What are the criteria for diagnosing Left Ventricular (LV) Hypertrophy?

From the Guidelines

Left ventricular hypertrophy (LVH) can be diagnosed using electrocardiogram (ECG) criteria, with the most recent and highest quality study recommending the use of Sokolow-Lyon, Cornell voltage, Cornell product, and Romhilt-Estes point score systems 1. The most commonly used and widely accepted criteria are:

• Sokolow-Lyon criteria: S wave in V1 + R wave in V5 or V6 ≥ 35 mm
• Cornell voltage criteria:
  • For men: S wave in V3 + R wave in aVL > 28 mm
  • For women: S wave in V3 + R wave in aVL > 20 mm
• Cornell product: (Cornell voltage × QRS duration) ≥ 2,440 mm·ms
• Romhilt-Estes point score system: A score of 5 or more indicates LVH When using these criteria, it's essential to consider that:

• ECG criteria have high specificity but lower sensitivity for detecting LVH, as noted in the 2009 study 1
• Echocardiography is more accurate and is often used to confirm the diagnosis, as recommended in the 2020 guideline 1
• Other factors like obesity, COPD, and chest wall deformities can affect ECG voltage
• The presence of bundle branch blocks can make LVH diagnosis more challenging These criteria are based on the principle that increased left ventricular mass leads to higher QRS voltage and longer QRS duration. However, ECG changes may lag behind actual anatomical changes, so serial ECGs or additional imaging may be necessary in some cases. Some key points to consider in the diagnosis of LVH include:

• The existence of many different criteria for diagnosing LVH makes clinical application more complex, as noted in the 2009 study 1
• The sensitivity and specificity of each criterion are different, and the diagnostic accuracy will depend on the specific criterion used
• Patients who meet one set of criteria for LVH commonly do not meet other criteria, highlighting the importance of using multiple criteria in diagnosis
• The value of multiple criteria may be additive, and automated systems should apply multiple criteria to improve diagnostic accuracy 1

From the Research

Criteria for Diagnosing Left Ventricular Hypertrophy

The criteria for diagnosing Left Ventricular (LV) Hypertrophy include:

• Electrocardiographic (ECG) criteria such as Cornell voltage, Cornell voltage product, Sokolow-Lyon voltage, Sokolow-Lyon product, RaVL, RaVL+SV3, RV6/RV5 ratio, strain pattern, left atrial enlargement, and QT interval 2
• Echocardiographically derived left ventricular mass indexed to body surface area, using sex-dependent thresholds 3
• Point-scoring systems such as the Romhilt-Estes, Perugia, and Glasgow-Royal-Infirmary modified Romhilt-Estes score 3
• Measuring the amplitude of the deepest S wave (SD) in any single lead and adding it to the S wave amplitude of lead V4 (SV4) 4
• A scoring system consisting of 5 criteria, including amplitudes of Q in V3, R in V6, S in V3, T in V6, P' in V1, P in V6, as well as R and T-axis discordance, R peak time in V6, QRS duration, weight, height, sex, and age 5

Performance of ECG Criteria

The performance of ECG criteria for diagnosing LVH varies, with:

• Sensitivity ranging from 7.6% to 40.9% and specificity ranging from 70.2% to 99.2% in patients without complete left bundle branch block (CLBBB) 2
• Sensitivity ranging from 11.9% to 95.2% and specificity ranging from 6.6% to 96.6% in patients with CLBBB 2
• The Sokolow-Lyon criteria with voltage ≥3.0mV showing a sensitivity of 22.2% and specificity of 88.3% in patients with CLBBB 2
• A scoring system consisting of 5 criteria showing a sensitivity of 67.9% and specificity of 81.4% in the derivation cohort, and 62.5% and 83.2% in the validation cohort, respectively 5

Comparison of Criteria

Comparing the performance of different criteria, the:

• Point-scoring systems, such as the Perugia score and the Glasgow Royal Infirmary modified Romhilt-Estes score, were found to be the most accurate, with sensitivities of 22% and 24%, respectively, at 95% specificity 3
• Proposed SD + SV4 criteria outperformed Cornell voltage, with a significantly higher sensitivity of 62% compared to 35% 4
• Back propagation neural network (BPN) model using ECG signals showed a testing accuracy, precision, sensitivity, and specificity of 0.961,0.958,0.966, and 0.956, respectively, outperforming 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH 6