The use of both internal thoracic arteries for coronary revascularization increases the estimate of post-operative lower limb ischemia in patients with peripheral artery disease

Abstract

Background: Patients with a history of peripheral arterial disease (PAD) undergoing coronary artery bypass grafting (CABG) exhibit higher rates of complications. There are conflicting data on the survival benefits for bilateral thoracic artery (BITA) grafting compared with left internal thoracic artery (LITA) CABG in patients with PAD. The aim of the study was to explore the influence of the use of BITA grafts vs. LITA for CABG on post-operative acute lower limb ischemia (ALLI) and main post-operative complications in patients with concomitant PAD.

Methods: We used a propensity-score (PS) based analysis to compare outcomes between the two surgical procedures, BITA and LITA. The inverse probability of treatment weighting PS technique was applied to adjust for pre- and intra-operative confounders, and to get optimal balancing of the pre-operative data. The primary outcome was the estimate of postoperative ALLI. Secondary outcomes included overall death and death of cardiac causes within 30 days of surgery, stroke and acute kidney disease (AKD).

Results: The study population consisted of 1961 patients. The LITA procedure was performed in 1768 patients whereas 193 patients underwent a BITA technique. The estimate of ALLI was 14% higher in the BITA compared to the LITA (p < 0.001) group. Thirty-day mortality, cardiac death, occurrence of stroke and AKI did not differ significantly between the groups.

Conclusions: The use of both ITAs led to a significant increase in ALLI. This result was most likely caused by the complete disruption of the ITA collateral providing additional blood supply to the lower extremities. Based on our data, BITA should be used with extreme caution in PAD patients. Further research on this topic is necessary to confirm our findings.

Keywords: Coronary disease; Limb ischemia; Peripheral artery disease.

Fig. 1

a. Optimize plot. The plot is a graphical display of the balance criteria as a function of the GBM iteration. The graph demonstrates that a favorable balance was achieved with 1000 iterations and that such a balance was worse with a higher number of iterations that, thus were unnecessary. b. Overlap Assessment. The figure presents two sets of box plots of PS distributions. An overlap between the groups is desirable meaning that there are no values of the pretreatment variables that occur only in one of the treatment conditions. However, there are no specific rules for what constitutes sufficient overlap so, in doubtful cases the combination of the overlap plot and the balance table are used. In our model there is a good overlap between the two boxplots. c. Standardized Effect Size plot. It assesses the balance of pretreatment variables before and after weighting. It shows the maximum pairwise ASMDs. The ASMDs cutoff for defining unbalanced variables was 0.20. The light blue line represents pretreatment covariates for which the maximum pairwise ASMD reduced after weighting. The red lines mean the pretreatment covariates for which the maximum pairwise ASMD increased after weighting. A good balance is obtained when, after weighting in the majority of variables the ASMDs are < 0.20 and there is a prevalence of light blue lines. In our model, after weighting the pretreatment variables, the balance showed excellent results with all ASMD values being well below 0.2. d. Quantile-quantile (Q-Q) plot. This plot also assesses the balance of pretreatment variables. In the plot, the Kolmogorov-Smirnov p-value is plotted against the rank of p-value for pretreatment variables. Along a 45-degree fitting line, open symbols represent weighted covariates and solid symbols represent unweighted covariates. A good balance is obtained when open symbols lie close, below or above, the 45-degree line. After weighting, in our model, all covariates were well above the reference line

Fig. 2

The estimate and 95% CI of post-operative ALLI in employing LITA or BITA. *p < 0.001(df.0.14 [0.07–0.22]