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. 2021 Nov;34(11):2371-2381.
doi: 10.1111/tri.14013. Epub 2021 Sep 12.

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

Elsaline Rijkse  1 , Hongchao Qi  2 , Shabnam Babakry  1 , Diederik C Bijdevaate  3 , Hendrikus J A N Kimenai  1 , Joke I Roodnat  4 , Jan N M IJzermans  1 , Robert C Minnee  1
Affiliations

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

Elsaline Rijkse et al. Transpl Int. 2021 Nov.

Abstract

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.

Keywords: cardiovascular disease; kidney transplantation; recipient screening.

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Conflict of interest statement

The authors have declared no conflicts of interest.

Figures

Figure 1
Figure 1
Receiver‐Operating‐Characteristic (ROC) curve of the final model. With the chosen cut‐off value (the dot on the curve), sensitivity and specificity were 0.83 and 0.73, respectively. The cut‐off value can be used to determine whether contrast enhanced imaging is advised.
Figure 2
Figure 2
Calibration plot for the final model, which visualizes the observed probability of the outcome in comparison to the predicted probability based on the model. The blue lines represent each one of the 20 imputed datasets. The dashed line in the middle represents perfect calibration.
Figure 3
Figure 3
Example of the online risk calculator for (a) Patient 1. (b) Patient 2.
See this image and copyright information in PMC

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