Could clustering of comorbidities be useful for better defining the internal medicine patients’ complexity?

  • Flavio Tangianu Internal Medicine, S. Martino Hospital, Oristano, Italy.
  • Paola Gnerre Internal Medicine, San Paolo Hospital, Savona, Italy.
  • Fabrizio Colombo Internal Medicine, Niguarda Ca’ Granda Hospital, Milano, Italy.
  • Roberto Frediani Internal Medicine, Maggiore Hospital, Chieri (TO), Italy.
  • Giuliano Pinna Internal Medicine, Cardinal Massaia Hospital, Asti, Italy.
  • Franco Berti Internal Medicine 2 Department, S. Camillo Forlanini Hospital, Roma, Italy.
  • Giovanni Mathieu Internal Medicine, E. Agnelli Hospital, Pinerolo (TO), Italy.
  • Micaela La Regina Internal Medicine, Clinical Risk Manager, La Spezia, Italy.
  • Francesco Orlandini Health Director, ASL 4 Regione Liguria, Italy.
  • Antonino Mazzone Medical Department, Internal Medicine, ASST Ovest-Milanese, Legnano (MI), Italy.
  • Clelia Canale Internal Medicine, S.S. Annunziata Hospital, Savigliano (CN), Italy.
  • Daniele Borioni Internal Medicine, Maggiore Hospital, Bologna, Italy.
  • Roberto Nardi | nardidoc48@gmail.com Internal Medicine, Maggiore Hospital, Bologna, Italy.

Abstract

Internal medicine patients are mostly elderly with multiple comorbidities, usually chronic. The high prevalence of comorbidity and multimorbidity has a significant impact on both positive responses to treatment and the occurrence of adverse events. Clustering is the process of nosography grouping into meaningful associations with some index disease, so that the objects within a cluster have high similarity in comparison with one another. In the decision-making process it is imperative that, in addition to understanding the immediate clinical problems, we are able to explicit all the contextual factors that have to be taken into account for the best outcome of care. Cluster analysis could be leveraged in developing better interventions targeted to improve health outcomes in subgroups of patients.

Downloads

Download data is not yet available.
Published
2018-06-20
Section
Brief Reports
Keywords:
Internal medicine patients, multi/comorbidity, complexity, cluster analysis.
Statistics
Abstract views: 1038

PDF: 308
HTML: 304
Share it

PlumX Metrics

PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.

How to Cite
Tangianu, F., Gnerre, P., Colombo, F., Frediani, R., Pinna, G., Berti, F., Mathieu, G., La Regina, M., Orlandini, F., Mazzone, A., Canale, C., Borioni, D., & Nardi, R. (2018). Could clustering of comorbidities be useful for better defining the internal medicine patients’ complexity?. Italian Journal of Medicine, 12(2), 137-144. https://doi.org/10.4081/itjm.2018.940

Most read articles by the same author(s)

1 2 > >>