Machine learning in the prediction of liver iron concentration and iron chelation therapy adjustment.
Authors
Affiliations (13)
Affiliations (13)
- Division of Medical Oncology and Hematology, Department of Medicine, University Health Network, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.
- Vector Institute, Toronto, Canada.
- Division of Hematology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
- Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada.
- Division of Oncology and Hematology, Unity Health Toronto, Toronto, Ontario, Canada.
- Department of Medicine, William Osler Health System, Brampton, Ontario, Canada.
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
- Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
- Toronto General Hospital Research Institute, University Health Network, Toronto, Canada.
- Peter Munk Cardiac Centre, University Health Network, Toronto, Canada.
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.
Abstract
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. 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. 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. 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.