Digital Twin Mathematical Models Suggest Individualized Hemorrhagic Shock Resuscitation Strategies

Authors

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 Fox
Steven C Chang
Andrew P Cap
Yoram Vodovotz

Publication Date

6-12-2024

Journal

Communications Medicine

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.

Keywords

Medical research, Computational biology and bioinformatics

Comments

Supplementary Materials

PMID: 38867000