Factor analysis applied to acute myocardial infarction

The authors examine the utility of factor analysis for identifying characteristics of a patient population that are reliably predictive of prognosis or useful for estimating risks of morbidity or mortality. This technique involves grouping the most highly correlated variables together and looking for patterns of association, with the goal of describing the data by a few fundamental variables. It is concluded that factor analysis allows reduction of a large number of variables to a smaller number of meaningful factors, integrates subjective data appropriately, facilitates identification of fundamental factors, and ensures that the factors are independent. The nine factors found that reduced the total variance appreciably were mobility, body mass, race/age, block/race, block/enzyme, mass/AV block, Latino, infarct location, and enzymes