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dc.contributor.authorMatesanz Fernández, María
dc.contributor.authorSeoane-Pillado, Teresa
dc.contributor.authorIñiguez-Vázquez, Iria
dc.contributor.authorSuárez-Gil, Roi
dc.contributor.authorPértega-Díaz, Sonia
dc.contributor.authorCasariego Vales, Emilio
dc.date.accessioned2022-05-18T10:19:56Z
dc.date.available2022-05-18T10:19:56Z
dc.date.issued2021-02-05
dc.identifier.citationMatesanz-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):294-299es_ES
dc.identifier.issn0032-5473
dc.identifier.urihttp://hdl.handle.net/2183/30685
dc.descriptionReviewes_ES
dc.description.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.es_ES
dc.language.isoenges_ES
dc.publisherBMJes_ES
dc.relation.urihttp://dx.doi.org/10.1136/postgradmedj-2020-139361es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectGeneral medicinees_ES
dc.subjectHealth policyes_ES
dc.subjectInternal medicinees_ES
dc.titleDescription of multimorbidity clusters of admitted patients in medical departments of a general hospitales_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitlePostgraduate Medical Journales_ES
UDC.volume98es_ES
UDC.issue1158es_ES
UDC.startPage294es_ES
UDC.endPage299es_ES


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