Department of Cardiology, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark.
Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
Int J Cardiovasc Imaging. 2024 Aug;40(8):1661-1670. doi: 10.1007/s10554-024-03151-6. Epub 2024 Jun 16.
Computer simulations of coronary fractional flow reserve (FFR) based on coronary imaging have emerged as an attractive alternative to invasive measurements. However, most methods are proprietary and employ non-physiological assumptions. Our aims were to develop and validate a physiologically realistic open-source simulation model for coronary flow, and to use this model to predict FFR based on intracoronary optical coherence tomography (OCT) data in individual patients. We included patients undergoing elective coronary angiography with angiographic borderline coronary stenosis. Invasive measurements of coronary hyperemic pressure and absolute flow and OCT imaging were performed. A computer model of coronary flow incorporating pulsatile flow and the effect of left ventricular contraction was developed and calibrated, and patient-specific flow simulation was performed. Forty-eight coronary arteries from 41 patients were included in the analysis. Average FFR was 0.79 ± 0.14, and 50% had FFR ≤ 0.80. Correlation between simulated and measured FFR was high (r = 0.83, p < 0.001). Average difference between simulated FFR and observed FFR in individual patients was - 0.009 ± 0.076. Overall diagnostic accuracy for simulated FFR ≤ 0.80 in predicting observed FFR ≤ 0.80 was 0.88 (0.75-0.95) with sensitivity 0.79 (0.58-0.93) and specificity 0.96 (0.79-1.00). The positive predictive value was 0.95 (0.75-1.00) and the negative predictive value was 0.82 (0.63-0.94). In conclusion, realistic simulations of whole-cycle coronary flow can be produced based on intracoronary OCT data with a new, computationally simple simulation model. Simulated FFR had moderate numerical agreement with observed FFR and a good diagnostic accuracy for predicting hemodynamic significance of coronary stenoses.
基于冠状动脉成像的计算机模拟计算冠状动脉血流储备分数（FFR）已成为一种有吸引力的替代有创测量的方法。然而，大多数方法都是专有的，并采用非生理假设。我们的目的是开发和验证一种用于冠状动脉血流的生理现实的开源模拟模型，并使用该模型基于个体患者的冠状动脉光学相干断层扫描（OCT）数据预测 FFR。我们纳入了接受选择性冠状动脉造影检查且存在有创测量的冠状动脉充血压力和绝对血流量以及 OCT 成像的患者。开发并校准了一个纳入心动周期收缩的冠状动脉血流脉动模型，并进行了患者特异性血流模拟。共纳入 41 名患者的 48 支冠状动脉。平均 FFR 为 0.79 ± 0.14，50%的患者 FFR≤0.80。模拟 FFR 与测量 FFR 之间具有高度相关性（r=0.83，p<0.001）。模拟 FFR 与患者个体观察 FFR 的平均差值为-0.009 ± 0.076。预测观察 FFR≤0.80 的模拟 FFR≤0.80 的整体诊断准确性为 0.88（0.75-0.95），灵敏度为 0.79（0.58-0.93），特异性为 0.96（0.79-1.00）。阳性预测值为 0.95（0.75-1.00），阴性预测值为 0.82（0.63-0.94）。总之，基于新的计算简单的模拟模型，可基于冠状动脉 OCT 数据生成整个心动周期冠状动脉血流的现实模拟。模拟 FFR 与观察 FFR 具有中等程度的数值一致性，对预测冠状动脉狭窄的血流动力学意义具有良好的诊断准确性。
