Utilidad diagnóstica del ECG para predecir la geometría del ventrículo izquierdo por ecocardiograma

Abstract

Objective: To explore the diagnostic utility of 31 electrocardiogram (ECG) criteria for detecting echocardiographic (Echo) left ventricular geometry using accuracy. Methods: This cross-sectional study included consecutive adults (> 18 years) that were classified by Echo left ventricular geometry as normal (NL), concentric remodeling (CR), concentric hypertrophy (CH), and eccentric hypertrophy (EH). Thirty-one state-of-the-art ECG criteria for Echo left ventricular hypertrophy were calculated. AUC 95%CI, accuracy, sensitivity, specificity, and positive and negative predictive value for detecting Echo left ventricular geometries were compared. Multivariable linear regression models were produced using the ECG criteria as the dependent variable. Results: A total of 672 adults were included in the study. From 31 ECG criteria, Cornell (ECG21, SV3 + RaVL) and modified Cornell (ECG 31, RaVL + deepest S in all leads) criteria have the best overall AUC in differentiating NL versus CH (0.666 and 0.646), NL versus EH (0.686 and 0.656), CR versus CH (0.687 and 0.661), and CR versus EH (0.718 and 0.676). In multivariable linear regression models, CH and EH had the strongest effect on the final voltage in Cor-nell (ECG21) and modified Cornell (ECG31). Conclusions: From 31 state-of-the-art criteria, Cornell and modified Cornell criteria have the best AUC and accuracy for predicting most left ventricular geometries. CH and EH had the strongest effect on the voltage of Cornell and modified Cornell criteria compared to body mass index, age, diabetes, hypertension, and chronic heart disease. The ECG criteria poorly differentiate NL from CR and CH from EH.