Introduction
The ventilator of our dreams does not exist. New technological tools have the po- tential to either move the ideal ventilator within our reach or make it recede into the distance. Technology is omnipresent in the ICU. The fi eld of mechanical ven- tilation has benefi ted enormously from technological advances over the years, most notably since the development of computer science. Traditionally, the ven- tilator disregarded the patient’s responses to mechanical ventilation, delivering a constant output until the ventilator’s limits were reached. Modern ventilators can react to responses from the patient or respiratory system, thereby improving patient-ventilator synchrony, avoiding added work of breathing, and enhancing tolerance to assisted ventilation. This is achieved through closed-loop technol- ogy, in which the ventilator adapts its output in order to minimize the difference between ventilation results and predefi ned targets. The risks associated with mechanical ventilation have been extensively described, and decreased aggres- siveness or invasiveness is now possible thanks to advanced monitoring systems and diverse ventilation modes that optimize patient-ventilator synchronization.
These advances have grown out of improvements in physiological knowledge and progress in ventilation-output processing. The current surge of technologi- cal possibilities carries both the hope of priceless benefi ts for patients and the specter of major harm. One concern is that manufacturers, driven by commer- cial strategies, may make an increasing number of cosmetic changes that are based on simplistic physiological models and likely to disserve patients. Few of the new ventilation modes have proven clinical advantages [1], yet each of them increases the complexity of ventilators. Another concern is that the increased complexity of ventilator interfaces may multiply the number of setting errors.
These two problems may lead to a third important source of harm, namely, a widening gap between the knowledge, understanding, and clinical skills of the average user and the complexity and sophistication of new machines. Clinicians, physiologists, and industrials must make every effort to minimize this inevita- ble risk.
L. Brochard, M. Dojat, and F. Lellouche
What is Wrong With Mechanical Ventilation and Ventilators Today?
Ventilator-associated lung injury (VALI) is clearly a major threat in patients with the most severe forms of hypoxemic respiratory failure, i.e., acute lung injury (ALI) [2, 3]. When ventilatory settings are adjusted to optimize gas exchange, lung damage occurs, leading to poor outcomes [4, 5]. Thus, mechanical ventila- tion can be harmful to the patient, and trade-offs between gas exchange, hemo- dynamics, and lung distension must be made. Similar fi ndings have been ob- tained in patients with asthma or obstructive lung disease [6, 7]. Surprisingly, the potential for ventilators to cause harm has often received limited attention from clinicians. Although the risk of lung damage can be decreased by simple measures (e.g., limiting plateau pressure and reducing tidal volume), lung pro- tection remains a crucial objective that improved monitoring tools will help us to achieve.
During assisted ventilation, patient-ventilator asynchrony is being increas- ingly recognized as a sign that the ventilator is not meeting the patient’s needs [8, 9]. Evidence from animal experiments shows a considerable impact of me- chanical ventilation on diaphragm function and stresses the importance of maintaining some degree of spontaneous respiratory-muscle activity [10–13].
However, the actual synchrony provided by assisted modes is far from ideal and may not be optimal from the standpoint of preserving the respiratory muscles.
Although it is diffi cult to evaluate the clinical consequences of asynchronies, such as missing efforts, double triggering, or prolonged inspiration, in most cas- es there is little reason to think that asynchrony will benefi t the patient [14–17].
With non-invasive ventilation (NIV), poor synchrony may have direct adverse clinical effects [18].
Leaks are responsible for major dyssynchrony that may be diffi cult to rec- ognize. For instance, in a patient with end-inspiration leaks, pressure-support ventilation may fail to synchronize with the end of the patient’s inspiratory ef- fort, as carefully analyzed by Calderini et al. [19]. In this situation, the ventilator may fail to detect the end-inspiratory off-switch, which may result in prolonged insuffl ation with major dyssynchrony. If leaks occur, the mask should be read- justed and the pressures reduced. In patients with persistent leaks and asyn- chrony, steps must be taken to minimize adverse effects on patient-ventilator interactions. During pressure-support ventilation, adjusting the cycling-off cri- terion or adding an inspiratory time limit helps to prevent the ventilator’s inspi- ration from extending beyond the end of the patient’s neural inspiration. These settings, however, may be diffi cult to access on the ventilator panel, absent, or available only indirectly. This is an interesting example of how manufacturers dealt with physiological and technical issues in a way that should benefi t patients but that also creates new staff-training needs.
Asynchrony detection, on-line feedback to clinicians about the effects of cur- rent ventilatory settings or predicted effects of new settings, better technologies that minimize delays and response times, optimization of control of artifactual signals, or automatic ventilator driving in response to asynchrony are approach- es that may deserve investigation in the future [20, 21]. Clearly, enhanced airway pressure control has considerably improved the sensitivity of inspiratory triggers
but has also increased the risk of self-triggering. The extent to which clinicians should repeatedly optimize ventilatory settings may be diffi cult to determine.
It is important, however, to realize that improper settings can generate major dyssynchrony, make the patient uncomfortable, and/or unnecessarily increase the work of breathing. Whether automated systems that make these adjustments based on reasonable physiological criteria will benefi t both the patient and the clinician is therefore a crucial question for the future of intensive care medicine.
