Multimorbidity patterns with K-means nonhierarchical cluster analysis
Abstract 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 pat...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-07-01
|
| Series: | BMC Family Practice |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12875-018-0790-x |
| _version_ | 1828827746090876928 |
|---|---|
| author | Concepción Violán Albert Roso-Llorach Quintí Foguet-Boreu Marina Guisado-Clavero Mariona Pons-Vigués Enriqueta Pujol-Ribera Jose M. Valderas |
| author_facet | Concepción Violán Albert Roso-Llorach Quintí Foguet-Boreu Marina Guisado-Clavero Mariona Pons-Vigués Enriqueta Pujol-Ribera Jose M. Valderas |
| author_sort | Concepción Violán |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-12T15:07:30Z |
| format | Article |
| id | doaj.art-fda8c79805554acba49b9c8913baeaba |
| institution | Directory Open Access Journal |
| issn | 1471-2296 |
| language | English |
| last_indexed | 2024-12-12T15:07:30Z |
| publishDate | 2018-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Family Practice |
| spelling | doaj.art-fda8c79805554acba49b9c8913baeaba2022-12-22T00:20:42ZengBMCBMC Family Practice1471-22962018-07-0119111110.1186/s12875-018-0790-xMultimorbidity patterns with K-means nonhierarchical cluster analysisConcepción Violán0Albert Roso-Llorach1Quintí Foguet-Boreu2Marina Guisado-Clavero3Mariona Pons-Vigués4Enriqueta Pujol-Ribera5Jose M. Valderas6Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol)Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol)Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol)Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol)Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol)Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol)Health Services & Policy Research Group, Academic Collaboration for Primary Care, University of Exeter Medical SchoolAbstract 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.http://link.springer.com/article/10.1186/s12875-018-0790-xMultimorbidityCluster analysisMultiple correspondence analysisK-means clusteringPrimary health careElectronic health records |
| spellingShingle | Concepción Violán Albert Roso-Llorach Quintí Foguet-Boreu Marina Guisado-Clavero Mariona Pons-Vigués Enriqueta Pujol-Ribera Jose M. Valderas Multimorbidity patterns with K-means nonhierarchical cluster analysis BMC Family Practice Multimorbidity Cluster analysis Multiple correspondence analysis K-means clustering Primary health care Electronic health records |
| title | Multimorbidity patterns with K-means nonhierarchical cluster analysis |
| title_full | Multimorbidity patterns with K-means nonhierarchical cluster analysis |
| title_fullStr | Multimorbidity patterns with K-means nonhierarchical cluster analysis |
| title_full_unstemmed | Multimorbidity patterns with K-means nonhierarchical cluster analysis |
| title_short | Multimorbidity patterns with K-means nonhierarchical cluster analysis |
| title_sort | multimorbidity patterns with k means nonhierarchical cluster analysis |
| topic | Multimorbidity Cluster analysis Multiple correspondence analysis K-means clustering Primary health care Electronic health records |
| url | http://link.springer.com/article/10.1186/s12875-018-0790-x |
| work_keys_str_mv | AT concepcionviolan multimorbiditypatternswithkmeansnonhierarchicalclusteranalysis AT albertrosollorach multimorbiditypatternswithkmeansnonhierarchicalclusteranalysis AT quintifoguetboreu multimorbiditypatternswithkmeansnonhierarchicalclusteranalysis AT marinaguisadoclavero multimorbiditypatternswithkmeansnonhierarchicalclusteranalysis AT marionaponsvigues multimorbiditypatternswithkmeansnonhierarchicalclusteranalysis AT enriquetapujolribera multimorbiditypatternswithkmeansnonhierarchicalclusteranalysis AT josemvalderas multimorbiditypatternswithkmeansnonhierarchicalclusteranalysis |