Using heart rate variability to develop a predictive model for post-operative cardiovascular complications following...

Area of Science:

• Cardiology
• Medical Informatics
• Physiology

Background:

• Heart Rate Variability (HRV) reflects autonomic nervous system regulation of heart rhythm.
• Deviations in HRV correlate with adverse clinical outcomes, particularly under physiological stress.
• Perioperative cardiovascular complications significantly increase mortality in abdominal surgery patients.

Purpose of the Study:

• To develop a predictive model for post-operative cardiovascular complications using HRV parameters.
• To enable early risk stratification and inform clinical decision-making in surgical patients.
• To utilize easily acquired HRV data for improved patient management.

Main Methods:

• Recruited adult patients undergoing elective major abdominal surgery in High Dependency Units.
• Monitored HRV via ECG at pre-operative, immediate post-operative, and post-operative day 1 time points.
• Developed a multivariable logistic regression model incorporating stepwise selection of HRV predictors and patient age.

Main Results:

• A predictive model combining three post-operative HRV parameters and patient age achieved an AUC of 0.980.
• The model demonstrated a negative predictive value of 1.00 at a specific probability cut-off.
• Analysis included 89 patients, with 8 experiencing cardiovascular complications within 7 days.

Conclusions:

• The developed HRV-based model shows potential for rapid clinical decision-making and patient triage.
• Early risk stratification using this model may facilitate safe early discharge or step-down care.
• Easily acquired HRV parameters offer a valuable tool for assessing post-operative cardiovascular risk.