Predictive Models for Kidney Offer Acceptance: Challenges and Strategies | JoVE Visualize

Area of Science:

Transplant medicine
Machine learning in healthcare
Predictive modeling

Background:

Organ offer acceptance prediction is challenging due to high volumes, imbalanced data (more declines than acceptances), and limited insight into human decision-making.
Existing offer acceptance models are used for program evaluation and policy development, but best practices and baselines are not well-established.
This study investigates the impact of various machine learning models, feature sets, and sampling procedures on organ offer acceptance prediction.

Purpose of the Study:

To compare the performance of different machine learning models for predicting kidney organ offer acceptance.
To evaluate the impact of incorporating additional features, such as transportation logistics, on model performance.
To assess the effectiveness of different data sampling procedures in improving prediction accuracy.

Main Methods:

Evaluated multiple kidney offer acceptance models, ranging from logistic regression to gradient boosted trees (XGBoost).
Trained models using donor and candidate characteristics, then augmented the best-performing model with transportation-related features and sampling procedures.
Compared model performance using metrics like average precision and AUROC (Area Under the Receiver Operating Characteristic curve).

Main Results:

The XGBoost model demonstrated the best performance improvement over the baseline logistic regression model (average precision increased from 0.0645 to 0.0907).
Incorporating transportation-related features further enhanced model performance (average precision reached 0.0940).
No substantial performance differences were observed based on the sampling procedures used.

Conclusions:

Advanced machine learning models and non-clinical data, like transportation distances, can improve organ offer acceptance prediction.
Significant trade-offs between precision and recall were observed, indicated by low average precision scores despite high AUROCs.
Current models, even optimized ones, may not offer clear advantages over existing organ allocation policies, necessitating further research for clinical applicability.