Experimental evaluation of a real-time implementation of compensatory reserve measurement in a human model of hemorrhagic shock

Introduction The leading cause of preventable traumatic death is hemorrhage. Early detection of hemorrhagic shock remains a critical challenge. For the early prediction of hemorrhagic shock-related cardiovascular decompensation, our team has developed the compensatory reserve measurement (CRM) algorithm. CRM uses a photoplethysmography waveform to quantify the body’s capacity to compensate during hypovolemia. This study focuses on the development and use of an application that can predict CRM in real-time (CRM RT ) during simulated hypovolemia experiments. Methods The CRM RT application was developed in Python to generate CRM predictions and highlight trend trajectories in real-time (RT). Data were collected during a human research protocol that was reviewed and approved by the Institutional Review Board. Participants (n = 20) meeting the inclusion criteria underwent a simulated hypovolemia procedure in a lower-body negative pressure chamber while wearing a Masimo® MightySat® Rx pulse oximeter. Data were streamed in RT via a Bluetooth® connection to a computer running the CRM RT application. Results CRM was successfully implemented for RT data capture during the research study. The CRM RT application achieved a median performance error of −0.95%, while the median absolute performance error was higher at 19.00%. CRM RT resulted in an average early prediction time of 18.3 min by tracking the slope trend changes in RT. Discussion The CRM RT application effectively tracked CRM during simulated hypovolemia using a wearable non-invasive sensor. Predictions served as an earlier indicator of hemorrhage compared to traditional vital signs, addressing a limitation of current triage practices. Overall, the CRM RT application represents a promising advancement toward RT prediction of hypovolemic decompensation.