Description of multimorbidity clusters of admitted patients in medical departments of a general hospital
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Description of multimorbidity clusters of admitted patients in medical departments of a general hospitalAutor(es)
Fecha
2021-02-05Cita bibliográfica
Matesanz-Fernández M, Seoane-Pillado T, Iñiguez-Vázquez I, Suárez-Gil R, Pértega-Díaz S, Casariego-Vales E. Description of multimorbidity clusters of admitted patients in medical departments of a general hospital. Postgrad Med J. 2022;98(1158):294-299
Resumen
[Abstract] Objective: We aim to identify patterns of disease clusters among inpatients of a general hospital and to describe the characteristics and evolution of each group.
Methods: We used two data sets from the CMBD (Conjunto mínimo básico de datos - Minimum Basic Hospital Data Set (MBDS)) of the Lucus Augusti Hospital (Spain), hospitalisations and patients, realising a retrospective cohort study among the 74 220 patients discharged from the Medic Area between 01 January 2000 and 31 December 2015. We created multimorbidity clusters using multiple correspondence analysis.
Results: We identified five clusters for both gender and age. Cluster 1: alcoholic liver disease, alcoholic dependency syndrome, lung and digestive tract malignant neoplasms (age under 50 years). Cluster 2: large intestine, prostate, breast and other malignant neoplasms, lymphoma and myeloma (age over 70, mostly males). Cluster 3: malnutrition, Parkinson disease and other mobility disorders, dementia and other mental health conditions (age over 80 years and mostly women). Cluster 4: atrial fibrillation/flutter, cardiac failure, chronic kidney failure and heart valve disease (age between 70-80 and mostly women). Cluster 5: hypertension/hypertensive heart disease, type 2 diabetes mellitus, ischaemic cardiomyopathy, dyslipidaemia, obesity and sleep apnea, including mostly men (age range 60-80). We assessed significant differences among the clusters when gender, age, number of chronic pathologies, number of rehospitalisations and mortality during the hospitalisation were assessed (p<0001 in all cases).
Conclusions: We identify for the first time in a hospital environment five clusters of disease combinations among the inpatients. These clusters contain several high-incidence diseases related to both age and gender that express their own evolution and clinical characteristics over time.
Palabras clave
General medicine
Health policy
Internal medicine
Health policy
Internal medicine
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Atribución-NoComercial-SinDerivadas 3.0 España
ISSN
0032-5473