The Heavy Heart: Unveiling the Risks of Left Ventricular Hypertrophy (LVH)
Left ventricular hypertrophy (LVH) is a pivotal clinical finding that warrants comprehensive evaluation to determine its etiology. In the general population, the prevalence of LVH is approximately 14.9% in men and 9.1% in women (Kannel et al., 1999). Differentiating between various causes of LVH - such as hypertensive heart disease, hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), and infiltrative cardiomyopathies like cardiac amyloidosis - is essential, as it directly impacts therapeutic strategies and prognostication. Echocardiography remains the cornerstone for diagnosing LVH; however, many patients are not referred for formal echocardiographic assessment, partly because certain causes of LVH may present with nonspecific, limited, or no symptoms.
LVH is also associated with an elevated risk of adverse cardiovascular events, including heart failure, arrhythmias, and stroke. Specifically, increased myocardial stiffness in patients with high-risk LVH is a hallmark of stage-B heart failure with preserved ejection fraction (HFpEF) (Hieda et al., 2020).
Point-of-care ultrasound (POCUS) offers an efficient means to establish the presence of LVH and can assist in differentiating among its causes, thereby facilitating timely and appropriate further workup. Nevertheless interpretation and measurement tend to be highly variable. The integration of artificial intelligence (AI) into POCUS, as exemplified by AISAP’s development of an AI platform significantly reduces variability in interpretation and increases confidence in the results. AISAP has developed an additional AI module (FDA pending) to accurately measure LVH from a single cardiac view. AI-assisted POCUS enables physicians, to rapidly and accurately assess LVH at the bedside generating a billable report. The AI has been fine-tuned to handle images from inexpensive devices obtained by non-experts.
By enabling accurate and immediate bedside assessment of LVH in any care setting and any ultrasound device, healthcare providers can identify at-risk patients earlier in the disease course, initiate appropriate workup, and potentially improve clinical outcomes. This approach will be particularly beneficial in underserved and underprivileged areas, where access to advanced imaging modalities may be limited.
If you’re exploring innovative approaches to improving cardiovascular diagnostics - or have feedback, ideas, or interest in collaborating around LVH detection and AI-powered POCUS - we’d love to hear from you.
Reach out to our team to join the conversation and help shape the future of bedside cardiac care.
