Guberti Diletta, Ferrario Manuela, Carrara Marta
Department Electronics, Information, and Bioengineering (DEIB), Politecnico Di Milano, Milan, Italy.
Med Biol Eng Comput. 2025 Feb 27. doi: 10.1007/s11517-025-03328-8.
Wave separation analysis (WSA) is the gold standard to analyze the arterial blood pressure (ABP) waveform, decomposing it into a forward and a reflected wave. It requires ABP and arterial blood flow (ABF) measurement, and ABF is often unavailable in clinical settings. Therefore, methods to estimate ABF from ABP have been proposed, but they are not investigated in critical conditions. In this work, an autoregressive with exogenous input model was proposed as an original method to estimate ABF from the measured ABP. Its performance in assessing WSA indices to characterize the arterial tree was evaluated in critical conditions, i.e., during sepsis. The triangular and the personalized flow approximation and the multi-Gaussian ABP decomposition were compared to the proposed model. The results highlighted how the black-box modeling approach is superior to other flow estimation models when computing WSA indices in septic condition. This approach holds promise for overcoming challenges in clinical settings where ABF data are unavailable.
波形分离分析(WSA)是分析动脉血压(ABP)波形的金标准,可将其分解为前行波和反射波。它需要测量ABP和动脉血流(ABF),而在临床环境中ABF往往难以获取。因此,人们提出了从ABP估计ABF的方法,但尚未在危急情况下进行研究。在这项工作中,提出了一种带外生输入的自回归模型作为从测量的ABP估计ABF的原创方法。在危急情况下,即脓毒症期间,评估了其在评估用于表征动脉树的WSA指标方面的性能。将三角形和个性化血流近似以及多高斯ABP分解与所提出的模型进行了比较。结果突出表明,在脓毒症状态下计算WSA指标时,黑箱建模方法优于其他血流估计模型。这种方法有望克服临床环境中ABF数据不可用的挑战。