[Risk stratification in the chest pain unit: an unresolved problem].

26 " I think that it is important that the doctor should study how to learn prognosis knowing and warning in advance that whoever lives and whoever dies, he will be able to avoid criticism. " Hippocrates, Aphorisms II.19 1 Since the earliest of times, knowing the prognosis of a sick individual has been a constant desire of doctors. When faced with the uncertainty that illness produces in the individual patient and their family, anguish forces us to gamble on the future. In recent years, risk stratification has acquired a new meaning as it has become the basis on which we choose the treatment for individual patients and is especially important when the treatment can save a life but entails a risk that on occasion may be greater than the expected benefit. The patient with chest pain attending an emergency department constitutes the essential paradigm: they may be at risk of imminent death or they may be suffering something quite ordinary; their treatment might involve cardiac intervention or simply require a tranquilizer. Establishing risk and prognosis, therefore, is a necessity. With the incorporation of statistical techniques into medical investigation numerous formulae were developed to predict the prognosis of patients with coronary disease. The first algorithms, described in patients with acute myocardial infarction by Schnur (1953), Peel (1962), and Norris (1969), which included only clinical variables, had some popularity but were seen to be somewhat impractical and to have a large margin of error. Later attempts, incorporating new clinical variables and hemodynamic or angiograp-hic parameters, are more exact but they also fail to resolve the problem of risk stratification. 2 In chest pain units, the problem of risk prediction is of particular interest and even has financial consequences. Consequently, new methods of prediction have been developed in an attempt to select those high-risk patients who require hospitalization and a more aggressive treatment and to discharge low-risk patients from the emergency department; results are far from satisfactory in this situation as well. Recently, Doukky and Calvin 3 highlighted the difficulties of developing a model to predict correctly. Firstly, it would have to be based on a large patient sample, representative of the clinical condition being studied. Moreover, it should analyze all the important variables and their independent contributions using adequate statistical techniques. The events that constitute the objective of the prediction should have clinical relevance: death, myocardial infarction or stroke, for example. …