Development of hemorrhage identification model using non-invasive vital signs.

Early detection and timely management of bleeding is critical as failure to recognize physiologically significant bleeding is associated with significant morbidity and mortality. Many such instances are detected late, even in highly monitored environments, contributing to delay in recognition and intervention. We propose a non-invasive early identification model to detect bleeding events using continuously collected photoplethysmography (PPG) and electrocardiography (ECG) waveforms. Fifty-nine York pigs undergoing fixed-rate, controlled hemorrhage were involved in this study and a least absolute shrinkage and selection operator regression-based early detection model was developed and tested using PPG and ECG derived features. The output of the early detection model was a risk trajectory indicating the future probability of bleeding. Our proposed models were generally accurate in predicting bleeding with an area under the curve of 0.89 (95% CI 0.87-0.92) and achieved an average time of 16.1 mins to detect 16.8% blood loss when a false alert rate of 1% was tolerated. Models developed on non-invasive data performed with similar discrimination and lead time to hemorrhage compared to models using invasive arterial blood pressure as monitoring data. A bleed detection model using only non-invasive monitoring performs as well as those using invasive arterial pressure monitoring.