Replacement of animal models of cardiac arrest and resuscitation strategies using a computer simulation

Cardiac arrest is caused by an electrical problem in the heart causing the heart to stop pumping blood around the body and to the brain, causing the person to fall unconscious and stop breathing. Survival can be increased significantly by the early use of cardiopulmonary resuscitation (CPR). CPR is attempted in nearly 30,000 people who suffer out-of-hospital cardiac arrest in England each year, but survival rates are low and compare unfavourably to a number of other countries. Return of spontaneous circulation (ROSC) is achieved in about 25% of these attempts. Only 8% of people in whom resuscitation is attempted survive to hospital discharge. The optimal combination and pattern of chest compressions and ventilation of the lungs has not been established. Recommendations from the European Resuscitation Council have shifted over the years in this respect, but the evidence basis is weak. Similarly, post-resuscitation care has evolved, and cardiac arrest outcomes have improved as a result of post-arrest organ support. Despite these incremental improvements in resuscitation, mortality rates after cardiac arrest remain high. Cardiac arrest and the post resuscitation strategies concern a wide range of patients characterized by different co-morbidities.

Due to the difficulty of conducting large trials in the field of cardiac arrest and due to the lack of homogeneity in animal studies, the scientific evidence supporting the CPR and post-resuscitation treatment still suffers from important gaps in the knowledge of physiopathology, treatment and the best methods for predictive evaluation. In particular, although animal models are essential in advancing resuscitation research, they are susceptible to various biases compromising internal and external validity, which may explain unsuccessful transition to human clinical trials.

Computational modelling offers fresh approach to research into medical issues. In contrast to trials on animal models and humans, in-silico models of individualised patient and disease-pathology are low-cost, highly flexible and are amendable to detailed validation, assuring reproducibility and translation into human application.  Our simulation suite, the Interdisciplinary Collaboration in Systems Medicine (ICSM), is a set of integrated, high-fidelity cardiopulmonary models, offering an excellent opportunity to test new CPR and post-resuscitation strategies, with infinitely repeatable and configurable simulations examining a broad variety of outcome measures. The ICSM suite has already been extensively validated against different patient data and ventilation strategies, such as apnoea, COPD, ARDS patients.  With the ICSM simulator will be able to obtain novel quantitative knowledge of the pathophysiological state of cardiac arrest, create an original in-silico population of subjects suffering of this disease with a range of co-morbidities (e.g. obesity, asthma and COPD), validate them against published data and our prospective data, expose the configured population of in-silico subjects to the intervention of interest and develop new intervention strategies for future applications of personalized therapeutic interventions.

The principal advantage of in-silico models of individualised patient- and disease-pathology is that they are completely configurable and reproducible, replicating extreme variations, and different treatments can be applied to the same spectrum or subset of virtual patients in in multiple different crisis scenarios. A GUI (graphic user interface) will be implemented to eliminate the need to learn a language to run the application and making the simulator friendlier and easier for other researchers who do not have programming skills. Finally, the healthcare industry could consider incorporating our high-fidelity modelling technology into next-generation mechanical ventilators in order to achieve real-time, personalised, patient simulation that can guide the treatment of critical illness.

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PhD Studentship

Status:

Not yet active

Principal investigator

Professor Jonathan Hardman

Institution

University of Nottingham

Co-Investigator

Dr Marianna Laviola

Grant reference number

NC/S001328/1

Award date:

Apr 2019 - Mar 2022

Grant amount

£90,000