Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population
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 individua...
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BMJ Publishing Group
2019-08-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/9/8/e029594.full |
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| author | Concepción Violán Quintí Foguet-Boreu Sergio Fernández-Bertolín Marina Guisado-Clavero Margarita Cabrera-Bean Francesc Formiga Jose Maria Valderas Albert Roso-Llorach |
| author_facet | Concepción Violán Quintí Foguet-Boreu Sergio Fernández-Bertolín Marina Guisado-Clavero Margarita Cabrera-Bean Francesc Formiga Jose Maria Valderas Albert Roso-Llorach |
| author_sort | Concepción Violán |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5445e9d9d67e45dc817aecc6f583dfde |
| institution | DOAJ |
| issn | 2044-6055 |
| language | English |
| publishDate | 2019-08-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-5445e9d9d67e45dc817aecc6f583dfde2025-08-20T03:11:03ZengBMJ Publishing GroupBMJ Open2044-60552019-08-019810.1136/bmjopen-2019-029594Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean populationConcepción Violán0Quintí Foguet-Boreu1Sergio Fernández-Bertolín2Marina Guisado-Clavero3Margarita Cabrera-Bean4Francesc Formiga5Jose Maria Valderas6Albert Roso-Llorach7Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Barcelona, SpainFundació Institut Universitari per a la recerca a l`Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, SpainFundació Institut Universitari per a la recerca a l`Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, SpainFundació Institut Universitari per a la recerca a l`Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, SpainSignal Theory and Communications Department, Universitat Politecnica de Catalunya, Barcelona, SpainHospital Universitari de Bellvitge, Barcelona, SpainHealth Services and Policy Research Group, University of Exeter Medical School, Exeter, UKFundació Institut Universitari per a la recerca a l`Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, SpainObjectives 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.https://bmjopen.bmj.com/content/9/8/e029594.full |
| spellingShingle | Concepción Violán Quintí Foguet-Boreu Sergio Fernández-Bertolín Marina Guisado-Clavero Margarita Cabrera-Bean Francesc Formiga Jose Maria Valderas Albert Roso-Llorach Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population BMJ Open |
| title | Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population |
| title_full | Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population |
| title_fullStr | Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population |
| title_full_unstemmed | Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population |
| title_short | Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population |
| title_sort | soft clustering using real world data for the identification of multimorbidity patterns in an elderly population cross sectional study in a mediterranean population |
| url | https://bmjopen.bmj.com/content/9/8/e029594.full |
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