Literature DB >> 29282845

Clusters of functional domains to identify older persons at risk of disability.

Luisa Costanzo1, Claudio Pedone1, Matteo Cesari2, Luigi Ferrucci3, Stefania Bandinelli4, Raffaele Antonelli Incalzi1.   

Abstract

AIM: To date, there is no consensus on which set of variables should be used to identify older persons at risk of disability in activities of daily living. The present study aimed to: (i) evaluate how different deficits cluster in a population of community-dwelling older persons; and (ii) investigate whether the discriminative capacity of physical performance measures towards the development of disability might be improved by adding psychological, social and environmental indicators.
METHODS: Data are from 709 non-disabled older persons participating in the "Invecchiare in Chianti" study. We carried out a cluster analysis of 12 deficits in multiple functional domains, selected from the available frailty assessment instruments. Then, participants were assigned to a group, based on the obtained clusters of variables. For each group, we measured the prognostic capacity and the predictive ability for 6-year disability.
RESULTS: The analysis showed a "physical" cluster (including weight loss, reduced grip strength/gait speed/physical activity, impaired balance, environmental barriers) and a "psychosocial" cluster (e.g. living alone, depression, low income). Thus, participants were classified into four groups according to the presence of a physical and/or psychosocial cluster. Compared with the "fit" group, the relative risks of becoming disabled in the "physical," "psychosocial" and "mixed" deficit groups were 2.23 (95% CI 0.71-7.00), 1.52 (95% CI 0.62-3.75) and 6.37 (95% CI 2.83-14.33), respectively. The positive and negative predictive values for the "physical," "psychosocial" and "mixed" deficit groups were, respectively, 9% and 87%, 6% and 83%, and 27% and 94%.
CONCLUSIONS: As expected, physical and psychosocial deficits cluster predominantly into different groups. Even when both are considered simultaneously, the ability to predict incident disability is still insufficient. Geriatr Gerontol Int 2018; 18: 685-691.
© 2017 Japan Geriatrics Society.

Entities:  

Keywords:  cluster analysis; community-dwelling older persons; disability; frailty; predictive value

Mesh:

Year:  2017        PMID: 29282845      PMCID: PMC5934311          DOI: 10.1111/ggi.13226

Source DB:  PubMed          Journal:  Geriatr Gerontol Int        ISSN: 1447-0594            Impact factor:   2.730


  29 in total

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Journal:  J Am Med Dir Assoc       Date:  2010-03-24       Impact factor: 4.669

10.  Frailty consensus: a call to action.

Authors:  John E Morley; Bruno Vellas; G Abellan van Kan; Stefan D Anker; Juergen M Bauer; Roberto Bernabei; Matteo Cesari; W C Chumlea; Wolfram Doehner; Jonathan Evans; Linda P Fried; Jack M Guralnik; Paul R Katz; Theodore K Malmstrom; Roger J McCarter; Luis M Gutierrez Robledo; Ken Rockwood; Stephan von Haehling; Maurits F Vandewoude; Jeremy Walston
Journal:  J Am Med Dir Assoc       Date:  2013-06       Impact factor: 4.669

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  3 in total

1.  Predictive Capacity of Frailty Phenotype Toward Patterns of Disability Identified Using Latent Class Analysis.

Authors:  Luisa Costanzo; Matteo Cesari; Luigi Ferrucci; Stefania Bandinelli; Raffaele Antonelli Incalzi; Claudio Pedone
Journal:  J Am Med Dir Assoc       Date:  2019-02-13       Impact factor: 4.669

2.  Profiles of Frailty among Older People Users of a Home-Based Primary Care Service in an Urban Area of Barcelona (Spain): An Observational Study and Cluster Analysis.

Authors:  Juan-José Zamora-Sánchez; Edurne Zabaleta-Del-Olmo; Sergio Fernández-Bertolín; Vicente Gea-Caballero; Iván Julián-Rochina; Gemma Pérez-Tortajada; Jordi Amblàs-Novellas
Journal:  J Clin Med       Date:  2021-05-13       Impact factor: 4.241

Review 3.  Muscle mass, strength, and physical performance predicting activities of daily living: a meta-analysis.

Authors:  Daniel X M Wang; Jessica Yao; Yasar Zirek; Esmee M Reijnierse; Andrea B Maier
Journal:  J Cachexia Sarcopenia Muscle       Date:  2019-12-01       Impact factor: 12.910

  3 in total

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