ECG myocardial infarct size: a gender-, age-, race-insensitive 12-segment multiple regression model. I: Retrospective learning set of 100 pathoanatomic infarcts and 229 normal control subjects.

Published

Journal Article

In this early study of ongoing work with multiple regression modeling for mapping myocardial infarct (MI) into 12 left ventricular (LV) segments, promising results have been presented using electrocardiographic (ECG) 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. For version I, building 12 regression models using 68 single and 32 multiple 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. Version II predictions of MI in 12 LV segments for each subject were used to calculate the predicted total percentage LV infarct, which correlated 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 together. With refinements of the input ECG variables to include (1) improvement in the GARI-QRS variables, (2) adding a significant number of subjects with hypertrophies and conduction defects with and without MI to an expanded learning set, and (3) applying the enhanced 12-LV-segment regression models to a similar test set, it is to be expected that these regression models can be improved even further in such a way as to be applicable to general clinical populations using routine computerized ECG analysis programs.

Full Text

Cited Authors

  • Selvester, RH; Wagner, GS; Ideker, RE; Gates, K; Starr, S; Ahmed, J; Crump, R

Published Date

  • January 1, 1994

Published In

Volume / Issue

  • 27 Suppl /

Start / End Page

  • 31 - 41

PubMed ID

  • 7884373

Pubmed Central ID

  • 7884373

Electronic International Standard Serial Number (EISSN)

  • 1532-8430

International Standard Serial Number (ISSN)

  • 0022-0736

Digital Object Identifier (DOI)

  • 10.1016/s0022-0736(94)80041-3

Language

  • eng