Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population : Cross-sectional study in a Mediterranean population
Violán, Concepció (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Foguet-Boreu, Quintí (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Fernández-Bertolín, Sergio (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Guisado-Clavero, Marina (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Cabrera-Bean, Margarita (Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions)
Formiga, Francesc (Hospital Universitari de Bellvitge)
Valderas, Jose Maria (Health Services and Policy Research Group. Academic Collaboration for Primary Care. University of Exeter Medical School)
Roso-Llorach, Albert (Institut Universitari d'Investigació en Atenció Primària Jordi Gol)
Universitat Autònoma de Barcelona

Data:	2019
Resum:	Objectives The aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature. Design A cross-sectional study was conducted based on data from electronic health records. Setting 284 primary healthcare centres in Catalonia, Spain (2012). Participants 916 619 eligible individuals were included (women: 57. 7%). Primary and secondary outcome measures We extracted data on demographics, International Classification of Diseases version 10 chronic diagnoses, prescribed drugs and socioeconomic status for patients aged ≥65. Following principal component analysis of categorical and continuous variables for dimensionality reduction, machine learning techniques were applied for the identification of disease clusters in a fuzzy c-means analysis. Sensitivity analyses, with different prevalence cut-off points for chronic diseases, were also conducted. Solutions were evaluated from clinical consistency and significance criteria. Results Multimorbidity was present in 93. 1%. Eight clusters were identified with a varying number of disease values: nervous and digestive; respiratory, circulatory and nervous; circulatory and digestive; mental, nervous and digestive, female dominant; mental, digestive and blood, female oldest-old dominant; nervous, musculoskeletal and circulatory, female dominant; genitourinary, mental and musculoskeletal, male dominant; and non-specified, youngest-old dominant. Nuclear diseases were identified for each cluster independently of the prevalence cut-off point considered. Conclusions Multimorbidity patterns were obtained using fuzzy c-means cluster analysis. They are clinically meaningful clusters which support the development of tailored approaches to multimorbidity management and further research.
Ajuts:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2017SGR578 Ministerio de Economía y Competitividad PI16/00639
Nota:	Altres ajuts: PERIS/SLT002/16/00058
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, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	BMJ open, Vol. 9 Núm. 8 (january 2019) , p. e029594, ISSN 2044-6055

DOI: 10.1136/bmjopen-2019-029594
PMID: 31471439

14 p, 560.8 KB

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