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Description of multimorbidity clusters of admitted patients in medical departments of a general hospital
  1. María Matesanz-Fernández1,
  2. Teresa Seoane-Pillado2,
  3. Iria Iñiguez-Vázquez1,
  4. Roi Suárez-Gil1,
  5. Sonia Pértega-Díaz3,
  6. Emilio Casariego-Vales1
  1. 1Medicina Interna, Hospital Universitario Lucus Augusti, Lugo, Spain
  2. 2Área de Medicina Preventiva y Salud pública, Departamento de Ciencias de la Salud, Universidade da Coruña, A Coruña, Spain
  3. 3Unidad de Epidemiología Clínica y Bioestadística, Complexo Hospitalario Universitario A Coruña—Instituto de Investigación Biomédica, Complexo Hospitalario Universitario A Coruña, A Coruña, Spain
  1. Correspondence to Dr María Matesanz-Fernández, Internal Medicine, Hospital Universitario Lucus Augusti, lugo 27002, Spain; maria.matesanz.fernandez{at}sergas.es

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.

  • general medicine (see internal medicine)
  • health policy
  • internal medicine

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Footnotes

  • Contributors EC-V and MM-F: design of study and data interpretation. IÍ-V and RS-G: data interpretation. TS-P and SP-D: statistical analysis.

  • Funding María Matesanz-Fernández acknowledges a FEMI grant for young researchers from the Sociedad Española de Medicina Interna.

  • Competing interests Declaration of competing interest: The authors declare that there are no conflicts of interest regarding this research and publication.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available upon reasonable request. The main data source was the registry of hospitalisation entries, which includes all diagnoses carried out by the healthcare professional in charge, codified (by codifying medics) using the CIE.9MC. As additional data sources we used the nursing registries and since 2007 the computerised database IANUS, which gathers all data derived from medical assistance. The study protocols were approved by the ComitéÉtico de Investigación Clínica de Galicia (Ethics Committee of Clinic Research of Galicia; registry code CEIC of Galicia 2014/409). A computerised database of each hospitalization event using all the aforementioned registries was created. It includes a list of all the hospitalisations and their associated main and secondary medical diagnoses that were thought to be the cause of the hospitalisations.

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