The influence of hemodynamics on graft patency prediction model based on support vector machine

In the existing patency prediction model of coronary artery bypass grafting (CABG), the characteristics are based on graft flow, but no researchers selected hemodynamic factors as the characteristics. The purpose of this paper is to study whether the introduction of hemodynamic factors will affect the performance of the prediction model. Transit time flow-meter (TTFM) waveforms and 1-year postoperative patency results were obtained from 50 internal mammary arterial grafts (LIMA) and 82 saphenous venous grafts (SVG) in 60 patients. Taking TTFM waveforms as the boundary conditions, the CABG ideal models were constructed to obtain hemodynamic factors in grafts. Based on clinical characteristics and combination of clinical and hemodynamic characteristics, patency prediction models based on support vector machine (SVM) were constructed respectively. For LIMA, after the introduction of hemodynamic factors, the accuracy, sensitivity and specificity of the prediction model increased from 70.35%, 50% and 74.17% to 78.02%, 70% and 78.89%, respectively. For SVG, the accuracy, sensitivity and specificity of the prediction model increased from 63.24%, 40% and 76.91% to 74.41%, 60.1% and 82.73%, respectively. The performance of the prediction model can be improved by introducing hemodynamic factors into the characteristics of the model. The accuracy, sensitivity and specificity of the prediction results are higher with the addition of hemodynamic characteristics.