Abstract
Machine learning (ML) has shown its potential to improve patient care over the last decade. In organ transplantation, delayed graft function (DGF) remains a major concern in deceased donor kidney transplantation (DDKT). To this end, we harnessed ML to build personalized prognostic models to predict DGF. Registry data were obtained on adult DDKT recipients for model development (n = 55,044) and validation (n = 6176). Incidence rates of DGF were 25.1% and 26.3% for the development and validation sets, respectively. Twenty-six predictors were identified via recursive feature elimination with random forest. Five widely-used ML algorithms—logistic regression (LR), elastic net, random forest, artificial neural network (ANN), and extreme gradient boosting (XGB) were trained and compared with a baseline LR model fitted with previously identified risk factors. The new ML models, particularly ANN with the area under the receiver operating characteristic curve (ROC-AUC) of 0.732 and XGB with ROC-AUC of 0.735, exhibited superior performance to the baseline model (ROC-AUC = 0.705). This study demonstrates the use of ML as a viable strategy to enable personalized risk quantification for medical applications. If successfully implemented, our models may aid in both risk quantification for DGF prevention clinical trials and personalized clinical decision making.
Highlights
Machine learning (ML) has shown its potential to improve patient care over the last decade
The training set was used for recursive feature elimination with random forest (RFE-RF) to calculate the variable importance score (VIS) for each feature and determine an optimal set of predictors using the area under receiver operating characteristic curve (ROC-AUC) as the performance metric
We would like to clarify that throughout the manuscript, logistic regression (LR) is referred to as a ML algorithm, the appropriate classification of LR is context-dependent and depends upon whether it is used for prediction (ML) or inferential statistics to evaluate associations between the independent variable(s) and dependent variable
Summary
Machine learning (ML) has shown its potential to improve patient care over the last decade. In organ transplantation, delayed graft function (DGF) remains a major concern in deceased donor kidney transplantation (DDKT) To this end, we harnessed ML to build personalized prognostic models to predict DGF. To this end, several prognostic models have been developed using features available prior to transplant that enable early identification of patients at higher risk of DGF6,11–13. Successful implementation of our models may potentially assist with (1) development of DGF prevention clinical trials via accurate risk quantification of study subjects; and (2) personalized clinical decision making for DDKT patients
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