Abstract 4361929: Improving Early Detection of Hypertrophic Cardiomyopathy Using Viz-HCM: A Quality Improvement Initiative Leveraging Artificial Intelligence-Assisted EKG HCM Screening

Background: Hypertrophic cardiomyopathy (HCM) remains underdiagnosed despite an estimated prevalence of 1 in 500 in the general population. Due to its often asymptomatic presentation and reliance on advanced imaging for diagnosis, many cases go undetected for years. Traditional diagnostic approaches are time-intensive and often miss subtle signs, except in select subtypes like apical HCM with characteristic EKG features. There is a critical need for more efficient, scalable screening methods. Aim: To implement and evaluate the use of the artificial intelligence algorithm VIZ-HCM for early identification of previously undiagnosed HCM cases via electrocardiogram (EKG) analysis across the UTMB system by March 2025, with the goal of reducing diagnostic delays and facilitating earlier risk stratification to <2 months. Measure: Primary measures included the number of new HCM cases identified, time from EKG flag to provider evaluation and diagnosis, and time to sudden cardiac death (SCD) risk stratification profile completion. Intervention: VIZ-HCM was retrospectively applied to all EKGs performed in 2024 through November to identify known HCM cases previously enrolled in UTMB HCM Registry. Sensitivity and specificity of the algorithm were evaluated, yielding a sensitivity of 90% and a specificity of 70%. After establishing the performance of the Viz-HCM algorithm in our system, prospective screening initiative using VIZ-HCM was initiated from November 2024 to March 25, 2025. Results: A total of 48 newly diagnosed HCM patients were identified during the prospective phase. Time from EKG flag to diagnostic confirmation was reduced from years—as noted in historical literature—to a mean of 37 days. Similarly, the timeline for developing individualized SCD risk stratification profiles was significantly shortened. Conclusion: Implementation of VIZ-HCM significantly enhanced early detection of HCM and accelerated the timeline for diagnosis and risk stratification. Ongoing efforts are focused on expanding this screening initiative system-wide across all four campuses and 22 outpatient clinics within UTMB, with the ultimate aim of improving long-term outcomes for patients with HCM.

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Journal
Circulation
Published
2025-11-03
DOI
https://doi.org/10.1161/circ.152.suppl_3.4361929
Primary Topic
ECG Monitoring and Analysis
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article
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Abstract 4361929: Improving Early Detection of Hypertrophic Cardiomyopathy Using Viz-HCM: A Quality Improvement Initiative Leveraging Artificial Intelligence-Assisted EKG HCM Screening

Aiham Albaeni, Khaled Alabdallah, Deaa Abu Jazar, Omar Abdelfattah et al.
Circulation
ECG Monitoring and Analysis
article

Abstract 4361929: Improving Early Detection of Hypertrophic Cardiomyopathy Using Viz-HCM: A Quality Improvement Initiative Leveraging Artificial Intelligence-Assisted EKG HCM Screening

Aiham Albaeni, Khaled Alabdallah, Deaa Abu Jazar, Omar Abdelfattah, Khaled Chatila, Wissam Khalife
article en

Abstract

Background: Hypertrophic cardiomyopathy (HCM) remains underdiagnosed despite an estimated prevalence of 1 in 500 in the general population. Due to its often asymptomatic presentation and reliance on advanced imaging for diagnosis, many cases go undetected for years. Traditional diagnostic approaches are time-intensive and often miss subtle signs, except in select subtypes like apical HCM with characteristic EKG features. There is a critical need for more efficient, scalable screening methods. Aim: To implement and evaluate the use of the artificial intelligence algorithm VIZ-HCM for early identification of previously undiagnosed HCM cases via electrocardiogram (EKG) analysis across the UTMB system by March 2025, with the goal of reducing diagnostic delays and facilitating earlier risk stratification to <2 months. Measure: Primary measures included the number of new HCM cases identified, time from EKG flag to provider evaluation and diagnosis, and time to sudden cardiac death (SCD) risk stratification profile completion. Intervention: VIZ-HCM was retrospectively applied to all EKGs performed in 2024 through November to identify known HCM cases previously enrolled in UTMB HCM Registry. Sensitivity and specificity of the algorithm were evaluated, yielding a sensitivity of 90% and a specificity of 70%. After establishing the performance of the Viz-HCM algorithm in our system, prospective screening initiative using VIZ-HCM was initiated from November 2024 to March 25, 2025. Results: A total of 48 newly diagnosed HCM patients were identified during the prospective phase. Time from EKG flag to diagnostic confirmation was reduced from years—as noted in historical literature—to a mean of 37 days. Similarly, the timeline for developing individualized SCD risk stratification profiles was significantly shortened. Conclusion: Implementation of VIZ-HCM significantly enhanced early detection of HCM and accelerated the timeline for diagnosis and risk stratification. Ongoing efforts are focused on expanding this screening initiative system-wide across all four campuses and 22 outpatient clinics within UTMB, with the ultimate aim of improving long-term outcomes for patients with HCM.

CirculationVol. 152(Suppl_3)
Friends University (US), Texas Medical Center (US), Clear Creek Independent School District (US), The University of Texas Medical Branch at Galveston (US)
Openalex Percentile: Top 51%
ECG Monitoring and Analysis
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