The transfusion of packed red blood cells (pRBCs) saves lives, but iron overload limits the survival of chronically transfused patients. Quality control destroying pRBCs reveals 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, we sought 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, we identified the model with best trade-off between performance and complexity in hemoglobin/iron content prediction. Validation of this model in an independent cohort of 2,637 pRBCs confirmed an adjusted R² > 0.9 for prediction of individual hemoglobin/iron content of pRBCs corresponding to a mean absolute prediction error of ≤1.43 g hemoglobin/4.96 mg iron (associated standard deviation: ≤1.13 g hemoglobin/3.92 mg iron). Such unprecedented precise prediction enables reliable pRBC dosing per pharmaceutically active agent, and monitoring iron uptake in patients and individual iron loss in donors. The model was implemented in a free open-source web application to facilitate clinical application:

https://epahjeremy-prbc-prediction-hbprediction-dceyew.streamlitapp.com/