New modes of assisted mechanical ventilation

Neurally Adjusted Ventilatory Assist in Acute Respiratory Failure-A Narrative Review

Maintaining spontaneous breathing has both potentially beneficial and deleterious consequences in patients with acute respiratory failure, depending on the balance that can be obtained between the protecting and damaging effects on the lungs and the diaphragm. Neurally adjusted ventilatory assist (NAVA) is an assist mode, which supplies the respiratory system with a pressure proportional to the integral of the electrical activity of the diaphragm. This proportional mode of ventilation has the theoretical potential to deliver lung- and respiratory-muscle-protective ventilation by preserving the physiologic defense mechanisms against both lung overdistention and ventilator overassistance, as well as reducing the incidence of diaphragm disuse atrophy while maintaining patient–ventilator synchrony. This narrative review presents an overview of NAVA technology, its basic principles, the different methods to set the assist level and the findings of experimental and clinical studies which focused on lung and diaphragm protection, machine–patient interaction and preservation of breathing pattern variability. A summary of the findings of the available clinical trials which investigate the use of NAVA in acute respiratory failure will also be presented and discussed.

Proportional modes of ventilation: technology to assist physiology

Proportional modes of ventilation assist the patient by adapting to his/her effort, which contrasts with all other modes. The two proportional modes are referred to as neurally adjusted ventilatory assist (NAVA) and proportional assist ventilation with load-adjustable gain factors (PAV+): they deliver inspiratory assist in proportion to the patient’s effort, and hence directly respond to changes in ventilatory needs. Due to their working principles, NAVA and PAV+ have the ability to provide self-adjusted lung and diaphragm-protective ventilation. As these proportional modes differ from ‘classical’ modes such as pressure support ventilation (PSV), setting the inspiratory assist level is often puzzling for clinicians at the bedside as it is not based on usual parameters such as tidal volumes and PaCO₂ targets. This paper provides an in-depth overview of the working principles of NAVA and PAV+ and the physiological differences with PSV. Understanding these differences is fundamental for applying any assisted mode at the bedside. We review different methods for setting inspiratory assist during NAVA and PAV+ , and (future) indices for monitoring of patient effort. Last, differences with automated modes are mentioned.

A Critical Review of Mechanical Ventilation Virtual Simulators: Is It Time to Use Them?

BACKGROUND

Teaching mechanical ventilation at the bedside with real patients is difficult with many logistic limitations. Mechanical ventilators virtual simulators (MVVS) may have the potential to facilitate mechanical ventilation (MV) training by allowing Web-based virtual simulation.

OBJECTIVE

We aimed to identify and describe the current available MVVS, to compare the usability of their interfaces as a teaching tool and to review the literature on validation studies.

METHODS

We performed a comparative evaluation of the MVVS, based on a literature/Web review followed by usability tests according to heuristic principles evaluation of their interfaces as performed by professional experts on MV.

RESULTS

Eight MVVS were identified. They showed marked heterogeneity, mainly regarding virtual patient's anthropomorphic parameters, pulmonary gas exchange, respiratory mechanics and muscle effort configurations, ventilator terminology, basic ventilatory modes, settings alarms, monitoring parameters, and design. The Hamilton G5 and the Xlung covered a broader number of parameters, tools, and have easier Web-based access. Except for the Xlung, none of the simulators displayed monitoring of arterial blood gases and alternatives to load and save the simulation. The Xlung obtained the greater scores on heuristic principles assessments and the greater score of easiness of use, being the preferred MVVS for teaching purposes. No strong scientific evidence on the use and validation of the current MVVS was found.

CONCLUSIONS

There are only a few MVVS currently available. Among them, the Xlung showed a better usability interface. Validation tests and development of new or improvement of the current MVVS are needed.