TY - JOUR
T1 - Multimorbidity patterns with K-means nonhierarchical cluster analysis
AU - Violán, Concepción
AU - Roso-Llorach, Albert
AU - Foguet-Boreu, Quintí
AU - Guisado-Clavero, Marina
AU - Pons-Vigués, Mariona
AU - Pujol-Ribera, Enriqueta
AU - Valderas, Jose M.
PY - 2018/7/3
Y1 - 2018/7/3
N2 - © 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.
AB - © 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.
KW - Cluster analysis
KW - Diseases
KW - Electronic health records
KW - K-means clustering
KW - Multimorbidity
KW - Multiple correspondence analysis
KW - Primary health care
U2 - 10.1186/s12875-018-0790-x
DO - 10.1186/s12875-018-0790-x
M3 - Article
VL - 19
JO - BMC Family Practice
JF - BMC Family Practice
SN - 1471-2296
IS - 1
M1 - 108
ER -