Prediction of Graft Survival of Living-Donor Kidney Transplantation: Nomograms or Artificial Neural Networks?

Background. An artificial neural networks (ANNs) model was developed to predict 5-year graft survival of living-donor kidney transplants. Predictions from the validated ANNs were compared with Cox regression-based nomogram. Methods. Out of 1900 patients with living-donor kidney transplant; 1581 patients were used for training of the ANNs (training group), the remainder 319 patients were used for its validation (testing group). Many variables were correlated with the graft Survival by univariate analysis. Significant ones were used for ANNs construction of a predictive model. The same variables were subjected to a multivariate statistics using Cox regression model; their result was the basis of a nomogram construction. The ANNs predictive model and the nomogram were used to predict the graft survival of the testing group. The predicted probability(s) was compared with the actual survival estimates. Results. The ANNs sensitivity was 88.43% (95% confidence interval [CI] 86.4-90.3), specificity was 73.26% (95% Cl 70-76.3), and predictive accuracy was 88% (95% CI 87-90) in the testing group, whereas nomogram sensitivity was 61.84% (95% CI 50-72.8) with 74.9% (95% Cl 69-80.2) specificity and predictive accuracy was 72% (95% CI 67-77). The positive predictive value of graft survival was 82. 1% and 43.5% for the ANNs and Cox regression-based nomogram, respectively, and the negative predictive value was 82% and 86.3% for the ANNs and Cox rcgression-based nomogram, respectively. Predictions by both models fitted well with the observed findings. Conclusions. These results suggest that ANNs was more accurate and sensitive than Cox regression-based nomogram in predicting 5-year graft Survival.