Optimising mechanical ventilation through model-based methods and automation

Abstract

Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload.Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

“…Adjusting MV according to patient-specific condition and needs is important due to the highly heterogeneous nature of human lung and how they response to treatment [1], [18], [87]. In addition, setting patient-specific ventilation is difficult due to lack of real-time diagnostic.…”

“…In recent years, technology and wireless communication advances have contributed significantly to the potential of model-based methods in providing patient-specific mechanical ventilation treatment [18], [20]. A range of realtime tools have been developed to capture patient data [32]- [35].…”

“…P0 is the offset pressure and can be assumed as the positive endexpiratory pressure (PEEP) if there is no intrinsic PEEP [34]. The model has been widely used in studying patient-specific lung mechanics in ventilated ICU patients and Neonatal ICU patients [18], [52]- [57]. Golinger et al have shown that these parameters are associated with mortality [58], and it should be minimized to improve patient outcome.…”

“…Model-based methods are a growing means of personalising care [13]- [16]. While many models can successfully capture lung dynamics [17], very few can accurately predict the pulmonary response over time [10], [18]- [20]. In addition, these model-based works are performed retrospectively, and there is a need of a platform to enable real-time MV data monitoring, processing and to provide decision support information to the clinicians.…”

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

“… 7 , 9 , 43 , 44 , 61 However, a one-size-fits-all approach may benefit many or most patients, but may cause harm to others, failing to cater to individual patient-specific needs. 2 , 11 , 28 , 32 , 46 …”