1. Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population
- Author
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Violán, Concepció, Foguet-Boreu, Quintí, Fernández-Bertolín, Sergio, Guisado-Clavero, Marina, Cabrera-Bean, Margarita, Formiga, Francesc, Valderas, Jose Maria, Roso-Llorach, Albert, Universitat Autònoma de Barcelona, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, and Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
- Subjects
Male ,Chronic conditions ,Cross-sectional study ,Epidemiology ,Informàtica::Sistemes d'informació::Bases de dades [Àrees temàtiques de la UPC] ,lcsh:Medicine ,Disease ,030204 cardiovascular system & hematology ,Persones grans ,0302 clinical medicine ,Medicine ,Cluster Analysis ,Electronic Health Records ,030212 general & internal medicine ,Multiple Chronic Conditions ,Medical diagnosis ,Aged, 80 and over ,education.field_of_study ,Principal Component Analysis ,Mediterranean Region ,General Medicine ,Epidemiologia clínica ,Estudi de casos ,Female ,Mineria de dades ,medicine.medical_specialty ,Fuzzy clustering ,multimorbidity ,Population ,Mediterrània (Regió) ,03 medical and health sciences ,Cluster analysis ,Fuzzy Logic ,Humans ,education ,Data mining ,Categorical variable ,Socioeconomic status ,Aged ,business.industry ,Clinical epidemiology ,Research ,lcsh:R ,Multimorbidity ,chronic conditions ,Morbiditat ,Cross-Sectional Studies ,Fuzzy C-Means ,Spain ,Ciències de la salut::Medicina::Medicina comunitària i salut pública [Àrees temàtiques de la UPC] ,Case studies ,Older people ,Morbidity ,business ,Demography - Abstract
ObjectivesThe 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.DesignA cross-sectional study was conducted based on data from electronic health records.Setting284 primary healthcare centres in Catalonia, Spain (2012).Participants916 619 eligible individuals were included (women: 57.7%).Primary and secondary outcome measuresWe 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.ResultsMultimorbidity 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.ConclusionsMultimorbidity 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.
- Published
- 2019