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Cardiovascular disease epidemiology: Lessons for clinical medicine
PREVENTIVE
MEDICINE
12, 144- 145 (1983)
Cardiovascular
Disease Epidemiology: Clinical Medicine’ VITTORIO
Lessons
for
PUDDU
Via Savoia 80, 00198 Rome, Italy
It is both a pleasure and an honor to address this symposium dedicated to the worldwide promotion of cardiovascular epidemiology. As a clinician and a student of epidemiologists, I received my first lesson in epidemiology when discussing an experience concerning high serum cholesterol as a risk factor for myocardial infarction (MI). Looking at the level of serum cholesterol in a group of MI cases, I found that the majority of these cases had apparently normal values. The epidemiologists explained that, as cholesterol is a biological variable, individuals having levels different from the mean of the population are less and less frequent as the values move further and further from the mean. Suppose, for example, that out of a total of 1000 individuals, 200 show a low, 600 a medium, and 200 a high cholesterol level. Suppose that the relationship between infarction and cholesterol is a geometric one. We will have, after a given period of time, 4 new cases of infarction in the low cholesterol group, 24 in the medium cholesterol group, and 16 in the high cholesterol group. The clinician observes only the cases of infarction and concludes that there is no relationship between cholesterol and infarction. The epidemiologist, however, knows the proportion of cases in each group of the population and concludes that infarction is more frequent in the subjects with high cholesterol. Both the clinician and the epidemiologist have made correct observations, but the material was different: one selective, the other representative. The conclusion of the clinician is wrong; the conclusion of the epidemiologist is right. More recently I received another lesson from epidemiologists when, more than 200 years after its discovery, the Bayes’ theorem was brought to the attention of clinicians. As everybody knows, according to the Bayes’ theorem, the value of a test is influenced by the prevalence of the disease in the tested population. Since the probability of having ischemic heart disease is very low in young women, the probability of false positive electrocardiographic exercise stress tests is very high. Thus many diagnoses of coronary disease made in the past in young women with atypical chest pain and a positive electrocardiographic stress test were certainly false diagnoses. 1 Adapted from introductory remarks presented at the International Symposium and prevention of Atherosclerotic Disease, June 24-26, 1981, Anacapri, Italy. 144 0091-7435/83/010144-02$03.00/O Copyright AU rights
@ 1983 by Academic Press, Inc. of reproduction in any form reserved.
on Epidemiology
SYMPOSIUM:
ATHEROSCLEROTIC
DISEASE
145
I could bring before you other examples of lessons of epidemiology to clinicians: the introduction of the statistical evaluation of clinical and therapeutic experimentation, the selection of statistically representative groups of cases, etc. But, as a clinician, I must also present to the epidemiologists the embarrassment in which I find myself when I must answer a cardiac patient who asks me what his survival probability is. My first answer is that the mean survival is, for example, 5 years. The patient tells me that the mean represents a compromise between the minimum and maximum values of survival and he would like to know in which extremity he is. At that point, I have to step from epidemiology to clinical medicine to look at many details of the patient’s history, symptoms, and other signs which are generally neglected in an epidemiological survey. Again, as a clinician, it was dismaying to learn that the London School Questionnaire, in which only effort angina is considered, was used for epidemiological surveys on coronary heart disease. It was explained that rest angina was not considered because the diagnosis is uncertain, and the significance of the results is not changed due to the loss of these data. Based on clinical experience, especially that of the last few years which demonstrates that spontaneous angina is not rare at all, I would disagree with that data omission. As such examples show, neither epidemiology nor clinical medicine is sufficient in itself to determine individual patient care. The presentations that follow illustrate the continuing interactions between the two.
MEDICINE
12, 144- 145 (1983)
Cardiovascular
Disease Epidemiology: Clinical Medicine’ VITTORIO
Lessons
for
PUDDU
Via Savoia 80, 00198 Rome, Italy
It is both a pleasure and an honor to address this symposium dedicated to the worldwide promotion of cardiovascular epidemiology. As a clinician and a student of epidemiologists, I received my first lesson in epidemiology when discussing an experience concerning high serum cholesterol as a risk factor for myocardial infarction (MI). Looking at the level of serum cholesterol in a group of MI cases, I found that the majority of these cases had apparently normal values. The epidemiologists explained that, as cholesterol is a biological variable, individuals having levels different from the mean of the population are less and less frequent as the values move further and further from the mean. Suppose, for example, that out of a total of 1000 individuals, 200 show a low, 600 a medium, and 200 a high cholesterol level. Suppose that the relationship between infarction and cholesterol is a geometric one. We will have, after a given period of time, 4 new cases of infarction in the low cholesterol group, 24 in the medium cholesterol group, and 16 in the high cholesterol group. The clinician observes only the cases of infarction and concludes that there is no relationship between cholesterol and infarction. The epidemiologist, however, knows the proportion of cases in each group of the population and concludes that infarction is more frequent in the subjects with high cholesterol. Both the clinician and the epidemiologist have made correct observations, but the material was different: one selective, the other representative. The conclusion of the clinician is wrong; the conclusion of the epidemiologist is right. More recently I received another lesson from epidemiologists when, more than 200 years after its discovery, the Bayes’ theorem was brought to the attention of clinicians. As everybody knows, according to the Bayes’ theorem, the value of a test is influenced by the prevalence of the disease in the tested population. Since the probability of having ischemic heart disease is very low in young women, the probability of false positive electrocardiographic exercise stress tests is very high. Thus many diagnoses of coronary disease made in the past in young women with atypical chest pain and a positive electrocardiographic stress test were certainly false diagnoses. 1 Adapted from introductory remarks presented at the International Symposium and prevention of Atherosclerotic Disease, June 24-26, 1981, Anacapri, Italy. 144 0091-7435/83/010144-02$03.00/O Copyright AU rights
@ 1983 by Academic Press, Inc. of reproduction in any form reserved.
on Epidemiology
SYMPOSIUM:
ATHEROSCLEROTIC
DISEASE
145
I could bring before you other examples of lessons of epidemiology to clinicians: the introduction of the statistical evaluation of clinical and therapeutic experimentation, the selection of statistically representative groups of cases, etc. But, as a clinician, I must also present to the epidemiologists the embarrassment in which I find myself when I must answer a cardiac patient who asks me what his survival probability is. My first answer is that the mean survival is, for example, 5 years. The patient tells me that the mean represents a compromise between the minimum and maximum values of survival and he would like to know in which extremity he is. At that point, I have to step from epidemiology to clinical medicine to look at many details of the patient’s history, symptoms, and other signs which are generally neglected in an epidemiological survey. Again, as a clinician, it was dismaying to learn that the London School Questionnaire, in which only effort angina is considered, was used for epidemiological surveys on coronary heart disease. It was explained that rest angina was not considered because the diagnosis is uncertain, and the significance of the results is not changed due to the loss of these data. Based on clinical experience, especially that of the last few years which demonstrates that spontaneous angina is not rare at all, I would disagree with that data omission. As such examples show, neither epidemiology nor clinical medicine is sufficient in itself to determine individual patient care. The presentations that follow illustrate the continuing interactions between the two.