Carlson Kent D, Dragomir-Daescu Dan, Boilson Barry A
Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA.
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA.
Bioengineering (Basel). 2025 May 2;12(5):484. doi: 10.3390/bioengineering12050484.
Left ventricular assist device (LVAD) thrombosis typically presents late and may have devastating consequences for patients. While LVAD pump thrombosis is uncommon with current pump designs, many patients worldwide remain supported with previous generations of LVADs, including the HeartWare device (HVAD). Researchers have focused on investigating the acoustic signatures of LVADs to enable earlier detection and treatment of this condition. This study explored the use of machine learning algorithms to predict thrombosis from harmonic power values determined from the acoustic signatures of a cohort of HVAD patients ( = 11). The current dataset was too small to develop a predictive model for new data, but exhaustive cross validation indicated that machine learning models using the first two or the first three harmonic power values both resulted in reasonable prediction accuracy of the thrombosis outcome. Furthermore, when principal component analysis (PCA) was applied to the harmonic power variables from these promising models, the use of the resulting PCA variables in machine learning models further increased the thrombosis outcome prediction accuracy. K-nearest neighbor (KNN) models gave the best predictive accuracy for this dataset. Future work with a larger HVAD recording dataset is necessary to develop a truly predictive model of HVAD thrombosis. Such a predictive model would provide clinicians with a marker to detect HVAD thrombosis based directly on pump performance, to be used along with current clinical markers.
左心室辅助装置（LVAD）血栓形成通常出现较晚，可能给患者带来灾难性后果。虽然目前的泵设计使LVAD泵血栓形成并不常见，但全球许多患者仍依靠前代LVAD维持，包括HeartWare装置（HVAD）。研究人员专注于研究LVAD的声学特征，以便能更早地检测和治疗这种情况。本研究探讨了使用机器学习算法，根据一组HVAD患者（n = 11）声学特征确定的谐波功率值来预测血栓形成。当前数据集太小，无法为新数据开发预测模型，但详尽的交叉验证表明，使用前两个或前三个谐波功率值的机器学习模型都能得出合理的血栓形成结果预测准确率。此外，当对这些有前景模型的谐波功率变量应用主成分分析（PCA）时，在机器学习模型中使用所得的PCA变量进一步提高了血栓形成结果预测准确率。对于该数据集，K近邻（KNN）模型给出了最佳预测准确率。未来有必要使用更大的HVAD记录数据集开展工作，以开发出真正能预测HVAD血栓形成的模型。这样的预测模型将为临床医生提供一个基于泵性能直接检测HVAD血栓形成的指标，以便与当前临床指标一起使用。
