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Title: Graphing comorbidity.
Epworth Authors: McKenzie, Dean
Fahey, Michael
Gwini, Stella
Ponsford, Jennie
Downing, Marina
Sultana, Ron
Barrett, Jonathan
McConchie, Steven
Other Authors: Dipnall, Joanna
Keywords: Comorbidity
Comorbid Disorders
Synergistic Relationships
Graphic Representation
Regression Methods
Decisions Trees
Heat Maps
Charlson Comorbidity Index
Elixhauser Comorbidity Index
Stata Software
Microsoft Excel
R (Programming Language)
Factor Analysis
Latent Class Analysis
Self-Organising Maps
Illustrative Medical Data
Intensive Care Unit, Epworth HealthCare, Victoria, Australia.
Clinical Institutes and Medical Audit, Epworth HealthCare, Victoria, Australia
Epworth Prostate Centre, Epworth Healthcare, Victoria, Australia
Emergency Department, Epworth HealthCare, Melbourne, Victoria, Australia
Epworth Research Institute, Epworth HealthCare, Victoria, Australia
Issue Date: Jun-2017
Citation: Epworth Research Institute Research Week 2017; Poster 26: pp 50
Conference Name: Epworth Research Institute Research Week 2017
Conference Location: Epworth HealthCare Research Institute, Victoria, Australia
Abstract: INTRODUCTION: Coined in the1970s by physician, epidemiologist and applied statistician Prof. Alvan Feinstein MD (1925-2001), the term "comorbidity" may be defined as the presence of two or more disorders, such as alcohol use disorder and depression, or colorectal cancer and chronic obstructive pulmonary disease. Comorbidity or "multimorbidity" is generally associated with increased use of hospital services and poorer prognosis, due to additive (higher number of disorders generally indicates poorer health) and multiplicative (synergistic or interactive) relationships between the disorders. An example of such a synergistic relationship is patients with lower limb injuries and comorbid diabetes mellitus not being able to manage and control the latter, due to restricted movement. Instruments based upon weighted counts of chronic disorders such as the Charlson, and Elixhauser indices are predictive of mortality and other adverse outcomes but provide little information on specific synergistic co-relationships between comorbid disorders. Various regression methods, as well as decision trees (a favourite tool of Dr. Feinstein), can quantify such relationships and predict outcome, however there are very few methods of graphically representing co-morbidity. AIMS: To present a simple and easy to understand method of graphing comorbidity. METHADOLOGY: "Heat maps", a twentieth century development of a nineteenth century concept, can show in vivid colour or in monochrome the overall strength of relationships between two or more disorders in a sample of patients. Heat maps are less complex than network graphs, provide more information than pie charts and Venn diagrams and are readily implemented in R, SPSS, Stata and Microsoft Excel. Graphs such as heat maps allow effective communication with clinicians and may act as a conduit to further analysis, including factor analysis, latent class analysis, and self-organizing maps. RESULTS: Illustrative published medical data and custom charts and graphs will be presented. [See Poster].
Type: Conference Poster
Affiliated Organisations: Department of Statistics, Data Science and Epidemiology at Swinburne University of Technology
Monash University
Deakin University
Type of Clinical Study or Trial: Review
Appears in Collections:Health Informatics
Research Month

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