z-logo
open-access-imgOpen Access
A Call for Universal Definitions in Cardiovascular Disease
Author(s) -
Joseph S. Alpert,
Kristian Thygesen
Publication year - 2006
Publication title -
circulation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.795
H-Index - 607
eISSN - 1524-4539
pISSN - 0009-7322
DOI - 10.1161/circulationaha.106.648030
Subject(s) - medicine , checklist , disease , university hospital , family medicine , gerontology , pathology , psychology , cognitive psychology
Defining concept or object enables humans to communicate effectively with each other concerning that defined entity. Medicine has struggled through its long history to define accurately the various diseases that are a daily component of the human condition. Accurate, clear, and easily interpreted definitions of a disease entity allow physicians to communicate among themselves and ultimately to explain to patients the implications of the specific conditions from which they suffer. The clinical scientist also requires an accurate definition of a specific disease. Often, a checklist is used to assist the investigator in identifying patients with the specific illness that is being studied. If the clinical scientist’s diagnostic criteria are accurate and reproducible, then similar patients with that disease entity can be entered into the clinical trial, the results of which may be generalized for the management of other patients who satisfy the same disease criteria. Furthermore, under favorable circumstances, results from one clinical trial can be compared and even combined with the results of other trials, as long as the same disease definition and criteria were used in the comparative investigations.Article p 790 Unfortunately, the ideal world of a universally understood and applied definition does not exist in the domain of acute myocardial infarction (MI). Over the many years that clinical research has been performed on patients with acute MI, different definitions using contemporary diagnostic tools have been used, and consequently it is often a challenge to compare the “apples” in 1 study to the “oranges” in another. Similar problems arise in the arena of public health statistics because physicians use different algorithms to define MI in the clinical setting, leading to inaccuracies when public health data are collated from discharge summaries. Thus, studies involving large databases that use hospital discharge diagnoses can contain significant inaccuracies because different physicians …

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom