## Development of a clinical model to predict the likelihood of identification of the Adamkiewicz artery by angiotomography

Abstract Background There are clinically important morphological differences in the Adamkiewicz artery (AKA) between populations that do and do not have aortic disease and they have an influence on the neuroischemic complications involving the spinal cord during surgical operations. It is not yet known whether clinical parameters correlate with the predictability of identification of the artery using angiotomography. Objective To develop a mathematical model that by correlating clinical parameters with atherosclerosis enables prediction of the probability of identification of the AKA in patients examined with angiotomography. Method This is a cross-sectional, observational study using a patient database and image bank. A multivariate statistical analysis was conducted and a logit mathematical model was constructed to predict AKA identification. Significant variables were used to build a formula for calculation of the probability of identification. This model was calibrated and its power of discrimination was assessed using receiver operating characteristic (ROC) curves. Selection of explanatory variables was based on largest area under the ROC curve (p = 0.041) and combined significance of variables. Results A total of 110 cases were analyzed (54.5% were male, mean age was 60.97 years, and ethnicity coefficients were white -2.471, brown -1.297, and black -0.971) and the AKA was identified in 60.9%. Body mass index: 27.06 ± 0.98 (coef. -0.101); smokers: 55.5% (coef. -1.614/-1.439); diabetes: 13.6%; hypertension: 65.5% (coef. -1.469); dyslipidemia: 58.2%; aortic aneurysm: 38.2%; aortic dissection: 12.7%; and mural thrombus: 24.5%. The constant was 6.262. The formula for calculating the probability of detection is as follows: ( e − ( C o e f . E t n i c i t y + ( C o e f . B M I × B M I ) + C o e f . s m o ker + C o e f . S A H + C o e f . d y s l i p + C o n s tan t ) + 1 ) − 1 . The prediction model was constructed and made available at: https://vascular.pro/aka-model . Conclusions Using the covariates ethnicity, body mass index, smoking, arterial hypertension, and dyslipidemia, it proved possible to create a mathematical model for predicting identification of the AKA with a combined significance of nine coefficients (p = 0.042).