Early Detection in HCM: Can Structured AI Follow-Up Reduce Clinical Deterioration?

Hypertrophic Cardiomyopathy (HCM) is a progressive, inherited condition that requires lifelong vigilance. Although HCM is well-documented in research, many hospitals still struggle to ensure consistent long-term monitoring for patients

For hospitals, the primary challenge is shifting from reactive care to proactive, continuous monitoring.

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Traditional HCM Monitoring: Key Challenges

HCM is far more prevalent than many assume, potentially affecting 1 in 200 individuals globally. However, even for patients within the healthcare system, traditional monitoring models face inherent structural challenges in capturing true clinical status:

• The Normalization of Symptoms: Patients adapt their lifestyle and often report feeling “fine,” even when subtle symptoms progress.
• The Asymptomatic Trap: Younger patients often remain symptom-free until a serious cardiac event occurs. Without frequent touchpoints, early warning signs are easily missed.
• The Follow-Up Burden: Cardiology departments are overstretched. Conducting monthly or quarterly check-ins for thousands of at-risk patients is logistically impossible with human resources alone.
• Nonspecific Warning Signs: Mild fatigue, palpitations, or shortness of breath are often dismissed by patients as stress or aging, unless a structured clinical questionnaire is applied.

These monitoring gaps are reflected in higher rates of hospitalizations, complex interventions, and increased healthcare utilization. Quantifying this burden highlights the need for early and structured follow-up.

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What Is the Clinical and Economic Burden of HCM?

The burden of HCM extends well beyond the individual patient, affecting healthcare systems through increased hospitalizations, complex interventions, and long-term management costs.

Clinical Burden

A 2024 study showed that 45.4% of hospitalized HCM patients experience heart failure, which significantly increases mortality risks.⁸ In Spain, data from the EORP Registry indicates that atrial fibrillation, often detected only after symptoms become severe, affects nearly one-third of these patients, dramatically increasing stroke risk.⁵

Economic Burden: Data from Spain

A landmark 2025 study published in Revista Española de Cardiología, co-authored by Dr. Juan Ramón Gimeno from Hospital Virgen de la Arrixaca (Murcia), offers the first real-world analysis of oHCM costs in Spain.

The data shows a clear escalation across NYHA functional classes (a 1-to-4 scale that measures how much heart symptoms limit a patient's daily activity): annual costs per patient range from €4,430 at NYHA I to €17,545 at NYHA IV, a 4x increase driven by hospitalizations, ER visits, and invasive procedures.

The clinical implication is significant: if proactive, structured follow-up can help detect symptom progression early and keep patients at NYHA I–II, the potential to reduce both clinical risk and healthcare costs is substantial.

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Given the clear escalation in risk and cost across NYHA classes, early detection of symptom progression becomes a clinical priority.

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How Clinical Voice AI Transforms HCM Follow-Up

This is where emerging digital solutions, particularly Clinical Voice AI, offer a scalable complement to cardiology teams, proactively engaging with patients in natural and autonomous conversations.

Precision Monitoring with the HCMSQ Protocol

This transformation is driven by the application of the Hypertrophic Cardiomyopathy Symptom Questionnaire (HCMSQ) protocol through voice AI.

This structured assessment allows the AI to follow-up the patient by phone calls and provides the care team with a real-time snapshot of:

• Dyspnea: Precise tracking of shortness of breath according to NYHA functional classes.
• Chest Pain: Frequency and intensity of anginal symptoms.
• Pre-syncope & Syncope: Immediate detection of high-risk fainting or dizziness episodes.
• Fatigue & Palpitations: Real-world impact on the patient’s daily quality of life.

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Beyond Data Collection

By automating these check-ins, hospitals can:

1. Detect Early Warning Signals: Generate alerts for worsening symptoms before they lead to an ER visit.
2. Standardize Care: Ensure every patient receives the same high-quality follow-up, regardless of hospital volume.
3. Optimize Consultations: When the patient arrives for their appointment, the cardiologist spends less time "data gathering" and more time on high-value clinical decision-making.

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Conclusion: A Proactive Future for HCM Care

The hospitals benefiting most from AI are those that integrate it not as a standalone tool, but as an extension of the care team’s capacity.
By moving from a reactive, appointment-based model to proactive, AI-supported longitudinal follow-up, cardiology teams can close the gap between diagnosis and effective management, not just for HCM, but across a wide range of chronic or high-risk cardiovascular conditions.

Clinical Voice AI enables teams to:

• Engage patients consistently, even between visits
• Detect early warning signs and intervene sooner
• Standardize follow-up while reducing administrative burden
• Focus cardiologists’ expertise on high-value clinical decisions

The HCMSQ protocol for HCM demonstrates what’s possible, but similar structured follow-up approaches can be applied to heart failure, arrhythmias, post-MI care, or other chronic cardiac conditions, improving patient safety, adherence, and outcomes across cardiology.

Evolving Models for Longitudinal Cardiac Care

If your cardiology team is exploring ways to standardize follow-up in HCM or other cardiomyopathies, structured AI-supported monitoring may offer a scalable and clinically aligned solution. Contact us to learn more.

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References

1. Maron BJ, Gardin JM, et al. "Prevalence of hypertrophic cardiomyopathy in a general population of young adults." Circulation, 1995;92(4):785-789. (CARDIA Study)
2. ElHabr AK, Riello RJ III, Desai NR, et al. "Epidemiology of Hypertrophic Cardiomyopathy in the United States From 2016 to 2023." JACC: Advances, 2025. doi:10.1016/j.jacadv.2025.102552
3. Maron BJ. "How common is hypertrophic cardiomyopathy… really?: Disease prevalence revisited 27 years after CARDIA." American Heart Journal Plus, 2023. doi:10.1016/j.ahjo.2023.100288
4. CoulditbeHCM.com / Bristol-Myers Squibb disease awareness resource; based on Maron MS et al., Am J Cardiol, 2016.
5. Wybraniec MT, Gimeno JR, Charron P, et al. "Hypertrophic cardiomyopathy and atrial fibrillation: the EORP Cardiomyopathy Registry." European Heart Journal, 2025. PMC11836841
6. Barriales-Villa R, Escobar-López L, Vilanova Larena D, Salazar-Mendiguchía J, Echeto A, Hernández I, Rebollo-Gómez E, Gimeno JR. "Clinical management and healthcare resource utilization among patients with obstructive hypertrophic cardiomyopathy in Spain: a real-world study." Rev Esp Cardiol, 2025;78:1041-53. doi:10.1016/j.rec.2025.04.004
7. Arbelo E, Protonotarios A, Gimeno JR, et al. "2023 ESC Guidelines for the management of cardiomyopathies." European Heart Journal, 2023;44(37):3503-3626. doi:10.1093/eurheartj/ehad194
8. Ismail MF, Obeidat O, Abughazaleh S, et al. "Temporal trends, prevalence, predictors, and outcomes of heart failure in patients with hypertrophic cardiomyopathy in the United States: Insights from the National Inpatient Sample." Curr Probl Cardiol, 2024;49(8):102665. doi:10.1016/j.cpcardiol.2024.102665

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