ECG MYOCARDIAL INFARCT SIZE - A GENDER-INSENSITIVE, AGE-INSENSITIVE, RACE-INSENSITIVE 12-SEGMENT MULTIPLE-REGRESSION MODEL .1. RETROSPECTIVE LEARNING SET OF 100 PATHOANATOMIC INFARCTS AND 229 NORMAL CONTROL SUBJECTS

In this early study of ongoing work with multiple regression modeling for mapping myocardial infarct (MI) into 12 left ventricular (LV) segm ents, promising results have been presented using electrocardiographic (EGG) QRS variables that are gender, age, and race insensitive (GARI) , the GARI-QRS 12-segment multiple regression model. These include Q, R, and S duration, expressed as percentage total QRS duration, and R/Q duration, R/Q amplitude, R/S duration, and R/S amplitude variables. F or version I, building 12 regression models using 68 single and 32 mul tiple MIs, the GARI-QRS variables correlated with pathoanatomic MI in each of 12 segments with r values ranging from .67 to .88. In version II of the model, using all MIs and 229 normal subjects, r = .73-.91. V ersion II predictions of MI in 12 LV segments for each subject were us ed to calculate the predicted total percentage LV infarct, which corre lated well with that found at autopsy. The r values found were .81 for all single MIs, .73 for multiple MIs, and .80 for all MIs taken toget her. With refinements of the input ECG variables to include (1) improv ement in the GARI-QRS variables, (2) adding a significant number of su bjects with hypertrophies and conduction defects with and without MI t o an expanded learning set, and (3) applying the enhanced 12-LV-segmen t regression models to a similar test set, it is to be expected that t hese regression models can be improved even further in such a way as t o be applicable to general clinical populations using routine computer ized ECG analysis programs.