1. From Unit to Dose: A Machine Learning Approach for Precise Prediction of Hemoglobin and Iron Content in Individual Packed Red Blood Cell Units
- Author
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Jeremy Epah, Ilay Gülec, Stefan Winter, Johanna Dörr, Christof Geisen, Eva Haecker, Dietmar Link, Matthias Schwab, Erhard Seifried, and Richard Schäfer
- Subjects
General Chemical Engineering ,General Engineering ,General Physics and Astronomy ,Medicine (miscellaneous) ,General Materials Science ,Biochemistry, Genetics and Molecular Biology (miscellaneous) - Abstract
Transfusion of packed red blood cells (pRBCs) saves lives, but iron overload limits survival of chronically transfused patients. Quality control methods, which involve entering pRBC units and removing them from the blood supply, reveal that hemoglobin (38.5-79.9 g) and heme iron (133.42-276.89 mg) vary substantially between pRBCs. Yet, neither hemoglobin nor iron content can be quantified for individual clinically used pRBCs leading to rules of thumb for pRBC transfusions. Keeping their integrity, the authors seek to predict hemoglobin/iron content of any given pRBC unit applying eight machine learning models on 6,058 pRBCs. Based on thirteen features routinely collected during blood donation, production and quality control testing, the model with best trade-off between performance and complexity in hemoglobin/iron content prediction is identified. Validation of this model in an independent cohort of 2637 pRBCs confirms an adjusted R
- Published
- 2022
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