title: Association between clusters of diseases and polypharmacy in hospitalized elderly patients: results from the REPOSI study.
abstract: BACKGROUND
  Although the association between multimorbidity and polypharmacy has been clearly documented, no study has analyzed whether or not specific combinations of diseases influence the prescription of polypharmacy in older persons. We assessed which clusters of diseases are associated with polypharmacy in acute-care elderly in-patients.
  
  
  METHODS
  This cross-sectional study was held in 38 Italian internal medicine and geriatric wards participating in the Registro Politerapie SIMI (REPOSI) study during 2008. The study sample included 1155 in-patients aged 65 years or older. Clusters of diseases, defined as two or more co-occurring specific chronic diseases, were identified using the odds ratio (OR) for the associations between pairs of diseases followed by cluster analysis. Polypharmacy was defined as the prescription of five or more different medications at hospital discharge. Logistic regression models were run to analyze the association between clusters of diseases and polypharmacy.
  
  
  RESULTS
  Among clusters of diseases, the highest mean number of drugs (>8) was found in patients affected by heart failure (HF) plus chronic obstructive pulmonary disease (COPD), HF plus chronic renal failure (CRF), COPD plus coronary heart disease (CHD), diabetes mellitus plus CRF, and diabetes mellitus plus CHD plus cerebrovascular disease (CVD). The strongest association between clusters of diseases and polypharmacy was found for diabetes mellitus plus CHD plus CVD, diabetes plus CHD, and HF plus atrial fibrillation (AF).
  
  
  CONCLUSIONS
  The observed knowledge of the relationship among co-occurring diseases and polypharmacy should help to identify and monitor older in-patients at risk of polypharmacy.
authors.affiliation: Mario Negri Institute for Pharmacological Research, Milano, Italy. alessandro.nobili@marionegri.it
authors.forename: Alessandro
authors.initials: A
authors.lastname: Nobili
authors.forename: Alessandra
authors.initials: A
authors.lastname: Marengoni
authors.forename: Mauro
authors.initials: M
authors.lastname: Tettamanti
authors.forename: Francesco
authors.initials: F
authors.lastname: Salerno
authors.forename: Luca
authors.initials: L
authors.lastname: Pasina
authors.forename: Carlotta
authors.initials: C
authors.lastname: Franchi
authors.forename: Alfonso
authors.initials: A
authors.lastname: Iorio
authors.forename: Maura
authors.initials: M
authors.lastname: Marcucci
authors.forename: Salvatore
authors.initials: S
authors.lastname: Corrao
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Licata
authors.forename: Pier Mannuccio
authors.initials: PM
authors.lastname: Mannucci
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D016000:Cluster Analysis; D015897:Comorbidity; D003430:Cross-Sectional Studies; D003704:Dementia / Q000453:epidemiology; D003920:Diabetes Mellitus / Q000453:epidemiology*; D004700:Endocrine System Diseases / Q000453:epidemiology; D005260:Female; D005767:Gastrointestinal Diseases / Q000453:epidemiology; D005853:Geriatrics; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007388:Internal Medicine; D016015:Logistic Models; D008171:Lung Diseases / Q000453:epidemiology; D008297:Male; D009017:Morbidity; D009369:Neoplasms / Q000453:epidemiology; D019338:Polypharmacy; D015995:Prevalence; D012307:Risk Factors