Risk of post-transplant cardiovascular events in kidney transplant recipients with preexisting aortoiliac stenosis.

Prediction of the risk of cardiovascular events (CVE's) is important to optimize outcomes after kidney transplantation. Aortoiliac stenosis is frequently observed during pre-transplant screening. We hypothesized that these patients are at higher risk of post-transplant CVE's due to the joint underlying atherosclerotic disease. Therefore, we aimed to assess whether aortoiliac stenosis was associated with post-transplant CVE's. This retrospective, single-center cohort study included adult kidney transplant recipients, transplanted between 2000 and 2016, with contrast-enhanced imaging available. Aortoiliac stenosis was classified according to the Trans-Atlantic Inter-Society Consensus (TASC) II classification and was defined as significant in case of ≥50% lumen narrowing. The primary outcome was CVE-free survival. Eighty-nine of 367 patients had significant aortoiliac stenosis and were found to have worse CVE-free survival (median CVE-free survival: stenosis 4.5 years (95% confidence interval (CI) 2.8-6.2), controls 8.9 years (95% CI 6.8-11.0); log-rank test P < .001). TASC II C and D lesions were independent risk factors for a post-transplant CVE with a hazard ratio of 2.15 (95% CI 1.05-4.38) and 6.56 (95% CI 2.74-15.70), respectively. Thus, kidney transplant recipients with TASC II C and D aortoiliac stenosis require extensive cardiovascular risk management pre-, peri,- and post-transplantation.

To screen or not to screen? The development of a prediction model for aorto-iliac stenosis in kidney transplant candidates.

Screening for aorto-iliac stenosis is important in kidney transplant candidates as its presence affects pre-transplantation decisions regarding side of implantation and the need for an additional vascular procedure. Reliable imaging techniques to identify this condition require contrast fluid, which can be harmful in these patients. To guide patient selection for these imaging techniques, we aimed to develop a prediction model for the presence of aorto-iliac stenosis. Patients with contrast-enhanced imaging available in the pre-transplant screening between January 1st, 2000 and December 31st, 2018 were included. A prediction model was developed using multivariable logistic regression analysis and internally validated using bootstrap resampling. Model performance was assessed with the concordance index and calibration slope. Three hundred and seventy-three patients were included, 90 patients (24.1%) had imaging-proven aorto-iliac stenosis. Our final model included age, smoking, peripheral arterial disease, coronary artery disease, a previous transplant, intermittent claudication and the presence of a femoral artery murmur. The model yielded excellent discrimination (optimism-corrected concordance index: 0.83) and calibration (optimism-corrected calibration slope: 0.91). In conclusion, this prediction model can guide the development of standardized protocols to decide which patients should receive vascular screening to identify aorto-iliac stenosis. External validation is needed before this model can be implemented in patient care.