Predicting blood loss volume in a canine model of hemorrhagic shock using arterial waveform machine learning analysis.

Abstract

Objective: To determine if the compensatory reserve algorithm validated in humans can be applied to canines. Our secondary objective was to determine if a simpler waveform analysis could predict the percentage of blood loss volume.

Methods: 6 purpose-bred, anesthetized dogs underwent 5 rounds of controlled hemorrhage and resuscitation while continuously recording invasive arterial blood pressure waveforms in this prospective, experimental study. We calculated human compensatory reserve using deep learning (hCRM-DL) and machine learning (hCRM-ML) models previously developed with human data. We trained a metric to track blood loss volume using features extracted from canine (c) arterial waveforms as an input.

Results: When applied to the 6 dogs, the hCRM-DL model (R2 = 0.38) more poorly fit a linear regression model against mean arterial pressure and had lower area under the receiver operating characteristic (AUROC; 0.60) compared to the hCRM-ML model (R2 = 0.61; AUROC, 0.73). Conversely, the arterial waveform analysis for canine blood loss volume metric (cBLVM) predicted blood loss in dogs experiencing controlled hemorrhagic shock more accurately (R2 = 0.74). The cBLVM model for predicting blood loss volume had the highest AUROC score (0.81) and was the earliest indicator of hemorrhage onset.

Conclusions: The hCRM-ML and hCRM-DL algorithms did not translate to accurate prediction of the onset of hemorrhagic shock in dogs. However, the arterial waveform feature analysis-derived cBLVM might provide decision support to resuscitate dogs with hemorrhagic shock.

Clinical relevance: Canine BLVM may be useful in estimating blood loss in dogs, which can guide resuscitation strategies for these patients.