Diagnosis Of Anterior Myocardial Infarction Research Articles

The established electrocardiographic classification of anterior wall myocardial infarction misguides clinicians in terms of infarct location, extent and prognosis.

The currently used scheme for the classification of infarct location and extent in anterior myocardial infarction (MI) is intuitive rather than being evidence-based, and recent evidence suggests that it may be misleading both in anatomic and prognostic sense. Consecutive patients with the diagnosis of anterior MI were enrolled. All electrocardiograms (ECG) were first classified according to established scheme and then reassessed using newer criteria for angiographic site of occlusion. The site of left anterior descending (LAD) occlusion was determined using multiple angiographic views. Clinic, echocardiographic and angiographic outcomes were compared. A total of 379 anterior MI cases were enrolled, final study population consisted of 267 patients. The established scheme did not predict infarct size or adverse outcomes. Location of the myocardium subtended by the occluded coronary network did not match with the anatomic location as ECG classification implies. Many high-risk patients with proximal LAD were classified as "anteroseptal", whereas the majority of the patients labeled as "extensive anterior MI" had in fact distal occlusions. On the other hand, expert interpretation was fairly accurate in predicting adverse outcomes and the site of angiographic involvement. Classifying patients according to the established scheme neither gives prognostic information nor accurately localizes infarction. It should be regarded as obsolete and its use should be abandoned. Instead, the extent of infarction can be inferred from newer criteria provided by the angiographic correlation studies.

Thrombus-in-transit causing paradoxical embolism in cerebral and coronary arterial circulation.

We describe the case of an elderly man who was admitted to our hospital with the diagnosis of an acute anterior myocardial infarction, together with an acute bioccipital ischaemic stroke. Coronary angiography revealed embolization of the distal left anterior descendens and the first septal branch. The definite diagnosis of paradoxical embolism was made by means of a transoesophageal echocardiography which demonstrated a large snake-like floating thrombus crossing a patent foramen ovale. This diagnosis was also supported by the presence of deep venous thrombosis as a probable origin of the intra-atrial thrombus and the secondary pulmonary embolism which contributed to the elevated right heart pressure.The patient was treated with full-dose heparin and subsequently oral anticoagulation. After discharge, follow-up by transoesophageal echocardiography was organized and once the intracardiac thrombus had disappeared, elective transcatheter closure of the patent foramen ovale was performed.

Artificial neural networks for the electrocardiographic diagnosis of healed myocardial infarction

Artificial neural networks are computer-based expert systems that learn by example, in contrast to the currently used rule-based electrocardiographic interpretation programs. For the purpose of this study, 1,107 electrocardiograms (ECGs) from parents who had undergone cardiac catheterization were used to train and test neural networks for the diagnosis of myocardial infarction. Different combinations of QRS and ST-T measurements were used as input to the neural networks. In a learning process, the networks automatically adjusted their characteristics to correctly diagnose anterior or inferior wall myocardial infarction from the ECG. Two thirds of the ECGs were used in this process. Thereafter, the performance of the networks was studied in a separate test set, using the remaining third of the ECGs. The results from the networks were also compared with that of conventional electrocardiographic criteria. The sensitivity for the diagnosis of anterior myocardial infarction was 81% for the best network and 68% for the conventional criteria (p <0.01), both having a specificity of 97.5%. The corresponding sensitivities of the network and the criteria for the diagnosis of inferior myocardial infarction were 78% and 65.5% (p <0.01), respectively, compared at a specificity of 95%. The results indicate that artificial neural networks may be of interest in the attempt to improve computer-based electrocardiographic interpretation programs.

Poor R wave progression in the precordial leads: Clinical implications for the diagnosis of myocardial infarction

A definite diagnosis of anterior myocardial infarction is often difficult to make in patients when a pattern of poor R wave progression in the precordial leads is present on the electrocardiogram. The purpose of this study was to determine whether a mathematical model could be devised to identify patients with anterior infarction among 102 consecutive patients with poor R wave progression. Each patient underwent exercise testing with thallium scanning. The diagnosis of anterior infarction was established in 20 (20%) of the 102 patients by the presence of fixed thallium-201 perfusion defects in the anterior wall or septum, or both. With the use of a multivariate stepwise discriminant analysis of clinical and electrocardiographic variables, five variables (sex, ST-T changes, S wave amplitude in leads V2 and V3 and the sum of the R wave amplitude in leads V3 and V4) that were statistically significant by univariate analysis were selected by the model to identify patients with anterior infarction (sensitivity 85%, specificity 71%). The discriminant model was subsequently applied prospectively to an additional 21 patients with poor R wave progression and provided a sensitivity of 85% and a specificity of 88%. Thus, anterior infarction (fixed thallium-201 defects in the anteroseptal segments) was present in 20% of patients with poor R wave progression in the precordial leads; and a mathematical model can be used to identify a subset of patients with anterior infarction in a group of patients with poor R wave progression.

Vectorcardiographic criteria for the diagnosis of anterior myocardial infarction.

Frank lead vectorcardiograms (VCG) from four carefully selected patient subgroups (226 patients) were analyzed to develop optimal criteria for the diagnosis of anterior myocardial infarction. Specificity was evaluated using 100 healthy volunteers under age 30 and 80 patients with normal left ventriculogram and normal coronary arteriograms. Sensitivity was determined using 25 patients with evolutionary ST-T wave changes (V1-2), and LDH and CPK isoenzyme evidence of acute myocardial infarction; and 21 patients with anterior wall akinesia or dyskinesia and greater than 70% occlusion of the left anterior descending coronary artery. Patients with VCG evidence of bundle branch block, left or right ventricular hypertrophy were excluded. The criterion for the diagnosis of anterior myocardial infarction which was found to give the highest sensitivity with greater than or equal to 95% specificity was: initial anterior QRS forces must not exceed 0.1 mV in maximal anterior amplitude and also must not exceed 24 msec in duration. The performance of this proposed criterion was then tested using four similarly defined patient subgroups consisting of a total of 222 patients. The incidence of false positive diagnosis in these test subgroups was less than 1% with a sensitivity of greater than 95%. The overall performance of the proposed criterion was found to be significantly superior to both the widely accepted VCG and ECG criteria for anterior myocardial infarction. Thus, this quantitative criterion using both time and duration of initial anterior forces is both a highly specific and a sensitive indicator of anterior myocardial infarction.

St-qR pattern: new sign for diagnosis of anterior myocardial infarction during right ventricular pacing.

St-qR pattern: new sign for diagnosis of anterior myocardial infarction during right ventricular pacing.