Identification of optimal electrocardiographic criteria for the diagnosis of unrecognized myocardial infarction: A population-based study

Background: Despite using the same tool (ECG), the proportion of myocardial infarctions that goes unrecognized varies from 20% to 60% in population-based studies. The reasons for such wide variations have not been studied. We sought to evaluate the effect of ECG-MI criteria and study methodology on the prevalence of unrecognized myocardial infarction (UMI) and to identify the optimal ECG-MI criteria for UMI detection in epidemiologic studies. Methods: A random population-based sample of 2042 adults, age ≥45 years, underwent history, medical record abstraction and ECG. Six different ECG-MI criteria and two subjective recognized myocardial infarction (RMI) identification criteria, from different published studies, were applied to the same survey ECG. The operating test characteristics of different criteria were compared with the objective criterion standard of a RMI by Gillum criteria. Results: The UMI proportion estimates varied from 32% to 61% due to variation in ECG-MI criteria, while keeping the study population, MI recognition criteria, and ECG constant. Subjective criteria for Ml recognition had limited value (positive predictive value of 44-93%) in picking up RMI. Depending on the ECG abnormality used to define MI, ECG reading had widely varying sensitivity (21-37%; P < 0.0001) with consistently high specificity (92-97%) for detection of RMI. Conclusions: The prevalence estimates of UMI vary widely and are strongly dependent on the ECG-MI and MI recognition criteria. Future studies of UMI should explicitly recognize this variation and select the ECG-MI criteria that match their study aims.