title: A new method to incorporate age and gender into the criteria for the detection of acute inferior myocardial infarction.
abstract: Recent studies have shown that younger women are more likely to die during and after hospitalization for acute myocardial infarction (MI) than older women and men of all ages. This may be partly due to incorrect diagnosis or late detection of acute MI in younger women. At high specificity levels (>98%), the sensitivity of the initial ECG to detect acute MI may be as low as 30% when using traditional criteria by both physicians and computerized interpretation programs. This study examines if women of different age groups have a similar ECG presentation to men during acute inferior MI and if the diagnostic accuracies of the initial ECG are comparable. We analyzed chest pain ECGs from Mayo Clinic and Medical College of Wisconsin, which included 1,339 patients with acute inferior MI and 1,169 age-matched controls with noncardiac chest pain. We subdivided all groups by age (below and above 60 years) and compared ECG parameters (ST elevation, ST depression, QRS duration, R-wave amplitude, Q-wave duration and amplitude, QT interval) between genders. For inferior MI patients under age 60, women had lower ST elevations at the J point in lead II than men (57 +/- 91 microV vs. 86 +/- 117 microV, P < .02). This trend was reversed for patients over age 60 (lead a VF: 102 +/- 126 microV vs. 84+/-117 microV, P < .04; Lead III: 130+/-146 microV vs. 103+/-131 microV, P < .007). A neural network method was used to identify the most significant group of ECG parameters for detecting acute MI. An adaptive fuzzy logic method was developed for adapting to the threshold differences among the different gender and age groups. The new algorithm improved the sensitivity of acute inferior MI detection by more than 25% relative to old algorithm, while maintaining the high specificity around 98% for noncardiac chest pain patients.
authors.affiliation: GE Medical Systems-Information Technologies, Milwaukee, WI 53223, USA.
authors.forename: J
authors.initials: J
authors.lastname: Xue
authors.forename: B
authors.initials: B
authors.lastname: Taha
authors.forename: S
authors.initials: S
authors.lastname: Reddy
authors.forename: R S
authors.initials: RS
authors.lastname: Wright
authors.forename: T
authors.initials: T
authors.lastname: Aufderheide
mesh_terms: D000367:Age Factors; D000368:Aged; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000453:epidemiology; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D012737:Sex Factors