# Machine learning-based prediction of one-year mortality after alloHCT identifies the impact of pre-transplant immunity and inflammation

## TL;DR

This study uses machine learning to better predict one-year mortality after a type of stem cell transplant, showing that immune and inflammation markers are key factors.

## Contribution

A machine learning model outperforms existing clinical scores by incorporating pre-transplant immune and inflammatory parameters.

## Key findings

- A random forest model achieved an AUC of 0.773 in predicting one-year mortality after alloHCT.
- Pre-transplant CD4+, CD8+, and B-lymphocyte counts, along with albumin and CRP levels, were identified as key predictors.
- The model outperformed established clinical risk scores like HCT-CI and EASIX.

## Abstract

Accurate prediction of mortality after allogeneic hematopoietic stem cell transplantation (alloHCT) is essential for individualized treatment decisions, yet existing clinical risk scores capture only a limited number of variables and show modest predictive performance. In our single-center retrospective analysis, we included data from 909 adult patients with hematologic malignancies undergoing alloHCT. We used 31 features to build machine-learning models to predict death within the first year after alloHCT. These features included established clinical risk factors together with pre-transplant lymphocyte subsets and inflammatory markers. Among four models, a random forest algorithm showed the best performance (AUC = 0.773) and retained good generalizability in an independent test set (AUC = 0.748). SHapley Additive exPlanations (SHAP)-based interpretation of the machine-learning models showed that age together with five easily measurable pre-transplant immunological and inflammatory parameters influenced the outcome: pre-transplant CD4+, CD8+, and B-lymphocyte counts, albumin, and C-reactive protein (CRP) levels. Based on these features, our random forest approach outperformed established clinical risk scores (HCT-CI, EASIX, rDRI, mGPS) in predicting one-year mortality after alloHCT and more effectively distinguished patients at low and high risk of an adverse outcome. Our study shows that machine-learning-based models can not only predict patient outcomes after alloHCT but also serve as powerful tools for data exploration, confirming the prognostic relevance of pre-transplant inflammation while uncovering the critical role of lymphocyte subsets as previously unknown risk factors. External validation in independent multicenter cohorts will be required to confirm generalizability.