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Delirium superimposed on dementia: defining disease states and course from longitudinal measurements of a multivariate index using latent class analysis and hidden Markov chains

Published online by Cambridge University Press:  20 June 2011

Antonio Ciampi ,
Alina Dyachenko ,
Martin Cole  and
Jane McCusker
Antonio Ciampi*
Affiliation:
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
Alina Dyachenko
Affiliation:
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
Martin Cole
Affiliation:
Department of Psychiatry, St. Mary's Hospital Center and McGill University, Montreal, Canada
Jane McCusker
Affiliation:
Department of Clinical Epidemiology and Community Studies, St. Mary's Hospital Center and McGill University, Montreal, Canada
*
Correspondence should be addressed to: Dr. Antonio Ciampi, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020, Pine Ave. West, Montreal, QC, H3A 1A2, Canada. Phone: +1 (514) 398-1584; Fax: +1 (514) 398-4503. Email: antonio.ciampi@mcgill.ca.
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Abstract

Background: The study of mental disorders in the elderly presents substantial challenges due to population heterogeneity, coexistence of different mental disorders, and diagnostic uncertainty. While reliable tools have been developed to collect relevant data, new approaches to study design and analysis are needed. We focus on a new analytic approach.

Methods: Our framework is based on latent class analysis and hidden Markov chains. From repeated measurements of a multivariate disease index, we extract the notion of underlying state of a patient at a time point. The course of the disorder is then a sequence of transitions among states. States and transitions are not observable; however, the probability of being in a state at a time point, and the transition probabilities from one state to another over time can be estimated.

Results: Data from 444 patients with and without diagnosis of delirium and dementia were available from a previous study. The Delirium Index was measured at diagnosis, and at 2 and 6 months from diagnosis. Four latent classes were identified: fairly healthy, moderately ill, clearly sick, and very sick. Dementia and delirium could not be separated on the basis of these data alone. Indeed, as the probability of delirium increased, so did the probability of decline of mental functions. Eight most probable courses were identified, including good and poor stable courses, and courses exhibiting various patterns of improvement.

Conclusion: Latent class analysis and hidden Markov chains offer a promising tool for studying mental disorders in the elderly. Its use may show its full potential as new data become available.

Keywords

latent classeshidden Markov chainsdisease courseAICBICmodel selectionmultiple imputation
Type
Research Article
Information
International Psychogeriatrics , Volume 23 , Issue 10 , December 2011 , pp. 1659 - 1670
Copyright
Copyright © International Psychogeriatric Association 2011

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Supplementary material: File

Ciampi Supplementary Appendix

Supplementary Appendix: Model parameters

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