Twelve-year clinical trajectories of multimorbidity in a population of older adults

Vetrano, Davide L. (author): Karolinska Institutet,Stockholms universitet,Centrum för forskning om äldre och åldrande (ARC), (tills m KI),Fondazione Policlinico Universitario “A. Gemelli” IRCC, Italy; Università Cattolica del Sacro Cuore, Italy

Roso-Llorach, Albert (author)

Fernández, Sergio (author)

Guisado-Clavero, Marina (author)

Violán, Concepción (author)

Onder, Graziano (author)

Fratiglioni, Laura (author): Karolinska Institutet,Stockholms universitet,Centrum för forskning om äldre och åldrande (ARC), (tills m KI),Stockholm Gerontology Research Center, Sweden

Calderón-Larrañaga, Amaia (author): Karolinska Institutet,Stockholms universitet,Centrum för forskning om äldre och åldrande (ARC), (tills m KI)

Marengoni, Alessandra (author): Karolinska Institutet,Stockholms universitet,Centrum för forskning om äldre och åldrande (ARC), (tills m KI),University of Brescia, Italy

(creator_code:org_t)

2020-06-26
2020
English.
In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1

Related links:: https://doi.org/10.1...; show more...; https://www.nature.c...; https://urn.kb.se/re...; https://doi.org/10.1...; http://kipublication...; show less...

Abstract Subject headings

Multimorbidity-the co-occurrence of multiple diseases-is associated to poor prognosis, but the scarce knowledge of its development over time hampers the effectiveness of clinical interventions. Here we identify multimorbidity clusters, trace their evolution in older adults, and detect the clinical trajectories and mortality of single individuals as they move among clusters over 12 years. By means of a fuzzy c-means cluster algorithm, we group 2931 people >= 60 years in five clinically meaningful multimorbidity clusters (52%). The remaining 48% are part of an unspecific cluster (i.e. none of the diseases are overrepresented), which greatly fuels other clusters at follow-ups. Clusters contribute differentially to the longitudinal development of other clusters and to mortality. We report that multimorbidity clusters and their trajectories may help identifying homogeneous groups of people with similar needs and prognosis, and assisting clinicians and health care systems in the personalization of clinical interventions and preventive strategies. The co-occurrence of chronic diseases in the same person increases the risk of negative health events. Here authors show that grouping people based on their underlying disease patterns helps to identify homogeneous groups of people with similar needs and prognosis, facilitating personalized approaches.

By the author/editor: Vetrano, Davide ...; Roso-Llorach, Al ...; Fernández, Sergi ...; Guisado-Clavero, ...; Violán, Concepci ...; Onder, Graziano; show more...; Fratiglioni, Lau ...; Calderón-Larraña ...; Marengoni, Aless ...; show less...

About the subject

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Other Medical an ...; and Gerontology spec ...

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