Machine Learning Support for Decision-Making in Kidney Transplantation: Step-by-step Development of a Technological Solution

Abstract

Background Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. To maximize patient and graft survival, the allocation of donor organs to potential recipients requires careful consideration. Objective This study aimed to develop an innovative technological solution to enable better prediction of kidney transplant survival for each potential donor-recipient pair. Methods We used deidentified data on past organ donors, recipients, and transplant outcomes in the United States from the Scientific Registry of Transplant Recipients. To predict transplant outcomes for potential donor-recipient pairs, we used several survival analysis models, including regression analysis (Cox proportional hazards), random survival forests, and several artificial neural networks (DeepSurv, DeepHit, and recurrent neural network [RNN]). We evaluated the performance of each model in terms of its ability to predict the probability of graft survival after kidney transplantation from deceased donors. Three metrics were used: the C-index, integrated Brier score, and integrated calibration index, along with calibration plots. Results On the basis of the C-index metrics, the neural network–based models (DeepSurv, DeepHit, and RNN) had better discriminative ability than the Cox model and random survival forest model (0.650, 0.661, and 0.659 vs 0.646 and 0.644, respectively). The proposed RNN model offered a compromise between the good discriminative ability and calibration and was implemented in a technological solution of technology readiness level 4. Conclusions Our technological solution based on the RNN model can effectively predict kidney transplant survival and provide support for medical professionals and candidate recipients in determining the most optimal donor-recipient pair.

Major areas of interest of artificial intelligence research applied to health care administrative data: a scoping review

“…The popularity of these methods could be explained by the fact that they are generally associated with a greater potential for explainability of the results, which facilitates their translation into clinical practice. Other methods, such as support vector machine and recurrent neural networks, have also gained popularity in recent years, as they are known to show better performance for certain tasks ( Paquette et al, 2021 ). Traditional statistical methods are also widely used.…”

Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts

“…A study evaluating these models indicated that neural network-based models, particularly the RNN, exhibited superior discriminative abilities (with scores of 0.65, 0.66, and 0.66, respectively) compared to the Cox model and random survival forest model (with scores of 0.65 and 0.64, respectively). The RNN model thus strikes a balance between accurate predictions and practical applicability for healthcare professionals [66].…”

Artificial Intelligence Advances in Transplant Pathology

“…A summary of these studies is shown in Table 3. Furthermore, several review papers discussing AI in kidney transplantation and the assessment of renal transplant prognosis using classical machine learning approaches are available for interested readers in the additional resources [27,[40][41][42][43][44][45]. Sensitivity of 90-93% for intra-institutional and 77% for inter-institutional dataset…”