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OBJECTIVES: The electrocardiogram (ECG) is the most commonly used tool to screen for left ventricular hypertrophy (LVH), and yet current diagnostic criteria are insensitive in modern increasingly overweight society. We propose a simple adjustment to improve diagnostic accuracy in different body weights and improve the sensitivity of this universally available technique. METHODS: Overall, 1295 participants were included-821 with a wide range of body mass index (BMI 17.1-53.3 kg/m(2)) initially underwent cardiac magnetic resonance evaluation of anatomical left ventricular (LV) axis, LV mass and 12-lead surface ECG in order to generate an adjustment factor applied to the Sokolow-Lyon criteria. This factor was then validated in a second cohort (n=520, BMI 15.9-63.2 kg/m(2)). RESULTS: When matched for LV mass, the combination of leftward anatomical axis deviation and increased BMI resulted in a reduction of the Sokolow-Lyon index, by 4 mm in overweight and 8 mm in obesity. After adjusting for this in the initial cohort, the sensitivity of the Sokolow-Lyon index increased (overweight: 12.8% to 30.8%, obese: 3.1% to 27.2%) approaching that seen in normal weight (37.8%). Similar results were achieved in the validation cohort (specificity increased in overweight: 8.3% to 39.1%, obese: 9.4% to 25.0%) again approaching normal weight (39.0%). Importantly, specificity remained excellent (>93.1%). CONCLUSIONS: Adjusting the Sokolow-Lyon index for BMI (overweight +4 mm, obesity +8 mm) improves the diagnostic accuracy for detecting LVH. As the ECG, worldwide, remains the most widely used screening tool for LVH, implementing these findings should translate into significant clinical benefit.

Original publication




Journal article



Publication Date





1566 - 1572


Action Potentials, Adult, Aged, Body Mass Index, Electrocardiography, Female, Heart Conduction System, Heart Rate, Humans, Hypertrophy, Left Ventricular, Magnetic Resonance Imaging, Male, Middle Aged, Obesity, Predictive Value of Tests, Reproducibility of Results, Retrospective Studies, Signal Processing, Computer-Assisted