Machine Learning Models for Pre-Transplant Risk Stratification in Severe Aplastic Anemia

Background:

Severe Aplastic Anemia (SAA) is a life-threatening disease characterized by profound pancytopenia. Hematopoietic stem cell transplantation (HCT) has been a curative treatment since the 1970s [Blood.2012;120(6):1185-96] with the outcomes have tremendously improved over the past decades with current survival rates are approximately 82-92%. However, survival is much lower for patients with preexisting co-morbidities [Ann Hematol. 2020; 99(11):2529-2538]. This necessitates a pre-transplant risk stratification tool to personalize treatment and further improve patient outcomes. To our knowledge, there is no universally validated predictive tool for mortality in SAA patients undergoing HCT.

Objective:

Develop and evaluate machine learning models using pre-transplant features to predict mortality in SAA patients and identify the key features that played a vital role in the model's predictions for more personalized treatment.

Methods:

Data Collection & Preparation: An initial dataset of 545 SAA patients who underwent HCT between 1988 and 2018 was collected from the CIBMTR public datasets. The features included recipient, donor, disease, transplant- related variables, demographics, and lab results. The median age of patients was twenty-one years old (range 0-74), female-to- male ratio was 1:1.4, and mortality was 17.5%. No missing values were identified in the target variable of any patient. Missing values in remaining variables were imputed using the KNN imputer if more than 50% of the patients had missing data. Polynomial and interaction terms were generated to capture non-linear relationships. The data was split into an 80:20 training/validation set for hyperparameter fine-tuning.

Model Training: Machine learning models, such as RandomForest, Gradient Boosting, and XGBoost classifiers, were trained using a comprehensive preprocessing pipeline. Hyperparameter tuning was performed using GridSearchCV, and class imbalance was handled using SMOTE. Cross-validation was conducted using StratifiedKFold with five folds.

Model Evaluation: We assessed overall model discrimination using AUROC, and the balance of model sensitivity and positive predictive value using Area Under the Precision-Recall Curve (AUPRC) and F1 score. Feature importance was evaluated using SHAP (SHapley Additive exPlanations) value analysis. The robustness of the models was further verified through additional cross-validation strategies to ensure consistency in performance.

Results:

Among all models, the GradientBoosting model demonstrated high performance with a ROC AUC of 0.835 and AUPRC of 0.708. It showed high recall for class 0, live (0.95) with a good balance between precision and recall (F1-score of 0.92). Key predictors included sex, donor & recipient age, blood counts at diagnosis and graft type. Interactions between demographic (sex, age) and clinical features (blood counts at diagnosis, graft type) were significant.

Conclusion:

Our pre-transplant risk stratification model can potentially predict mortality in SAA patients undergoing HCT. Higher predictive power can be achieved with more comprehensive pre-transplant data. Future directions include validating the model on a separate dataset and in larger, multi-center prospective datasets. Continued research and collaboration are essential to advance these models and improve HCT outcome. Understanding the key predictors and their interactions can help clinicians identify high-risk patients and tailor treatment. We highlight the need for larger datasets with all relevant variables, including longitudinal dynamic ones, to further refine clinical practice.

No relevant conflicts of interest to declare.