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Validation of a population coronary disease predictive system: the CASSANDRA model
  1. Cindy A Kermott1,
  2. Kent R Bailey2,
  3. Janet E Olson3
  1. 1Department of Preventive, Occupational, Aerospace Medicine, Mayo Clinic, Rochester, Minnesota, USA
  2. 2Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
  3. 3Department of Epidemiology, Mayo Clinic, Rochester, Minnesota, USA
  1. Correspondence to Dr Cindy A Kermott, Department of Preventive, Occupational, Aerospace Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; kermott.cindy{at}mayo.edu

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Accurate prediction allows us to plan. This prediction capability has been made possible through the understanding of epidemiological principles of causation, while relying on statistical tools, or models, using an unbiased sample from a representative population in order to make an accurate claim. In the research work of Grau et al, an attempt had been undertaken to validate a prediction tool to estimate the disease burden, or risk, of coronary heart disease (CHD) should certain and known modifiable risk factors be altered given a public health initiative aimed at disease reduction.

The risk factors identified include those that are non-modifiable (age, gender) and those that may be modified (smoking, hypertension, diabetes, blood pressure). These had been originally defined in the Framingham Heart Study with 10-year risk of CHD contingent on the role of each of these factors. Risk of disease had been determined …

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