Key result
Machine-learning models using combined photoplethysmography and arterial blood pressure estimated blood loss volume with a mean error of 7.0 ± 139.4 mL, outperforming traditional formulas.
Why the study?
Effective and accurate blood loss volume estimation methods that can be implemented in operating rooms are currently lacking.
Does a machine-learning-based estimation model using PPG and ABP improve the accuracy of surgical blood loss volume estimation compared to traditional hematocrit computation formulas in a porcine hemorrhage model?
Does a machine-learning-based estimation model using PPG and ABP improve the accuracy of surgical blood loss volume estimation compared to traditional hematocrit computation formulas in a porcine hemorrhage model?
Absolute Event Rate: 7% vs 102.1%
Machine-learning models using continuously monitored PPG and ABP significantly improve the accuracy of surgical blood loss volume estimation compared to traditional hematocrit formulas in a preclinical model.
May enable real-time BLV monitoring in surgery; leaves open human validation and clinical adoption.
Background: There are currently no effective and accurate blood loss volume (BLV) estimation methods that can be implemented in operating rooms. To improve the accuracy and reliability of BLV estimation and facilitate clinical implementation, we propose a novel estimation method using continuously monitored photoplethysmography (PPG) and invasive arterial blood pressure (ABP). Methods: Forty anesthetized York Pigs (31.82 ± 3.52 kg) underwent a controlled hemorrhage at 20 mL/min until shock development was included. Machine-learning-based BLV estimation models were proposed and tested on normalized features derived by vital signs. Results: The results showed that the mean ± standard deviation (SD) for estimating BLV against the reference BLV of our proposed random-forest-derived BLV estimation models using PPG and ABP features, as well as the combination of ABP and PPG features, were 11.9 ± 156.2, 6.5 ± 161.5, and 7.0 ± 139.4 mL, respectively. Compared with traditional hematocrit computation formulas (estimation error: 102.1 ± 313.5 mL), our proposed models outperformed by nearly 200 mL in SD. Conclusion: This is the first attempt at predicting quantitative BLV from noninvasive measurements. Normalized PPG features are superior to ABP in accurately estimating early-stage BLV, and normalized invasive ABP features could enhance model performance in the event of a massive BLV.
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Chen et al. (2020) studied Hemorrhage (n=40). Machine-learning-based BLV estimation models using PPG and ABP vs. Traditional hematocrit computation formulas was evaluated on Blood loss volume estimation error. Machine-learning models using combined photoplethysmography and arterial blood pressure estimated blood loss volume with a mean error of 7.0 ± 139.4 mL, outperforming traditional formulas.
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