Weatherhead PET Center, Division of Cardiology, Department of Medicine, McGovern Medical School at UTHealth and Memorial Hermann Hospital, Houston, Texas.
Department of Cardiology, Cardiovascular Center Aalst OLV Hospital, Aalst, Belgium.
Catheter Cardiovasc Interv. 2020 Sep 1;96(3):E268-E277. doi: 10.1002/ccd.28780. Epub 2020 Feb 20.
We sought to develop an automatic method for correcting common errors in phasic pressure tracings for physiology-guided interventions on coronary and valvular stenosis.
Effective coronary and valvular interventions rely on accurate hemodynamic assessment. Phasic (subcycle) indexes remain intrinsic to valvular stenosis and are emerging for coronary stenosis. Errors, corrections, and clinical implications of fluid-filled catheter phasic pressure assessments have not been assessed in the current era of ubiquitous, high-fidelity pressure wire sensors.
We recruited patients undergoing invasive coronary physiology assessment. Phasic aortic pressure signals were recorded simultaneously using a fluid-filled guide catheter and 0.014″ pressure wire before and after standard calibration as well as after pullback. We included additional subjects undergoing hemodynamic assessment before and after transcatheter aortic valve implantation. Using the pressure wire as reference standard, we developed an automatic algorithm to match phasic pressures.
Removing pressure offset and temporal shift produced the largest improvements in root mean square (RMS) error between catheter and pressure wire signals. However, further optimization <1 mmHg RMS error was possible by accounting for differential gain and the oscillatory behavior of the fluid-filled guide. The impact of correction was larger for subcycle (like systole or diastole) versus whole-cycle metrics, indicating a key role for valvular stenosis and emerging coronary pressure ratios.
When calibrating phasic aortic pressure signals using a pressure wire, correction requires these parameters: offset, timing, gain, and oscillations (frequency and damping factor). Automatically eliminating common errors may improve some clinical decisions regarding physiology-based intervention.
我们旨在开发一种自动方法,用于纠正生理指导介入冠状动脉和瓣膜狭窄时相位压力描记中的常见错误。
有效的冠状动脉和瓣膜介入依赖于准确的血流动力学评估。相位(亚周期)指标仍然是瓣膜狭窄的固有指标,并且正在出现用于冠状动脉狭窄。在当前普遍存在的高保真压力线传感器时代,尚未评估充满流体的导管相位压力评估的错误、校正和临床意义。
我们招募了接受侵入性冠状动脉生理评估的患者。在标准校准前后以及回撤期间,使用充满流体的引导导管和 0.014 英寸压力线同时记录相位主动脉压力信号。我们还包括了在经导管主动脉瓣植入术前后进行血流动力学评估的额外受试者。使用压力线作为参考标准,我们开发了一种自动算法来匹配相位压力。
消除压力偏移和时间偏移可最大程度地提高导管和压力线信号之间的均方根(RMS)误差。然而,通过考虑差分增益和充满流体的引导的振荡行为,可以进一步优化 <1mmHg RMS 误差。校正的影响对于亚周期(如收缩期或舒张期)与全周期指标更大,表明瓣膜狭窄和新兴的冠状动脉压力比起着关键作用。
在使用压力线校准相位主动脉压力信号时,校正需要这些参数:偏移、定时、增益和振荡(频率和阻尼系数)。自动消除常见错误可能会改善一些基于生理学的干预决策。