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Non-invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-Driven or Model-Based?

机译:肺动脉高血压患者的非侵袭性压力估计:数据驱动或模型基于模型?

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Right heart catheterisation is considered as the gold standard for the assessment of patients with suspected pulmonary hypertension. It provides clinicians with meaningful data, such as pulmonary capillary wedge pressure and pulmonary vascular resistance, however its usage is limited due to its invasive nature. Non-invasive alternatives, like Doppler echocardiography could present insightful measurements of right heart but lack detailed information related to pulmonary vasculature. In order to explore non-invasive means, we studied a dataset of 95 pulmonary hypertension patients, which includes measurements from echocardiography and from right-heart catheterisation. We used data extracted from echocardiography to conduct cardiac circulation model personalisation and tested its prediction power of catheter data. Standard machine learning methods were also investigated for pulmonary artery pressure prediction. Our preliminary results demonstrated the potential prediction power of both data-driven and model-based approaches.
机译:右心导管呈现为评估涉嫌肺动脉高压患者的金标准。它为临床医生提供了有意义的数据,例如肺毛细血管楔形压力和肺血管阻力,但由于其侵入性,其使用受到限制。非侵入式替代品,如多普勒超声心动图都可以呈现右心的富有识别测量,但缺乏与肺脉管系统相关的详细信息。为了探索非侵入性手段,我们研究了95例肺动脉高压患者的数据集,包括来自超声心动图和右心导管的测量。我们使用从超声心动图提取的数据来进行心脏循环模型个性化并测试其预测导管数据的预测力。还研究了标准机学习方法用于肺动脉压力预测。我们的初步结果表明了数据驱动和基于模型的方法的潜在预测力。

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