Edwards Thomas, Gonzalez Jose M, Hernandez-Torres Sofia, Venn Emilee, Ford Rebekah, Ewer Nicole, Hoareau Guillaume L, Holland Lawrence, Convertino Victor A, Snider Eric
US Army Institute of Surgical Research, Joint Base San Antonio-Fort Sam Houston, Houston, TX.
Department of Small Animal Clinical Sciences, College of Veterinary Medicine, Texas A&M University, College Station, TX.
Am J Vet Res. 2024 Dec 11:1-10. doi: 10.2460/ajvr.24.09.0256.
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.
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.
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.
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.
Canine BLVM may be useful in estimating blood loss in dogs, which can guide resuscitation strategies for these patients.
确定在人类中验证的代偿储备算法是否可应用于犬类。我们的次要目的是确定一种更简单的波形分析是否能够预测失血量的百分比。
在这项前瞻性实验研究中，6只经专门培育的麻醉犬接受了5轮控制性出血和复苏，同时持续记录有创动脉血压波形。我们使用先前根据人类数据开发的深度学习（hCRM-DL）和机器学习（hCRM-ML）模型来计算人类代偿储备。我们训练了一种指标，将从犬类（c）动脉波形中提取的特征作为输入来追踪失血量。
当应用于这6只犬时，与hCRM-ML模型（R2 = 0.61；曲线下面积，0.73）相比，hCRM-DL模型（R2 = 0.38）与平均动脉压的线性回归模型拟合度更差，且受试者操作特征曲线下面积（AUROC；0.60）更低。相反，用于犬类失血量指标（cBLVM）的动脉波形分析能更准确地预测经历控制性出血性休克的犬的失血量（R2 = 0.74）。用于预测失血量的cBLVM模型具有最高的AUROC分数（0.81），并且是出血开始的最早指标。
hCRM-ML和hCRM-DL算法无法准确预测犬类出血性休克的发作。然而，源自动脉波形特征分析的cBLVM可能为出血性休克犬的复苏提供决策支持。
犬类BLVM可能有助于估计犬的失血量，从而指导这些患者的复苏策略。
