Computational modelling of cardiovascular pathophysiology to risk stratify commercial spaceflight

• Published in: Nature reviews cardiology Vol. 21; no. 10; pp. 667 - 681
• Main Authors: Morris, Paul D., Anderton, Ryan A., Marshall-Goebel, Karina, Britton, Joseph K., Lee, Stuart M. C., Smith, Nicolas P., van de Vosse, Frans N., Ong, Karen M., Newman, Tom A., Taylor, Daniel J., Chico, Tim, Gunn, Julian P., Narracott, Andrew J., Hose, D. Rod, Halliday, Ian
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-10-2024; Nature Publishing Group
• Subjects: 631/114; 692/4019; Astronauts; Cardiac Imaging; Cardiac Surgery; Cardiology; Cardiovascular Diseases - diagnosis; Cardiovascular Diseases - physiopathology; Computer Simulation; Humans; Initiatives; Medicine; Medicine & Public Health; Models, Cardiovascular; Pathophysiology; Review Article; Risk Assessment; Space Flight; Weightlessness - adverse effects
• ISSN: 1759-5002; 1759-5010; 1759-5010
• DOI: 10.1038/s41569-024-01047-5

For more than 60 years, humans have travelled into space. Until now, the majority of astronauts have been professional, government agency astronauts selected, in part, for their superlative physical fitness and the absence of disease. Commercial spaceflight is now becoming accessible to members of the public, many of whom would previously have been excluded owing to unsatisfactory fitness or the presence of cardiorespiratory diseases. While data exist on the effects of gravitational and acceleration (G) forces on human physiology, data on the effects of the aerospace environment in unselected members of the public, and particularly in those with clinically significant pathology, are limited. Although short in duration, these high acceleration forces can potentially either impair the experience or, more seriously, pose a risk to health in some individuals. Rather than expose individuals with existing pathology to G forces to collect data, computational modelling might be useful to predict the nature and severity of cardiovascular diseases that are of sufficient risk to restrict access, require modification, or suggest further investigation or training before flight. In this Review, we explore state-of-the-art, zero-dimensional, compartmentalized models of human cardiovascular pathophysiology that can be used to simulate the effects of acceleration forces, homeostatic regulation and ventilation–perfusion matching, using data generated by long-arm centrifuge facilities of the US National Aeronautics and Space Administration and the European Space Agency to risk stratify individuals and help to improve safety in commercial suborbital spaceflight.
During commercial spaceflight, individuals who might have underlying cardiovascular disease will be exposed to increased gravitational and acceleration (G) forces. In this Review, Morris and colleagues explore the use of computational models to simulate the effects of G forces on human cardiovascular pathophysiology to risk-stratify individuals and help to improve safety in commercial suborbital spaceflight.
Key points
Commercial suborbital spaceflight (CSOS) is a relatively new enterprise that, unlike traditional aviation, involves considerable acceleration (G) forces, which represents a new challenge for operators and aviation authorities who are required to develop medical safety guidelines.
Physiologically, CSOS participants are unlike professional astronauts, and the effects of increased G forces in these individuals are largely unknown, particularly in those with ageing or diseased cardiovascular systems.
Computational modelling is being used to simulate the effects of CSOS on the human cardiovascular system in various diseased and comorbid states to assess tolerance to increased G and to help to develop an evidence base to support medical guideline development.
Zero-dimensional, electrical analogue models are apposite for this objective, with a fully transient analysis and representation of homeostatic regulation mechanisms, species (oxygen and carbon dioxide) transport and directional G loading, which can be represented with capacitors.
Challenges include how clinical data are integrated and interpreted in the model and how model outputs will be validated, because exposing potentially high-risk individuals to extreme G forces might be unsafe and, therefore, unethical.