ASSOCIATIONS BETWEEN HEART RATE VARIABILITY AND NEED FOR LIFESAVING INTERVENTION IN A LARGE HELICOPTER EMS SERVICE

Area of Science:

• Emergency Medicine
• Critical Care
• Cardiology

Background:

• Heart rate variability (HRV) reflects autonomic nervous system regulation of cardiac function.
• HRV analysis offers potential for optimizing prehospital triage of critically ill patients.

Purpose of the Study:

• To investigate if early HRV measures during emergency transport predict the need for prehospital lifesaving interventions (LSIs).
• To evaluate sample entropy and other HRV metrics in a large, heterogeneous cohort of critically ill patients.

Main Methods:

• Retrospective analysis of 4,521 patient records from a helicopter critical care transport service.
• Calculation of time, frequency, and complexity domain HRV measures from continuous ECG data.
• Application of multivariable logistic regression and machine learning (gradient boosting, random forest) for prediction.

Main Results:

• A significant association was found between HRV measures (including sample entropy) and the need for prehospital LSI (OR 0.50).
• Machine learning models, particularly gradient boosting and random forest, demonstrated strong predictive performance (AUROC 0.78-0.79).
• 68.8% of analyzed patients received a prehospital LSI.

Conclusions:

• Specific HRV measures are significantly associated with the requirement for prehospital LSIs.
• Further research should explore clinical utility and establish actionable thresholds for HRV-guided triage and intervention.