Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies

Jeremy W. Cannon*, Danielle S. Gruen, Ruben Zamora, Noah Brostoff, Kelly Hurst, John H. Harn, Fayten El-Dehaibi, Zhi Geng, Rami Namas, Jason L. Sperry, John B. Holcomb, Bryan A. Cotton, Jason J. Nam, Samantha Underwood, Martin A. Schreiber, Kevin K. Chung, Andriy I. Batchinsky, Leopoldo C. Cancio, Andrew J. Benjamin, Erin E. FoxSteven C. Chang, Andrew P. Cap, Yoram Vodovotz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Optimizing resuscitation to reduce inflammation and organ dysfunction following human trauma-associated hemorrhagic shock is a major clinical hurdle. This is limited by the short duration of pre-clinical studies and the sparsity of early data in the clinical setting. Methods: We sought to bridge this gap by linking preclinical data in a porcine model with clinical data from patients from the Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) study via a three-compartment ordinary differential equation model of inflammation and coagulation. Results: The mathematical model accurately predicts physiologic, inflammatory, and laboratory measures in both the porcine model and patients, as well as the outcome and time of death in the PROMMTT cohort. Model simulation suggests that resuscitation with plasma and red blood cells outperformed resuscitation with crystalloid or plasma alone, and that earlier plasma resuscitation reduced injury severity and increased survival time. Conclusions: This workflow may serve as a translational bridge from pre-clinical to clinical studies in trauma-associated hemorrhagic shock and other complex disease settings.

Original languageEnglish
Article number113
JournalCommunications Medicine
Volume4
Issue number1
DOIs
StatePublished - Dec 2024
Externally publishedYes

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