Ghazal Arabidarrehdor ^1* George C. Kramer ² David Burmeister ³ Jose Salinas ⁴ Jin-Oh Hahn ¹

• ¹ University of Maryland, College Park, College Park, Maryland, United States
• ² University of Texas Medical Branch at Galveston, Galveston, Texas, United States
• ³ Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Illinois, United States
• ⁴ United States Army Institute of Surgical Research, San Antonio, Texas, United States

Treating extensive burn injury requires an individually tailored resuscitation protocol that includes hourly-titrated intravenous fluid infusion to avert both hypovolemic shock and edema. Due to the complexity of burn pathophysiology and significant variability in treatment protocols, there is an ongoing effort to optimize burn resuscitation. The goal of this work is to contribute to this effort by developing a mathematical model of burn pathophysiology and resuscitation for in silico testing of burn resuscitation protocols and decision-support systems. In our previous work, we developed and validated a mathematical model consisting of volume kinetics, burn-induced perturbations, and kidney function. In this work, we expanded our previous mathematical model to incorporate novel mathematical models of cardiovascular system and hormonal system (renin-angiotensinaldosterone (RAAS) system and antidiuretic hormone) which affect blood volume and pressure regulation. We also developed a detailed mathematical model of kidney function to regulate blood volume, pressure, and sodium levels, including components for glomerular filtration rate, reabsorption rates in nephron tubules, Tubuglomerular feedback, and myogenic mechanisms. We trained and validated the expanded mathematical model using experimental data from 15 pigs and 9 sheep with extensive burns to quantitatively evaluate its prediction accuracy for hematocrit, cardiac output, mean arterial pressure, central venous pressure, serum sodium levels, and urinary output (UO). We then trained and tested the mathematical model using a clinical dataset of 233 human burn patients with demographic data and UO measurements. The mathematical model could predict all tested variables very well, while internal variables and estimated parameters were consistent with the literature. To the best of our knowledge, this is the first mathematical model of burn injury and resuscitation which is extensively validated to replicate actual burn patients.Hence, this in silico platform may complement large animal pre-clinical testing of burn resuscitation protocols. Beyond its primary purpose, the mathematical model can be used as a training tool for healthcare providers, delivering insight into the pathophysiology of burn shock, and offering novel mathematical models of human physiology which can be independently used for other purposes and contexts.