# Heart rate variability in cardiovascular disease diagnosis, prognosis and management

## TL;DR

Heart rate variability (HRV) is a potential biomarker for diagnosing, predicting outcomes, and managing cardiovascular diseases by reflecting autonomic nervous system function.

## Contribution

This review systematically evaluates HRV's roles in diagnosis, prognosis, and management of cardiovascular disease, emphasizing recent advancements in wearable tech and machine learning.

## Key findings

- HRV can detect autonomic dysfunction early and predict adverse outcomes like sudden cardiac death.
- Wearable technology enables continuous HRV monitoring, improving its clinical utility.
- Machine learning enhances HRV analysis precision but requires validation in large trials.

## Abstract

Heart rate variability (HRV), the variation in intervals between consecutive heartbeats, reflects autonomic nervous system function and has been studied as a potential biomarker in cardiovascular disease (CVD). While reduced HRV has been linked to arrhythmias, heart failure, and ischaemic heart disease, findings across studies are mixed and its prognostic value remains debated. This review evaluates HRV's diagnostic, prognostic, and therapeutic roles in CVD. HRV can reveal autonomic dysfunction early, predict outcomes such as sudden cardiac death and recurrent myocardial infarction, and track recovery after cardiac events. It also shows promise in monitoring comorbid conditions like heart failure and depression that exacerbate cardiovascular risk. Advancements in wearable technology and machine learning are expanding HRV's potential. Wearable devices enable continuous, non-invasive HRV monitoring, while machine learning algorithms enhance the precision and predictive power of HRV analysis. These innovations may facilitate real-time data collection and tailored treatment plans, though their clinical utility requires validation in larger, prospective trials. Key challenges remain, including measurement variability, lack of standardisation, and limited incremental prognostic value over established risk factors. This review highlights HRV's emerging role in personalised cardiovascular care while acknowledging the substantial research needed before widespread clinical adoption.

Heart rate variability (HRV) in cardiovascular disease. HRV reflects autonomic regulation of cardiac function and has been studied across three clinical domains: diagnostic applications (autonomic dysfunction, early disease detection, risk stratification), prognostic value (prediction of adverse events, post–myocardial infarction risk, treatment monitoring), and management tools (biofeedback therapy, pharmacological modulation, and lifestyle interventions). Advances in wearable monitoring enable longitudinal HRV assessment, while future directions include artificial intelligence–assisted analysis and personalised medicine. Box breathing is shown as an example of a structured breathing technique used in HRV biofeedback.Heart rate variability in cardiovascular disease infographic. Central circle labeled \"Heart Rate Variability\" connected to sections: Diagnostic Uses (autonomic dysfunction, early disease detection, risk stratification), Prognostic Value (predict adverse events, post-myocardial infarction risk assessment, monitor treatment efficacy), and Management Tools (biofeedback therapy, pharmacological modulation, lifestyle intervention). Includes wearable monitoring and box breathing visuals. Future Directions mention AI integration and personalized medicine.

Heart rate variability (HRV) in cardiovascular disease. HRV reflects autonomic regulation of cardiac function and has been studied across three clinical domains: diagnostic applications (autonomic dysfunction, early disease detection, risk stratification), prognostic value (prediction of adverse events, post–myocardial infarction risk, treatment monitoring), and management tools (biofeedback therapy, pharmacological modulation, and lifestyle interventions). Advances in wearable monitoring enable longitudinal HRV assessment, while future directions include artificial intelligence–assisted analysis and personalised medicine. Box breathing is shown as an example of a structured breathing technique used in HRV biofeedback.