Cardiac Risk Stratification in Renal Transplantation Using a Form of Artificial Intelligence

The purpose of this study was to determine if an expert network, a form of artificial intelligence, could effectively stratify cardiac risk in candidates for renal transplant. Input into the expert network consisted of clinical risk factors and thallium-201 stress test data. Clinical risk factor screening alone identified 95 of 189 patients as high risk. These 95 patients underwent thallium-201 stress testing, and 53 had either reversible or fixed defects. The other 42 patients were classified as low risk. This algorithm made up the “expert system,” and during the 4-year follow-up period had a sensitivity of 82%, specificity of 77%, and accuracy of 78%. An artificial neural network was added to the expert system, creating an expert network. Input into the neural network consisted of both clinical variables and thallium-201 stress test data. There were 5 hidden nodes and the output (end point) was cardiac death. The expert network increased the specificity of the expert system alone from 77% to 90% (p <0.001), the accuracy from 78% to 89% (p <0.005), and maintained the overall sensitivity at 88%. An expert network based on clinical risk factor screening and thallium-201 stress testing had an accuracy of 89% in predicting the 4-year cardiac mortality among 189 renal transplant candidates. ert network constructed from clinical risk factors, thallium-201 stress test data, and a neural network was shown to be of value in the cardiac risk stratification of renal transplant candidates. With use of this form of artificial intelligence, an accuracy of 89% was obtained in the prediction of cardiac mortality among renal transplant candidates during a 4-year follow-up period.