychiatry, 74, 5867. doi: 10.1001/jamapsychiatry.2016.3162.CrossRefGoogle ScholarPubMed
Ismail, Z.et al. (2017b). The mild behavioral impairment checklist (mbi-c): a rating scale for neuropsychiatric symptoms in pre-dementia populations. Journal of Alzheimers Disease, 56, 929938. doi: 10.3233/JAD-160979.CrossRefGoogle ScholarPubMed
Ismail, Z.et al. (2018). Affective and emotional dysregulation as pre-dementia risk markers: exploring the mild behavioral impairment symptoms of depression, anxiety, irritability, and euphoria. International Psychogeriatrics, 30, 185196. doi: 10.1017/S1041610217001880.CrossRefGoogle ScholarPubMed
Ismail, Z. and Mortby, M.E. (2017). Cognitive and neuropsychiatric screening tests in older adults. In Chiu, H. and Shulman, K. (Eds.) Mental Health and Illness of the Elderly (pp. 343368). Singapore: Springer.CrossRefGoogle Scholar
Jack, C.R.et al. (2018). NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers and Dementia, 14, 535562. doi: 10.1016/j.jalz.2018.02.018.CrossRefGoogle Scholar
Juncos-Rabadán, O.et al. (2012). Prevalence and correlates of cognitive impairment in adults with subjective memory complaints in primary care centres. Dementia and Geriatric Cognitive Disorders, 33, 226232. doi: 10.1159/000338607.CrossRefGoogle ScholarPubMed
Kaufer, D.et al. (2000). Validation of the NPI-Q, a brief clinical form of the neuropsychiatric inventory. Journal of Neuropsychiatry, 12, 233239. doi: 10.1176/appi.neuropsych.12.2.233.CrossRefGoogle ScholarPubMed
Kononenko, I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine, 23, 89109. doi: 10.1016/S0933-3657(01)00077-X.CrossRefGoogle ScholarPubMed
Lanctôt, K.L.et al. (2017). Apathy associated with neurocognitive disorders: recent progress and future directions. Alzheimers and Dementia, 13, 84100. doi: 10.1016/j.jalz.2016.05.008.CrossRefGoogle ScholarPubMed
Lawton, M.P. and Brody, E.M. (1969). Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist, 3, 179186.CrossRefGoogle Scholar
Li, X.X. and Li, Z. (2018). The impact of anxiety on the progression of mild cognitive impairment to dementia in Chinese and English data bases: a systematic review and meta-analysis. International Journal of Geriatric Psychiatry, 33, 131140. doi: 10.1002/gps.4694.CrossRefGoogle ScholarPubMed
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F. and Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain – computer interfaces. Journal of Neural Engineering, 4, R1. doi: 10.1088/1741-2560/4/2/R01.CrossRefGoogle ScholarPubMed
Lyketsos, C.G.et al. (2011). Neuropsychiatric symptoms in Alzheimer’s disease. Alzheimers and Dementia, 7, 532539. doi: 10.1016/j.jalz.2011.05.2410.CrossRefGoogle ScholarPubMed
Mah, L., et al. (2015). Anxiety symptoms in amnestic mild cognitive impairment are associated with medial temporal atrophy and predict conversion to Alzheimer’s disease. American Journal of Geriatric Psychiatry, 165, 255269. doi: 10.1016/j.trsl.2014.08.005.Google Scholar
Maroco, J., et al. (2011). Data mining methods in the prediction of Dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Research Notes, 4, 299.CrossRefGoogle ScholarPubMed
de Medeiros, K.et al. (2010). The Neuropsychiatric Inventory-Clinician rating scale (NPI-C): reliability and validity of a revised assessment of neuropsychiatric symptoms in dementia. International Psychogeriatrics, 22, 984994. doi: 10.1017/S1041610210000876.CrossRefGoogle ScholarPubMed
Michaud, T.L., Su, D., Siahpush, M. and Murman, D.L. (2017). The risk of incident mild cognitive impairment and progression to dementia considering mild cognitive impairment subtypes. Dementia and Geriatric Cognitive Disorders Extra, 7, 1529. doi: 10.1159/000452486.CrossRefGoogle ScholarPubMed
Mitchell, A.J., Beaumont, H., Ferguson, D., Yadegarfar, M. and Stubbs, B. (2014). Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: meta-analysis. Acta Psychiatrica Scandinavica, 130, 439451. doi: 10.1111/acps.12336.CrossRefGoogle ScholarPubMed
