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BMJ Publishing Group, Postgraduate Medical Journal, 1158(98), p. 294-299, 2021

DOI: 10.1136/postgradmedj-2020-139361

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Description of multimorbidity clusters of admitted patients in medical departments of a general hospital

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

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.