Prognostic Features In Acute Myocardial Infarction908-109 Incremental Prognostic Value of Electrocardiographic Findings when Added to Baseline Clinical Variables in Patients with Acute Myocardial Infarction
Review articleOpen access
Abstract:

Risk assessment for patients with acute myocardial infarction is critical in facilitating appropriate therapeutic decision making and resource utilization. Prior investigation of the GUSTO I database has identified 16 baseline clinical variables that independently and accurately predict 3D-day mortality. We investigated the ability of the presenting electrocardiogram (ECG) to increase the predictive capacity of the clinical data, with a particular focus on the significance of ST segment shifts. We examined a 32,812 patient subset of the 41,021 pt enrolled in GUSTO I. Exclusionary criteria included absence of baseline ECG. LBBB, unknown 30-day mortality, <0.1 mV maximum ST elevation, paced or ventricular rhythm. 30-day follow-up was greater than 99.5% complete. Candidate ECG variables included LVH, RVH, ABBB. left anterior and left posterior hemiblock (LAHB, LPHB), prior MI in a distinct anatomic location, maximum ST elevation in anyone lead, sum of ST elevation in all leads, number of leads with ≥0.1 mV ST elevation (NUMLEAD), sum of the absolute ST deviation from baseline in all leads (SUMDEV). Multivariable logistic regression modelling of the ECG data alone showed SUMDEV, Prior MI, NUMLEAD, ABBB and LAHB to provide independent prognostic information. Stepwise addition of the ECG variables to the clinical data indicated independent prognostic information in SUMDEV and ABBB (×2 = 184). The predictive value of this information was of similar magnitude to heart rate and MI location and was an order of magnitude larger than that for time to treatment, diabetes mellitus or smoking.Multivariable Stepwise ECG ModelECG VariableAdjusted X2*SUMDEV229Prior MI89NUMLEAD54RBBB50LAHB16*-p < 0.001 for eachConclusionsElectrocardiographic variables add important information to baseline clinical and demographic data capable of improving early risk stratification of thrombolytic treated patients with acute myocardial infarction. Appropriate use of this information should improve clinical decision making and resource utilization.

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