May autonomic indices from cardiovascular variability help identify hypertension?

Area of Science:

• Cardiovascular Physiology
• Autonomic Nervous System Function
• Hypertension Research

Background:

• Hypertensive individuals exhibit altered heart rate (RR) variability and reduced baroreflex gain.
• These autonomic markers serve as key indicators of cardiovascular health.

Purpose of the Study:

• To determine if autonomic proxies can identify clinical hypertension using logistic models.
• To assess the predictive power of autonomic markers in distinguishing hypertensive and normotensive groups.

Main Methods:

• An observational study involving 405 participants (155 mild hypertensive, 250 controls).
• Statistical analysis included descriptive statistics, logistic regression modeling, variable selection, and concordance analysis.
• Cross-validation techniques were employed to refine predictive models.

Main Results:

• The most complete logistic model (Model D), incorporating autonomic indices (RR variability, baroreflex gain), age, and sex, achieved the highest accuracy (82.7%).
• Predictive performance improved with model complexity, with Model D showing a positive predictive value (PPV) of 0.767 and negative predictive value (NPV) of 0.866.
• A parsimonious set of autonomic proxies (Mean RR, ΔRRLFnu, baroreflex gain) combined with age and sex demonstrated high discriminant and predictive power (accuracy 80.5%).

Conclusions:

• A small subset of indirect autonomic proxies, alongside age and sex, can effectively identify hypertensive and normotensive groups.
• The clinical utility of these findings warrants further investigation.
• Autonomic profiling shows promise for non-invasive hypertension detection.