Multimorbidity patterns with K-means nonhierarchical cluster analysis

Concepción Violán, Albert Roso-Llorach, Quintí Foguet-Boreu, Marina Guisado-Clavero, Mariona Pons-Vigués, Enriqueta Pujol-Ribera, Jose M. Valderas

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    © 2018 The Author(s). Background: The purpose of this study was to ascertain multimorbidity patterns using a non-hierarchical cluster analysis in adult primary patients with multimorbidity attended in primary care centers in Catalonia. Methods: Cross-sectional study using electronic health records from 523,656 patients, aged 45-64 years in 274 primary health care teams in 2010 in Catalonia, Spain. Data were provided by the Information System for the Development of Research in Primary Care (SIDIAP), a population database. Diagnoses were extracted using 241 blocks of diseases (International Classification of Diseases, version 10). Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex. Results: The 408,994 patients who met multimorbidity criteria were included in the analysis (mean age, 54.2 years [Standard deviation, SD: 5.8], 53.3% women). Six multimorbidity patterns were obtained for each sex; the three most prevalent included 68% of the women and 66% of the men, respectively. The top cluster included coincident diseases in both men and women: Metabolic disorders, Hypertensive diseases, Mental and behavioural disorders due to psychoactive substance use, Other dorsopathies, and Other soft tissue disorders. Conclusion: Non-hierarchical cluster analysis identified multimorbidity patterns consistent with clinical practice, identifying phenotypic subgroups of patients.
    Original languageEnglish
    Article number108
    JournalBMC Family Practice
    Issue number1
    Publication statusPublished - 3 Jul 2018


    • Cluster analysis
    • Diseases
    • Electronic health records
    • K-means clustering
    • Multimorbidity
    • Multiple correspondence analysis
    • Primary health care


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