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.