Diagnostic Accuracy of Left Ventricular Hypertrophy in Patients with Myocardial Infarction by Computer-Assisted Electrocardiography (ELECTROPRES)


  • This work was supported by an unrestricted grant from Sanofi-Aventis which also supported the development and implementation of the platform ELECTROPRES.

Address for correspondence: Luis Rodríguez-Padial, M.D., Ph.D., F.E.S.C., Cardiac Unit, Hospital Virgen de la Salud, 45005 Toledo, Spain. Fax: +34925269149; E-mail: lrodriguez@sescam.org



Information is limited about the classification accuracy of electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH) in the presence of myocardial infarction (MI).


We evaluated LVH classification accuracy for a set of 16 ECG criteria and some combinations derived from them in 1642 patients (105 with MI) suspected of coronary heart disease with two-dimensional echocardiography evaluation and a standard 12-lead ECG recorded at the same time. Patients with left bundle branch block had previously been excluded. Measures of classification accuracy included sensitivity, specificity, likelihood ratios, and positive and negative predictive values.


Diagnostic accuracy varied widely for different LVH criteria. The criteria with the best overall performance had highest sensitivity in the presence of MI and sensitivities of approximately 30% with relatively low specificities ranging from 72% to 78%. However, the classification accuracy for them was similar to that for patients without MI. The prevalence of LVH in patients with MI was higher (56%) than in those with no MI (31%). Classification accuracy of the best single previously published LVH criteria was comparable to that of the best combinations of any three of them.


The classification accuracy of LVH criteria in the presence of MI is comparable to that in patients without MI, in part possibly due to the higher LVH prevalence in the MI group. The presence of a well-validated computer database facilitates comparative evaluation of ECG-LVH criteria and derivation of optimal combinations of criteria for any given clinical application.