Development and validation of a mathematical model to simulate human cardiovascular and respiratory responses to battlefield trauma. (25th November 2022)

• Record Type: Journal Article
• Authors: Jin, Xin; Laxminarayan, Srinivas; Nagaraja, Sridevi; Wallqvist, Anders; Reifman, Jaques
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 39:Number 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-25
• Subjects: cardiovascular system -- mathematical model -- respiratory system -- trauma; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.3662
• Languages: English
• ISSNs: 2040-7939

Abstract

Abstract: Mathematical models of human cardiovascular and respiratory systems provide a viable alternative to generate synthetic data to train artificial intelligence (AI) clinical decision‐support systems and assess closed‐loop control technologies, for military medical applications. However, existing models are either complex, standalone systems that lack the interface to other applications or fail to capture the essential features of the physiological responses to the major causes of battlefield trauma (i.e., hemorrhage and airway compromise). To address these limitations, we developed the cardio‐respiratory (CR) model by expanding and integrating two previously published models of the cardiovascular and respiratory systems. We compared the vital signs predicted by the CR model with those from three models, using experimental data from 27 subjects in five studies, involving hemorrhage, fluid resuscitation, and respiratory perturbations. Overall, the CR model yielded relatively small root mean square errors (RMSEs) for mean arterial pressure (MAP; 20.88 mm Hg), end‐tidal CO2 (ETCO2 ; 3.50 mm Hg), O2 saturation (SpO2 ; 3.40%), and arterial O2 pressure (PaO2 ; 10.06 mm Hg), but a relatively large RMSE for heart rate (HR; 70.23 beats/min). In addition, the RMSEs for the CR model were 3% to 10% smaller than the three other models for HR, 11% to 15% for ETCO2, 0% to 33% for SpO2, and 10% to 64% for PaO2, while they were similar for MAP. In conclusion, the CR model balances simplicity and accuracy, while qualitatively and quantitatively capturing human physiological responses to battlefield trauma, supporting its use to train and assess emerging AI and control systems. Abstract : We developed a cardio‐respiratory model, which balances model simplicity and prediction accuracy, to predict physiological responses to the leading causes of battlefield injuries (i.e., hemorrhage and airway compromise) and their associated treatments. The model resulted in more accurate predictions than three other existing models with similar or much higher complexities. The model can be used to generate synthetic data to train emerging AI decision‐support systems and to compare and contrast emerging closed‐loop control systems in support of military medical applications.