Discussion
This study aimed to examine the rates of ECG abnormalities among patients with suspected HCM in comparison to controls in a sample of individuals presenting for echocardiographic screening as part of the Anthony Bates Foundation’s screening programs. Our primary hypotheses were supported, indicating that individuals with suspected HCM have significantly higher rates of LVH and T wave inversion. Aggregated analysis showed that HCM identified on echocardiography was also associated with a significantly higher rate of any ECG abnormality compared to controls. However, despite significant differences from control subjects, rates of any ECG abnormality among HCM patients were still low, at less than 25%. Taken together, our findings suggest that though HCM is associated with greater rates of ECG abnormalities compared to controls, due to low rates of ECG abnormality among this population, ECG alone is likely considerably lacking in sensitivity for the identification of HCM. In sum, though ECG abnormalities may suggest potential HCM, they are insufficient for successful prediction and identification of the condition in the absence of additional confirmatory markers.
Our findings are consistent with research demonstrating that ECG alone has inadequate sensitivity for the diagnosis of HCM. However, some studies have used automation to improve upon sensitivity in identifying HCM. For example, one study discovered that an automated algorithm exhibited 88.6% sensitivity and 98% specificity in identifying patients with HCM based on ECG data. Another study concentrated on classifying HCM patients through ECG analysis. These insights indicate that while ECG can provide valuable information, it may not be comprehensive enough for accurate HCM identification when used in isolation (27–29). Despite advancements, at present the diagnosis of HCM still must involve a combination of tests including echocardiography, genetic testing, and clinical evaluation.
However, some emerging research has shown that novel ECG techniques may have the potential to improve predictive validity. For example, some studies suggest that integrating ECG monitoring techniques with machine learning and novel analytical strategies has the potential to improve the predictive validity of ECG in identifying HCM. This advancement may enhance the effectiveness of current therapies for individuals with this condition. Furthermore, artificial intelligence-enhanced electrocardiography (AI-ECG) has shown potential utility for the diagnosis of HCM. These developments offer promise for improving the management and diagnosis of individuals with HCM (30,31).
At present, the gold standard for the identification of HCM is echocardiography, specifically 2D echocardiography or CMR imaging for adult patients (12). These imaging techniques are crucial for establishing a clinical diagnosis of HCM (12). In cases where HCM is suspected due to cardiac symptoms, an abnormal 12-lead ECG, or a family history of inherited heart disease, and where the echocardiographic examination is inconclusive, CMR imaging is a crucial supplementary test for establishing a clear diagnosis (32–38). In such clinical scenarios, CMR imaging can pinpoint specific areas of LVH, especially when hypertrophy is localized to particular regions of the LV wall, such as the anterolateral wall, posterior septum, and apex. The enhanced sensitivity of CMR imaging in detecting LVH is due to its high spatial resolution and the absence of limitations caused by poor acoustic windows resulting from pulmonary or thoracic parenchyma (35–37). Some studies state that the most predictive modality for diagnosing HCM is CMR imaging. This imaging technique offers detailed insights into the heart’s structure and function, enabling accurate evaluation of myocardial hypertrophy, fibrosis, and other pathological changes linked to HCM. Its exceptional visualization and characterization of the myocardium make it an invaluable tool for diagnosing HCM and gauging its severity. Furthermore, CMR imaging aids in risk assessment and assists in determining the most suitable treatment approach for individuals with HCM (33,39). Despite advancements in ECG technology and novel approaches for the use of ECG data via automation and artificial intelligence-based models, echocardiography is still warranted for confirmation and CMR remains a useful tool in cases of inconclusive echocardiography.