A predictive model for kidney transplant graft survival using machine learning
This research offers an incremental improvement in predicting kidney transplant outcomes for clinicians and patients, potentially leading to better decision-making regarding transplant viability.
The study developed a random forest model to predict kidney transplant graft survival, using a dataset of 70,242 observations from 1995-2005. The model successfully predicted an additional 2,148 transplants compared to the existing kidney donor risk index, maintaining equal type II error rates of 10%.
Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.