Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?

Abstract

Both traditional statistics, such as the logistic regression (LR) model, and machine learning (ML) have strengths and limitations for predicting outcomes after kidney transplantation. The LR model is simple, interpretable, and reliable with small datasets. ML can capture complex, nonlinear patterns and manage many variables, but it needs larger, high-quality datasets to reach its full potential. In the recent issue of World Journal of Nephrology, Salgado et al compared six ML models with the LR model using donor, transplant, and recipient data from 523 deceased-donor kidney transplants. Surprisingly, ML models only slightly outperformed the LR model, and overall predictive performance remained modest, especially for identifying patients who developed delayed graft function. These results emphasize that dataset size, completeness, and relevant clinical variables may be more important than algorithm complexity. Future work should focus on improving data quality and developing models that are both accurate and clinically interpretable.

Keywords: Delayed graft function; Kidney transplantation; Predictive modeling; Machine learning; Logistic regression; Donor-recipient risk factors; Data quality; Clinical utility

Core Tip: Delayed graft function significantly impacts kidney transplant outcomes, yet predicting it remains challenging. Recent evidence shows that machine learning (ML) models offer only modest improvements over the traditional logistic regression model when the dataset is of limited quality. High-quality, comprehensive data and interpretable models are critical for accurate risk stratification. Integrating ML with transparent statistical approaches may optimize predictive performance and support clinically meaningful decision-making in transplantation.