Development of a risk score for early saphenous vein graft failure: An individual patient data meta-analysis

Alexios S Antonopoulos, Ayodele Odutayo, Evangelos K Oikonomou, Marialena Trivella, Mario Petrou, Gary S Collins, Charalambos Antoniades, SAFINOUS-CABG (Saphenous Vein Graft Failure—An Outcomes Study in Coronary Artery Bypass Grafting) group, Ioannis Akoumianakis, Keith M Channon, Laura Herdman, Marios Margaritis, Stefan Neubauer, Sheena Thomas, Stephen Fremes, Reena Karkhanis, Jeffrey Rade, Toshihiro Fukui, Hidefumi Nishida, Shuichiro Takanashi, Ho Young Hwang, Ki-Bong Kim, Luigi Mannacio, Vito Mannacio, Jota Nakano, Louis Perrault, Attila Kardos, Hitoshi Okabayashi, Dimitris Tousoulis, Andrew Kelion, Nik Sabharwal, George Krasopoulos, Rana Sayeed, David Taggart, Alexios S Antonopoulos, Ayodele Odutayo, Evangelos K Oikonomou, Marialena Trivella, Mario Petrou, Gary S Collins, Charalambos Antoniades, SAFINOUS-CABG (Saphenous Vein Graft Failure—An Outcomes Study in Coronary Artery Bypass Grafting) group, Ioannis Akoumianakis, Keith M Channon, Laura Herdman, Marios Margaritis, Stefan Neubauer, Sheena Thomas, Stephen Fremes, Reena Karkhanis, Jeffrey Rade, Toshihiro Fukui, Hidefumi Nishida, Shuichiro Takanashi, Ho Young Hwang, Ki-Bong Kim, Luigi Mannacio, Vito Mannacio, Jota Nakano, Louis Perrault, Attila Kardos, Hitoshi Okabayashi, Dimitris Tousoulis, Andrew Kelion, Nik Sabharwal, George Krasopoulos, Rana Sayeed, David Taggart

Abstract

Objectives: Early saphenous vein graft (SVG) occlusion is typically attributed to technical factors. We aimed at exploring clinical, anatomical, and operative factors associated with the risk of early SVG occlusion (within 12 months postsurgery).

Methods: Published literature in MEDLINE was searched for studies reporting the incidence of early SVG occlusion. Individual patient data (IPD) on early SVG occlusion were used from the SAFINOUS-CABG Consortium. A derivation (n = 1492 patients) and validation (n = 372 patients) cohort were used for model training (with 10-fold cross-validation) and external validation respectively.

Results: In aggregate data meta-analysis (48 studies, 41,530 SVGs) the pooled estimate for early SVG occlusion was 11%. The developed IPD model for early SVG occlusion, which included clinical, anatomical, and operative characteristics (age, sex, dyslipidemia, diabetes mellitus, smoking, serum creatinine, endoscopic vein harvesting, use of complex grafts, grafted target vessel, and number of SVGs), had good performance in the derivation (c-index = 0.744; 95% confidence interval [CI], 0.701-0.774) and validation cohort (c-index = 0.734; 95% CI, 0.659-0.809). Based on this model. we constructed a simplified 12-variable risk score system (SAFINOUS score) with good performance for early SVG occlusion (c-index = 0.700, 95% CI, 0.684-0.716).

Conclusions: From a large international IPD collaboration, we developed a novel risk score to assess the individualized risk for early SVG occlusion. The SAFINOUS risk score could be used to identify patients that are more likely to benefit from aggressive treatment strategies.

Keywords: coronary artery bypass grafting; individual patient meta-analysis; patency; prediction model; saphenous vein graft.

Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Graphical abstract
Graphical abstract
https://www.ncbi.nlm.nih.gov/pmc/articles/instance/7322547/bin/fx2.jpg
Summary of the study design and the main findings of each arm.
Figure 1
Figure 1
Summary of the study design and the main findings of each arm. CABG, Coronary artery bypass grafting; SVG, saphenous vein graft; IPD, individual patient data; CV, coefficient of variation; AUC, area under the curve; CI, confidence interval; Glu, glucose; CKD, chronic kidney disease.
Figure 2
Figure 2
Early saphenous vein graft occlusion in published literature. Forest plot of published studies for the incidence of saphenous vein graft occlusion during the first 12 months post-coronary artery bypass grafting. The size of the squares corresponds to the weight of each study. The diamonds and their width represent the pooled weighted effect size and the 95% confidence intervals (CI), respectively.
Figure 3
Figure 3
Subgroup analysis for the aggregate data meta-analysis. Subgroup analysis for completeness of angiographic follow-up, study location, on/off-pump surgery, study size, and period of patient enrollment (A). Meta-regression and bubble plots for the association between the incidence of graft occlusion and study size (number of grafts assessed, B) or the period of patient enrollment (chronological ranking of studies, C). The size of the circles represents the weight of each study on the pooled estimate for graft occlusion. ES, Effect size; CI, confidence interval; SVG, saphenous vein graft.
Figure 4
Figure 4
Development of a predictive model for early saphenous vein graft (SVG) occlusion. Calibration curve and goodness of fit for the developed prediction model for SVG occlusion (A) and area under the curve (AUC) for model's performance in derivation and validation cohorts (B). Confusion matrix for predicted versus observed SVG occlusion in the validation cohort based on the developed model (C). Nomogram for predicting the risk for early SVG occlusion from the developed model (D). Observed and predicted probabilities for SVG occlusion across strata of the constructed SAFINOUS score, derived from the final predictive model (E). CI, Confidence interval; EVH, endoscopic vein harvesting; RCA, right coronary artery; LCx, left circumflex artery; OM, obtuse marginal; SVG, saphenous vein graft.
Figure 5
Figure 5
Predictive value of SAFINOUS score for early saphenous vein graft occlusion across patient subgroups. OR, Odds ratio; CI, confidence interval; LVEF, left ventricular ejection fraction; EVH, endoscopic vein harvesting; SVG, saphenous vein graft.
Video 1
Video 1
The main findings and implications of the study are summarized in this online video by Dr Antonopoulos. Video available at: https://www.jtcvs.org/article/S0022-5223(19)31640-X/fulltext.
Figure E1
Figure E1
PRISMA-IPD flow chart for the study. IPD, Individual patient data.
Figure E2
Figure E2
Model training with internal validation in the derivation cohort with 10-fold cross-validation and optimism adjusted c-index. AUC, Area under the curve; ROC, receiver operating characteristic.

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