Molinuevo, J.L.et al. (2017). Implementation of subjective cognitive decline criteria in research studies. Alzheimers and Dementia, 13, 296311. doi: 10.1016/j.jalz.2016.09.012.CrossRefGoogle ScholarPubMed
Monastero, R., Mangialasche, F., Camarda, C., Ercolani, S. and Camarda, R. (2009). A systematic review of neuropsychiatric symptoms in mild cognitive impairment. Journal of Alzheimers Disease, 18, 1130. doi: 10.3233/JAD-2009-1120.CrossRefGoogle ScholarPubMed
Mortby, M.E., Burns, R., Eramudugolla, R., Ismail, Z. and Anstey, K.J. (2017). Neuropsychiatric symptoms and cognitive impairment: understanding the importance of co-morbid symptoms. Journal of Alzheimers Disease, 59, 141153. doi: 10.3233/JAD-170050.CrossRefGoogle ScholarPubMed
Patel, M.J., Andreescu, C., Price, J.C., Edelman, K.L., Reynolds, C.F. and Aizenstein, H.J. (2015). Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. International Journal of Geriatric Psychiatry, 30, 10561067. doi: 10.1002/gps.4262.CrossRefGoogle ScholarPubMed
Pedregosa, F.et al. (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 28252830.Google Scholar
Petersen, R.C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256, 183194. doi: 10.1111/j.1365-2796.2004.01388.x.CrossRefGoogle ScholarPubMed
Pietrzak, R.H.et al. (2012). Mild worry symptoms predict decline in learning and memory in healthy older adults: a 2-year prospective cohort study. American Journal of Geriatric Psychiatry, 20, 266275. doi: 10.1097/JGP.0b013e3182107e24.CrossRefGoogle ScholarPubMed
Pink, A.et al. (2015). Neuropsychiatric symptoms, APOE ϵ4, and the risk of incident dementia. Neurology, 84, 935943. doi: 10.1212/WNL.0000000000001307.CrossRefGoogle Scholar
Rosenberg, P.B., Mielke, M.M., Appleby, B.S., Oh, E.S., Geda, Y.E. and Lyketsos, C.G. (2013). The association of neuropsychiatric symptoms in MCI with incident dementia and Alzheimer disease. American Journal of Geriatric Psychiatry, 21, 685695. doi: 10.1016/j.jagp.2013.01.006.CrossRefGoogle ScholarPubMed
Roth, M., et al. (1986). CAMDEX: a standardised instrument for the diagnosis of mental disorder in the elderly with special reference to the early detection of dementia. The British Journal of Psychiatry, 149, 698709. doi: 10.1192/bjp.149.6.698.CrossRefGoogle Scholar
Salzberg, S.L. (1997). On comparing classifiers: pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery, 1, 317328. doi: 10.1023/A:1009752403260.CrossRefGoogle Scholar
Sanchez-Villegas, A., et al. (2008). Validity of a self-reported diagnosis of depression among participants in a cohort study using the Structured Clinical Interview for DSM-IV (SCID-I). BMC Psychiatry, 8, 43. doi: 10.1186/1471-244X-8-43.CrossRefGoogle Scholar
Schaffer, C. (1993). Overfitting avoidance as bias. Machine Learning, 10, 153178. doi: 10.1007/BF00993504.CrossRefGoogle Scholar
Singh-Manoux, A., et al. (2017). Trajectories of depressive symptoms before diagnosis of dementia: a 28-year follow-up study. JAMA Psychiatry, 74, 712718. doi: 10.1001/jamapsychiatry.2017.0660.CrossRefGoogle ScholarPubMed
Stella, F.et al. (2015). Caregiver report versus clinician impression: disagreements in rating neuropsychiatric symptoms in Alzheimer’s disease patients. International Journal of Geriatric Psychiatry, 30, 12301237. doi: 10.1002/gps.4278.CrossRefGoogle ScholarPubMed
Tapiainen, V., Hartikainen, S., Taipale, H., Tiihonen, J. and Tolppanen, A.M. (2017). Hospital-treated mental and behavioral disorders and risk of Alzheimer’s disease: a nationwide nested case-control study. European Psychiatry, 43, 9298. doi: 10.1016/j.eurpsy.2017.02.486.CrossRefGoogle ScholarPubMed
Winblad, B.et al. (2004). Mild cognitive impairment - beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256, 240246. doi: 10.1111/j.1365-2796.2004.01383.x.CrossRefGoogle Scholar
Yesavage, J.A. and Sheikh, J.I. (1986). Geriatric depression scale (GDS): recent evidence and development of a shorter version. Clinical Gerontologist, 5, 165173. doi: 10.1300/J018v05n01_09.CrossRefGoogle Scholar
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