Autonomous lung simulators for mechanical ventilation training in neonatology

Managing challenges in mechanical ventilation training

Introduction

Mechanical ventilation (non-invasive and invasive) is one of the cornerstones in the treatment of preterm and term born infants. Although respiratory support contributes to the survival and long-term wellbeing of this infants it is also associated with short-term (air leak syndrome, pulmonary haemorrhage,…) and long-term (chronic lung disease or bronchopulmonary dysplasia) complications.

Training in mechanical ventilation for neonatology nurses and doctors is therefore vital to reduce the potential harm of this therapy.

Challenges in mechanical ventilation training

The vulnerable lung of the premature baby does not allow mistakes in the ventilator settings as it may only take a few breaths with too high tidal volume or inspiratory pressure to cause irreparable damage. To overcome the problem of “learning by doing”, different simulation programs and lung simulators have been developed. These simulators allow to try different ventilation strategies on typical neonatal scenarios and to even train extreme situations.

A downside of conventional simulation programs and lung simulators is the fact, that the response of the lung to the mechanical ventilation needs the input of the trainer. Although very useful for team simulations this technique does not allow individualised ad hoc training of junior staff, as it always requires the presence of experienced senior staff.

Autonomous lung simulators

Low threshold training of junior staff may be achieved with an easy to use simulator where the response to a specific treatment is driven by built-in physiological based models not requiring any manual input once the simulation is running. LuSi (neosim, Switzerland) is such a lung simulator, not only looking like a sick late preterm baby but also behaving as such when correctly programmed.

Potential fields of application

As mentioned above, the main difference (and in my opinion the big advantage) compared to conventional simulator programs is the fact, that no “driver” is necessary throughout the simulation. This makes autonomous simulators very versatile in their application.

They can be used like conventional simulator programs for team simulation trainings. Furthermore they can be employed for hands-on teaching sessions from physiological basics to highly sophisticated ventilation strategies. The physiological feedback allows for real-time assessment of ventilator settings and patient-ventilator interactions.

Given an easy-to-use interface, autonomous simulators can also be used for self-training at times of lower workload. A permanent training space on the neonatology ward set up with a simulator, a ventilator and – for more advanced training – a patient monitor, allows the staff to have individual training sessions whenever someone finds time to turn on the simulator and the ventilator.

Programming of scenarios

In every simulation the training is only as good as the programmed scenarios. This also holds true with autonomous simulators. The better the physiological models used with the simulators the more complicated and also cumbersome is the programming of scenarios. Programming a realistic patient situation with a reasonable evolution requires quite some physiological knowledge and, like any other programming, a lot of time. This challenge can at least partially been overcome by providing platforms for sharing scenarios.

Personal experience with LuSi (neosim, Switzerland)

When I first got to know LuSi I had a thorough introduction to its programming but also to its mechanics. For me it is very important to know the way it works, making it understandable what potential limitations are and what to expect in different scenarios.

The physiological model used to mimic natural behaviour of the simulator are quite complex. A lot of different parameters can be changed whilst programming a new scenario. It is therefore very helpful that these parameters are grouped in main folders. Most of the values have a short description in a “mouse-over” menu. So once familiar with the way programming of scenarios is performed you will quickly be able to implement even rarely used parameters into your programming without the manual. Simple scenarios can be programmed offline, but for more sophisticated patient situations I strongly recommend to program whilst running the simulator with a ventilator. Some of the functions will not lead to the expected response of the simulator, but you will eventually find a way around it.

I personally used LuSi for trainings of different levels from introducing junior NICU nurses to the principles of mechanical ventilation to simulation of extreme patient conditions with experienced neonatologists. Respiratory physiology can be exemplified very easily with two or three different scenarios. If any questions arise you will always be able to make the requested changes on the spot and in addition to the theoretical input you can show it directly on the ventilator/patient monitor. Having worked with “conventional” simulation programs before, the most striking difference to this new generation of simulators is the fact that you always have enough time for your students, as you don’t have to find an adequate response of the patient. It is always there already.

Conclusions

Autonomous lung simulators offer totally new possibilities for mechanical ventilation training in neonatology. Minimal requirements for programming, compatibility with any given ventilator and wireless communication will result in easier application and therefore help to implement simulation of neonatal respiratory diseases into day to day work in NICUs.