Real-Time Comparison of Machine Learning-Enabled Devices for Measuring Compensatory Reserve Status

Area of Science:

• Physiology
• Biomedical Engineering
• Medical Devices

Background:

• Central hypovolemia is a critical condition requiring accurate monitoring.
• Existing methods for assessing compensatory reserve may have limitations in real-time application.
• Noninvasive photoplethysmography offers a potential avenue for continuous physiological monitoring.

Purpose of the Study:

• To compare the real-time performance of a novel compensatory reserve measurement (CRM) device against an established compensatory reserve index (CRI) device.
• To validate the CRM device using data from a simulated central hypovolemia study.
• To assess the potential of the CRM device for early detection of compensatory status changes.

Main Methods:

• A clinical research study involving simulated central hypovolemia induced by lower body negative pressure (LBNP).
• Data acquisition using noninvasive photoplethysmography sensors from 20 participants.
• Comparison of CRM and CRI algorithmic outputs using median error, median absolute error, and pooled Pearson correlations.
• Bland-Altman analysis to assess bias between the two devices.

Main Results:

• Strong linear agreement between CRM and CRI with a pooled correlation R² = 0.859.
• Near-zero mean bias (0.13%) between the CRM and CRI devices, indicating high concordance.
• CRM provided early measurement times (mean 20.52 min) comparable to CRI (mean 17.92 min).

Conclusions:

• The compensatory reserve measurement (CRM) device demonstrates statistically similar performance to the FDA-cleared compensatory reserve index (CRI) device.
• Both devices effectively track compensatory status changes during progressive central hypovolemia.
• Real-time tracking of compensatory changes using CRM holds significant value for continuous monitoring and triage in prehospital trauma settings.