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.　(C)　2019　Elsevier　Ltd.　All　rights　reserved.