Arai Tatsuya, Lee Kichang, Cohen Richard J
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4971-4. doi: 10.1109/IEMBS.2010.5627225.
Cardiac output (CO) and stroke volume (SV) are the key hemodynamic parameters to be monitored and assessed in ambulatory and critically ill patients. The purpose of this study was to introduce and validate a new algorithm to continuously estimate, within a proportionality constant, CO and SV by means of mathematical analysis of peripheral arterial blood pressure (ABP) waveforms. The algorithm combines three variants of the Windkessel model. Input parameters to the algorithm are the end-diastolic pressure, mean arterial pressures, inter-beat interval, and the time interval from end-diastolic to peak systolic pressure. The SV estimates from the three variants of the Windkessel model were weighted and integrated to provide beat-to-beat SV estimation. In order to validate the new algorithm, the estimated CO and SV were compared to those obtained through surgically implanted Transonic™ aortic flow probes placed around the aortic roots of six Yorkshire swine. Overall, estimation errors in CO and SV derived from radial ABP were 10.1% and 14.5% respectively, and 12.7% and 16.5% from femoral ABP. The new algorithm demonstrated statistically significant improvement in SV estimation compared with previous methods.
心输出量(CO)和每搏输出量(SV)是在门诊和危重症患者中需要监测和评估的关键血流动力学参数。本研究的目的是引入并验证一种新算法,通过对外周动脉血压(ABP)波形进行数学分析,在比例常数范围内连续估计CO和SV。该算法结合了Windkessel模型的三种变体。算法的输入参数为舒张末期压力、平均动脉压、心动周期、以及从舒张末期到收缩压峰值的时间间隔。对Windkessel模型的三种变体所估计的SV进行加权和整合,以提供逐搏SV估计。为了验证新算法,将估计的CO和SV与通过植入六只约克郡猪主动脉根部周围的Transonic™主动脉血流探头所获得的值进行比较。总体而言,从桡动脉ABP得出的CO和SV估计误差分别为10.1%和14.5%,从股动脉ABP得出的估计误差分别为12.7%和16.5%。与之前的方法相比,新算法在SV估计方面显示出统计学上的显著改善。