Heart rate variability in cardiovascular disease diagnosis, prognosis and management

Area of Science:

• Cardiology
• Autonomic Nervous System Research
• Biomarker Discovery

Background:

• Heart rate variability (HRV), the variation in intervals between heartbeats, reflects autonomic nervous system function.
• Reduced HRV is linked to cardiovascular diseases (CVD), but its prognostic value is debated.
• Existing research presents mixed findings on HRV's role in CVD outcomes.

Purpose of the Study:

• To review the diagnostic, prognostic, and therapeutic applications of HRV in cardiovascular disease.
• To explore the potential of wearable technology and machine learning in HRV analysis for CVD.
• To identify challenges and future research directions for clinical adoption of HRV in cardiovascular care.

Main Methods:

• Systematic literature review of studies on HRV and cardiovascular disease.
• Evaluation of HRV's predictive capabilities for cardiac events and outcomes.
• Assessment of emerging technologies like wearables and AI in HRV monitoring and analysis.

Main Results:

• HRV can detect early autonomic dysfunction and predict outcomes like sudden cardiac death and myocardial infarction.
• HRV shows potential in monitoring comorbid conditions (e.g., heart failure, depression) impacting cardiovascular risk.
• Wearable devices and machine learning enhance HRV monitoring and analysis precision, aiding personalized treatment.

Conclusions:

• HRV is an emerging biomarker with potential in personalized cardiovascular care.
• Technological advancements are expanding HRV's utility, but require further clinical validation.
• Standardization, overcoming measurement variability, and demonstrating incremental prognostic value are key challenges for widespread adoption.