ArticleFrontiers in physiology2019
A Whole-Body Mathematical Model of Sepsis Progression and Treatment Designed in the BioGears Physiology Engine.
Article in Frontiers in physiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Physiology-driven reversible dynamic synthetic tissue model for responsive moulage in infected wound training.PloS one · 2026Article
- A model of anaerobic tissue perfusion during trauma-Lactate trajectory curvature can determine recovery.PLoS computational biology · 2025Article
- Mathematical model of the inflammatory response to acute and prolonged lipopolysaccharide exposure in humans.NPJ systems biology and applications · 2024Article
- Whole body physiology model to simulate respiratory depression of fentanyl and associated naloxone reversal.Communications medicine · 2024Article
- Embracing complexity in sepsis.Critical care (London, England) · 2023Review
- Development and usability testing of a patient digital twin for critical care education: a mixed methods study.Frontiers in medicine · 2023Article
- Predicting the neuro-cardio-haemodynamic outcomes of sepsis and its pharmacological interventions: Get to the future through numerical equations.The Journal of physiology · 2021Article
- BurnCare tablet trainer to enhance burn injury care and treatment.BMC emergency medicine · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a debilitating condition associated with a high mortality rate that greatly strains hospital resources. Though advances have been made in improving sepsis diagnosis and treatment, our understanding of the disease is far from complete. Mathematical modeling of sepsis has the potential to explore underlying biological mechanisms and patient phenotypes that contribute to variability in septic patient outcomes. We developed a comprehensive, whole-body mathematical model of sepsis pathophysiology using the BioGears Engine, a robust open-source virtual human modeling project. We describe the development of a sepsis model and the physiologic response within the BioGears framework. We then define and simulate scenarios that compare sepsis treatment regimens. As such, we demonstrate the utility of this model as a tool to augment sepsis research and as a training platform to educate medical staff.
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