BACKGROUND: Delayed graft function (DGF) is one of the most important complications in the post-transplant period, having an adverse effect on both the immediate and long-term graft survival. In this study, an artificial neural network was used to predict the occurrence of DGF and compared with traditional logistical regression models for prediction of DGF. METHODS: A total of 304 cadaveric renal transplants performed at the Jewish Hospital, Louisville were included in the study. Covariate analysis by artificial neural networks and traditional logistical regression were done to predict the occurrence of DGF. RESULTS: The incidence of DGF in this study was 38%. Logistic regression analysis was more sensitive to prediction of no DGF (91 vs 70%), while the neural network was more sensitive to prediction of yes for DGF (56 vs 37%). Overall prediction accuracy for both logistic regression and the neural network was 64 and 63%, respectively. Logistic regression was 36.5% sensitive and 90.7% specific. The neural network was 63.5% sensitive and 64.8% specific. The only covariate with a P < 0.001 was the transplant of a white donor kidney to a black recipient. Cox proportional hazard regression was used to test for the negative effect of DGF on long-term graft survival. One year graft survival in patients without DGF was 92 +/- 2% vs 81 +/- 3% in patients with DGF. The 5-year graft survival was not affected by DGF in this study. CONCLUSION: Artificial neural networks may be used for prediction of DGF in cadaveric renal transplants. This method is more sensitive but less specific than logistic regression methods.
BACKGROUND: Delayed graft function (DGF) is one of the most important complications in the post-transplant period, having an adverse effect on both the immediate and long-term graft survival. In this study, an artificial neural network was used to predict the occurrence of DGF and compared with traditional logistical regression models for prediction of DGF. METHODS: A total of 304 cadaveric renal transplants performed at the Jewish Hospital, Louisville were included in the study. Covariate analysis by artificial neural networks and traditional logistical regression were done to predict the occurrence of DGF. RESULTS: The incidence of DGF in this study was 38%. Logistic regression analysis was more sensitive to prediction of no DGF (91 vs 70%), while the neural network was more sensitive to prediction of yes for DGF (56 vs 37%). Overall prediction accuracy for both logistic regression and the neural network was 64 and 63%, respectively. Logistic regression was 36.5% sensitive and 90.7% specific. The neural network was 63.5% sensitive and 64.8% specific. The only covariate with a P < 0.001 was the transplant of a white donor kidney to a black recipient. Cox proportional hazard regression was used to test for the negative effect of DGF on long-term graft survival. One year graft survival in patients without DGF was 92 +/- 2% vs 81 +/- 3% in patients with DGF. The 5-year graft survival was not affected by DGF in this study. CONCLUSION: Artificial neural networks may be used for prediction of DGF in cadaveric renal transplants. This method is more sensitive but less specific than logistic regression methods.
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