title: Cut-off values for transit time flowmetry: are the revision criteria appropriate?
abstract: BACKGROUND
  Graft Imaging to Improve Patency (GRIIP), a single-center, randomized blinded clinical trial, reported that intraoperative graft assessment with graft revision according to a priori criteria of transit time flowmetry (TTF) and intraoperative fluorescent angiography did not improve graft patency at one year after coronary artery bypass grafting (CABG) when compared with standard intraoperative management. The objective of this study is to investigate whether other TTF values are more predictive of the saphenous vein graft (SVG) failure and/or clinical outcomes.
  
  
  METHODS
  This is a case control retrospective study of 65 SVGs from 44 patients from GRIIP. Study outcomes were graft patency at 12 months and major adverse cardiac events (MACE; death, myocardial infarction, repeat revascularization).
  
  
  RESULTS
  Twenty-two SVGs were occluded. In receiver operating characteristic curve analysis, TTF mean flow was significantly predictive of one-year SVG failure (area under the curve = 0.698, p < 0.01), and 31 mL/min was the best cut-off value (p = 0.017, sensitivity 63.6%, specificity 67.4%). The risk of graft occlusion was 14/28, 50% for grafts with mean flow <31 mL/min and 8/37, 21.6% for grafts with mean flow ≥ 31 mL/min. In logistic regression models, mean flow was a significant predictor of early SVG failure (Odds Ratio 0.95 [0.91-0.99] per mL/min, p = 0.014) whereas other TTF values, patient comorbidities, and/or medication at discharge were not. However, TTF values were not predictive of MACE.
  
  
  CONCLUSIONS
  TTF can identify non-functional grafts during CABG, but is of questionable value to improve one-year graft patency.
authors.affiliation: Division of Cardiac Surgery, Schulich Heart Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Dai
authors.initials: D
authors.lastname: Une
authors.forename: Saswata
authors.initials: S
authors.lastname: Deb
authors.forename: Genta
authors.initials: G
authors.lastname: Chikazawa
authors.forename: Kamya
authors.initials: K
authors.lastname: Kommaraju
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Tsuneyoshi
authors.forename: Reena
authors.initials: R
authors.lastname: Karkhanis
authors.forename: Steve
authors.initials: S
authors.lastname: Singh
authors.forename: Jessica
authors.initials: J
authors.lastname: Vincent
authors.forename: Hideki
authors.initials: H
authors.lastname: Tsubota
authors.forename: Jeri
authors.initials: J
authors.lastname: Sever
authors.forename: Fuad
authors.initials: F
authors.lastname: Moussa
authors.forename: Gideon
authors.initials: G
authors.lastname: Cohen
authors.forename: George T
authors.initials: GT
authors.lastname: Christakis
authors.forename: Stephen E
authors.initials: SE
authors.lastname: Fremes
mesh_terms: D000368:Aged; D001783:Blood Flow Velocity; D016022:Case-Control Studies; D001026:Coronary Artery Bypass; D005260:Female; D005544:Forecasting; D006083:Graft Occlusion, Vascular / Q000175:diagnosis* / Q000453:epidemiology / Q000209:etiology; D006801:Humans; D007432:Intraoperative Period; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012189:Retrospective Studies; D012212:Rheology; D012501:Saphenous Vein / Q000502:physiology / Q000637:transplantation*; D013997:Time Factors; D014654:Vascular Patency