Machine-Learning Predictive Tool for the Individualized Prediction of Outcomes of Hematopoietic Cell Transplantation for Sickle Cell Disease: Registry-Based Study.

BACKGROUND

Disease-modifying therapies ameliorate disease severity of sickle cell disease (SCD), but hematopoietic cell transplantation (HCT), and more recently, autologous gene therapy are the only treatments that have curative potential for SCD. While registry-based studies provide population-level estimates, they do not address the uncertainty regarding individual outcomes of HCT. Computational machine learning (ML) has the potential to identify generalizable predictive patterns and quantify uncertainty in estimates, thereby improving clinical decision-making. There is no existing ML model for SCD, and ML models for HCT for other diseases focus on single outcomes rather than all relevant outcomes.

OBJECTIVE

This study aims to address the existing knowledge gap by developing and validating an individualized ML prediction model SPRIGHT (Sickle Cell Predicting Outcomes of Hematopoietic Cell Transplantation), incorporating multiple relevant pre-HCT features to make predictions of key post-HCT clinical outcomes.

METHODS

We applied a supervised random forest ML model to clinical parameters in a deidentified Center for International Blood and Marrow Transplant Research (CIBMTR) dataset of 1641 patients who underwent HCT between 1991 and 2021 and were followed for a median of 42.5 (IQR 52.5;range 0.3-312.9) months. We applied forward and reverse feature selection methods to optimize a set of predictive variables. To counter the imbalance bias toward predicting positive outcomes due to the small number of negative outcomes, we constructed a training dataset, taking each outcome as variable of interest, and performed 2-times repeated 10-fold cross-validation. SPRIGHT is a web-based individualized prediction tool accessible by smartphone, tablet, or personal computer. It incorporates predictive variables of age, age group, Karnofsky or Lansky score, comorbidity index, recipient cytomegalovirus seropositivity, history of acute chest syndrome, need for exchange transfusion, occurrence and frequency of vaso-occlusive crisis (VOC) before HCT, and either a published or custom chemotherapy or radiation conditioning, serotherapy, and graft-versus-host disease prophylaxis. SPRIGHT makes individualized predictions of overall survival (OS), event-free survival, graft failure, acute graft-versus-host disease (AGVHD), chronic graft-versus-host disease (CGVHD), and occurrence of VOC or stroke post-HCT.

RESULTS

The model's ability to distinguish between positive and negative classes, that is, discrimination, was evaluated using the area under the curve, accuracy, and balanced accuracy. Discrimination met or exceeded published predictive benchmarks with area under the curve for OS (0.7925), event-free survival (0.7900), graft failure (0.8024), acute graft-versus-host disease (0.6793), chronic graft-versus-host disease (0.7320), and VOC post-HCT (0.8779). SPRIGHT revealed good calibration with a slope of 0.87-0.96, with small negative intercepts (-0.01 to 0.03), for 4 out of the 5 outcomes. However, OS exhibits nonideal calibration, which may be reflective of the overall high OS in all subgroups.

CONCLUSIONS

A web-based ML prediction tool incorporating multiple clinically relevant variables predicts key clinical outcomes with a high level of discrimination and calibration and has potential in shared decision-making.