Web of Science: 51 cites, Scopus: 61 cites, Google Scholar: cites,
Multimorbidity patterns in the elderly : a prospective cohort study with cluster analysis
Guisado Clavero, Marina (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Roso-Llorach, Albert (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
López-Jimenez, Tomàs (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Pons-Vigués, Mariona (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Foguet-Boreu, Quintí (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Muñoz, Miguel Angel (Universitat Autònoma de Barcelona)
Violán, Concepció (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)

Data: 2018
Resum: Multimorbidity is the coexistence of more than two chronic diseases in the same individual; however, there is no consensus about the best definition. In addition, few studies have described the variability of multimorbidity patterns over time. The aim of this study was to identify multimorbidity patterns and their variability over a 6-year period in patients older than 65 years attended in primary health care. A cohort study with yearly cross-sectional analysis of electronic health records from 50 primary health care centres in Barcelona. Selected patients had multimorbidity and were 65 years of age or older in 2009. Diagnoses (International Classification of Primary Care, second edition) were extracted using O'Halloran criteria for chronic diseases. Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex and age group (65-79 and ≥80 years) at the beginning of the study period. Analysis of 2009 electronic health records from 190,108 patients with multimorbidity (59. 8% women) found a mean age of 71. 8 for the 65-79 age group and 84. 16 years for those over 80 (Standard Deviation [SD] 4. 35 and 3. 46, respectively); the median number of chronic diseases was seven (Interquartil range [IQR] 5-10). We obtained 6 clusters of multimorbidity patterns (1 nonspecific and 5 specifics) in each group, being the specific ones: Musculoskeletal, Endocrine-metabolic, Digestive/Digestive-respiratory, Neurological, and Cardiovascular patterns. A minimum of 42. 5% of the sample remained in the same pattern at the end of the study, reflecting the stability of these patterns. This study identified six multimorbidity patterns per each group, one nonnspecific pattern and five of them with a specific pattern related to an organic system. The multimorbidity patterns obtained had similar characteristics throughout the study period. These data are useful to improve clinical management of each specific subgroup of patients showing a particular multimorbidity pattern. The online version of this article (10. 1186/s12877-018-0705-7) contains supplementary material, which is available to authorized users.
Nota: This manuscript constitutes a part of the PhD thesis of MGC in the Public Health Department of the Universitat Autònoma de Barcelona. This work was supported by a pre-doctoral grant from Catalan Health Institute in Barcelona; by the Catalan Society of General Practitioners (CAMFiC) and by SIDIAP grant to MGC in 2015; this latter organization allowed us to explore their dataset to obtain the results. The funders had no role in the study design or data collection, analysis, and interpretation, writing of the manuscript, and decision to submit for publication.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Multimorbidity ; Chronic disease ; Ageing ; Primary health care ; Cluster analysis ; Electronic health record
Publicat a: BMC geriatrics, Vol. 18 (2018) , ISSN 1471-2318

DOI: 10.1186/s12877-018-0705-7
PMID: 29338690

11 p, 911.2 KB

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