Machine learning in the prediction of liver iron concentration and iron chelation therapy adjustment

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUROC 0.83 for iron overload prediction (PTM without drug features)

• Primary Outcome: Liver iron concentration prediction and iron overload status classification

• Nnt Or Sensitivity: High concordance between ML predictions and hematologist decisions for chelation drug type changes

• Confidence Interval: 95% CI 0.75-0.91

Clinical Application

Requires FerriScan MRI capability and comprehensive clinical data systems; computationally intensive ML infrastructure needed Relevant to Australian haematology centres with FerriScan capability; would require TGA approval for clinical decision support use and validation in Australian patient populations Adult patients with haemoglobinopathies or hemolytic anemias requiring iron chelation therapy in tertiary care settings

Abstract

OBJECTIVES: Iron chelation therapy titration is managed by hematologists who monitor iron levels and adjust medication dosages to achieve patient outcomes. This study developed a machine learning (ML) algorithm to predict the liver iron concentration (LIC) and chelation therapy adjustments. METHODS: This retrospective, single-centre cohort study included adult patients who underwent FerriScan MRI to obtain LIC for chelation therapy monitoring. Using an XgBoost-based ML framework, the proximal-time model (PTM) utilised clinical/drug, laboratory and MRI data features from one visit prior to the target visit, whereas the all-time model (ATM) utilised the data from all prior visits. RESULTS: 94 patients with 892 consecutive visits between January 2008 and November 2018 were included in this study. We assessed the prediction capabilities of the PTM and ATM in LIC, changes to chelation drug type and dosage changes. The PTM model was superior to the ATM model in all the experiments. When drug features were excluded, the CLICT model for predicting patient iron overload status improved to an AUROC of 0.83 [95% CI 0.75-0.91] for PTM; compared to an AUROC of 0.73 [95% CI 0.66-0.80] when drug features were included.For predicting changes in chelation type, the CLICT model showed AUROC of 0.83 [95% CIs 0.77-0.89] for PTM. There is high concordance of the agreement of hematologists with ML in not changing the chelation drug type. DISCUSSION/CONCLUSION: The ML model is a step toward creating a clinical decision support system tool for the prediction of LIC and iron chelation therapy adjustment in patients with haemoglobinopathies or hemolytic anemias.

References

1. 1.Loh, J. B., Kim, S., Ward, R., Sagheb, S., Binding, A., Kuo, K. H. M., McIntosh, C., & Jhaveri, K. (2026). Machine learning in the prediction of liver iron concentration and iron chelation therapy adjustment. Hematology, advance online publication. https://doi.org/10.1080/16078454.2026.2647314