The role of heart rate variability in cardiac surgery: applications and innovations

Abstract

Background
Heart rate variability, the physiological variation in time intervals between successive heartbeats, is a valuable marker used to index the functionality of the cardiac Autonomic Nervous System in healthy individuals and patients with cardiovascular and non-cardiovascular disorders. In cardiac surgery, heart rate variability can be a crucial tool for the operative management of patients. This manuscript reviews the role of heart rate variability in surgery, its current applications, and emerging trends in clinical settings.


Main body
Clinically, heart rate variability is used to evaluate surgical risk by identifying patients with impaired autonomic function who may be predisposed to complications such as arrhythmias or hemodynamic instability. During surgery, heart rate variability monitoring provides real-time insights into autonomic responses to anesthesia, fluid management, and surgical stress. Postoperatively, heart rate variability is instrumental in detecting early signs of sepsis, myocardial dysfunction, and autonomic dysregulation, thereby guiding timely interventions. Despite its clinical potential, heart rate variability analysis faces challenges, including variability in measurement techniques, limited standardization of interpretation, and the influence of confounding factors such as medications and mechanical ventilation. Additionally, real-time integration into surgical workflows remains underdeveloped.


Conclusions
Emerging trends in heart rate variability in cardiac surgery include the use of artificial intelligence for automated heart rate variability analysis, wearable biosensors for continuous monitoring, and tailored therapeutics. There are also new advances in machine-learning algorithms for heart rate variability interpretation, which are promising for enhancing ischemia detection and refining real-time decision-making during high-risk cardiac procedures. Thus, future research should focus on refining heart rate variability–based predictive models and integrating heart rate variability metrics into multimodal perioperative management strategies to improve surgical outcomes.