Viscosity Prediction in a Physiologically Controlled Ventricular Assist Device
Viscosity Prediction in a Physiologically Controlled Ventricular Assist Device
复制标题
生理控制心室辅助装置中的粘度预测
DOI：
--
复制
发表时间：
2018
影响因子：
4.6
通讯作者：
M. Daners
中科院分区：
文献类型：
--
作者：
A. Petrou;Menelaos Kanakis;S. Boës;Panagiotis Pergantis;M. Meboldt;M. Daners
目的：我们提出了一种新的机器学习模型，以准确预测在旋转心室辅助装置（VAD）支持病理性循环期间的血液模拟粘度。目的是持续监测VAD患者的红细胞压积（HCT），从而获得更可靠的泵流量估计和可能的早期发现不良事件，如出血或泵血栓形成。方法：通过改变泵速、模拟循环的生理要求和血液模拟物的粘度，将血泵连接到混合模拟循环，生成一个大型数据集。以泵的入口压力和本征信号作为模型的输入。高斯过程产生了性能最好的模型，然后使用堆叠泛化的变体将其组合以得到最终模型。最后的模型是用创建的数据集中未见过的测试数据进行评估的。结果：对于这些数据，该模型与真实HCT的平均绝对偏差为1.81%，证明该模型能够正确预测HCT的变化方向。结果表明，它与设定的速度和模拟的心血管循环状况无关。结论：预测模型的准确性可以提高流量估计器的质量，并在早期发现不良事件。建议对该方法进行血液评价以进一步验证。意义：其临床应用可为临床医生提供可靠而重要的患者血流动力学信息，从而加强对患者的监护和监督。
Objective: We present a novel machine learning model to accurately predict the blood-analog viscosity during support of a pathological circulation with a rotary ventricular assist device (VAD). The aim is the continuous monitoring of the hematocrit (HCT) of VAD patients with the benefit of a more reliable pump flow estimation and a possible early detection of adverse events, such as bleeding or pump thrombosis. Methods: A large dataset was generated with a blood pump connected to a hybrid mock circulation by varying the pump speed, the physiological requirements of the modeled circulation, and the viscosity of the blood-analog. The inlet pressure and the intrinsic signals of the pump were considered as inputs for the model. Gaussian process yielded models with the best performance, which were then combined using a variant of stacked generalization to derive the final model. The final model was evaluated with unseen testing data from the dataset created. Results: For these data, the model yielded a mean absolute deviation of 1.81% from the true HCT, while it proved to correctly predict the direction of the HCT change. It showed to be independent of the set speed and of the condition of the simulated cardiovascular circulation. Conclusion: The accuracy of the prediction model allows an improvement of the quality of flow estimators and the detection of adverse events at an early stage. The evaluation of this approach with blood is suggested for further validation. Significance: Its clinical application could provide the clinicians with reliable and important hemodynamic information of the patient and, thus, enhance patient monitoring and supervision.