Blood Pressure Prediction During Blood Transfusion: A Population-Informed Multi-Modal Sequential Inference Approach

Blood pressure (BP) management is a critical component of blood transfusion, but no mature technology capable of predicting BP response to blood transfusion exists. This paper concerns the development and preliminary in vivo testing of a BP prediction method applicable to hemorrhage and blood transfusion. Key obstacles are (i) large inter-individual variability in the BP response to blood transfusion, (ii) unknown hemorrhage, and (iii) input/state-dependent observability. To cope with these challenges, we developed a multi-modal sequential inference-enabled BP prediction method built upon a mathematical model of patient physiology parameterized by population-informed prior. The method infers patient-specific physiological state and hemorrhage , and uses them to predict future BP in a patient receiving blood transfusion. The in vivo testing of the method using the data collected from large animals undergoing hemorrhage and blood transfusion showed that it could adequately predict mean arterial BP with median absolute errors for 5-min and 15-min predictions of 3.1 mmHg and 7.4 mmHg as well as adequately infer physiological state and hemorrhage: all the hemorrhage events were detected with <3.5 min delay, with median F1 score of 85%. In sum, the prediction of BP response to blood transfusion may be feasible, even in the presence of unknown hemorrhage.