Recently, Prinianakis et al. assessed a new automatic trigger that uses on-line fl ow waveform analysis [22]. They found improved trigger sensitivity with re- duced work of breathing but more frequent auto-triggering. Other equivalent automatic systems have been incorporated into new ventilators but not evalu- ated. Automation of inspiratory cycling-off would also be helpful in improving patient-ventilator synchrony. This parameter is diffi cult to set, as optimal values may range from 1% to 85% of peak fl ow, according to the patient’s respiratory mechanics [15]. Cycling-off based on automatic determination of time constants has been suggested [23], evaluated in a lung model [24], and implemented in a ventilator [25].
Specifi c shapes of the airway pressure and fl ow waveforms are related to pa- tient-ventilator asynchronies [21] such as auto-triggering, ineffective efforts [8, 26, 27], short cycles [28], double cycles [29, 30], prolonged inspirations [28, 31], initial or fi nal overshoot [21, 32], and premature termination [29]. Automatic rec- ognition of these shapes followed by adjustment of the settings that cause them may deserve consideration. Figure 1 gives an example of numerous ineffective efforts that could be diagnosed by the ventilator, both from analysis of the fl ow signal or from esophageal pressure analysis. Figure 2 illustrates the problem of prolonged inspiration during noninvasive ventilation with leaks. The ventilator could perform similar detection automatically.
Undue prolongation of mechanical ventilation may result from failure to de- termine when the patient is ready to be separated from the ventilator. Further- more, even a brief prolongation may lead to ventilation-related complications that require an additional period of mechanical ventilation [33]. Protocols for the so-called weaning phase have been found useful in some cases but unsuc- cessful in others, for many reasons including the diffi culty in implementing such protocols into clinical practice [34, 35]. Closed-loop technology can be used to introduce weaning protocols or automated procedures into ventilatory modes.
Poor quality of the human-machine interface is another common problem.
Excessive complexity is a more common cause than lack of technical options.
Factors include complexity of ventilatory modes and monitoring, poor display legibility, or poor ergonomics to control the settings. Non-intuitive settings, a need for extensive training, and the non-standardized nomenclature used by manufacturers infl uence these factors.
What do we want from the Ventilator of Tomorrow?
Incorporation of modern monitoring tools is a fi rst step toward improving the physiological approach to mechanical ventilation. Pressure volume (PV) curves
of the respiratory system, fi rst used 30 years ago, are now being introduced into ventilator monitoring systems [36]. Unfortunately, a single PV measurement offers limited information, and generation of multiple loops, including tidal curves together with measurement of alveolar recruitment, will be important for optimizing ventilation in the most severely ill patients [37, 38].
Gas exchange monitoring has also improved considerably. Ironically, modern ventilators make little or no use of gas exchange monitoring, although blood gas abnormalities are the leading reason for clinician-driven changes in ventilatory settings. Little attention has been paid to monitoring carbon dioxide (CO2) elim- ination in the intensive care. Today however, volumetric capnography can offer important insights into lung physiology and arterial PCO2 trends can now be ac- curately assessed using transcutaneous measurements, even in adults [39–41].
Respiratory mechanics and work-of-breathing estimates may also help to improve ventilator settings [42, 43]. One diffi culty is the need for semi-inva- sive measurements from esophageal (and gastric) probes, although a number of surrogates exist, such as occlusion pressure, also known as P0.1 [44]. Another common diffi culty is the lack of precise or universally accepted threshold val-
Fig. 1. An example of numerous ineffective efforts that are not detected by the ventilator from the airway pressure curve (Paw), but which could be detected from the fl ow signal (fl ow).
There is a huge difference between the respiratory rate (RR) depicted by the ventilator’s moni- tor and the true rate of the patient. The ventilator, from an automated analysis of the esopha- geal pressure signal (Pes), could also diagnose this and provide the two frequencies.
ues. However, changes in measurement values over time can provide valuable information.
Working with gases that have different densities may have a number of ad- vantages. A mixture of helium and oxygen can be used to measure and monitor lung volume. This mixture may also facilitate diffi cult ventilation and reduce work of breathing, especially during noninvasive ventilation [45–47]. It requires adequate calibration of transducers [48].
Sophisticated ventilation modes have been made possible by improvements in computer science [49, 50]. When the physiological and clinical knowledge need- ed to manage well-defi ned clinical situations is acquired, it can be embedded within a computer program that drives the ventilator using artifi cial intelligence techniques, such as production rules, fuzzy logic, or neural networks. These new techniques allow planning and control. Control is a local task that consists in determining what the immediate next step is. Planning is a strategic task aimed
Fig. 2. Illustration of the problem of prolonged inspiration during non-invasive ventilation with leaks, as seen on the airway pressure tracing (Paw). The end of inspiration is not recog- nized and the insuffl ation time is prolonged well beyond the end of inspiration. The patient has time to expire and to take a second inspiration during the same breath, as indicated by arrows. The fl ow (fl ow) signal has a specifi c shape (arrow). The ventilator, from an automated analysis of the esophageal pressure signal (Pes), could also diagnose this and propose specifi c actions to the clinician, such as limiting the maximal inspiratory time.
at regulating the time-course of the process. For control and planning, numer- ous techniques have been developed in the fi elds of control theory and artifi cial intelligence, respectively. The main difference between these two fi elds lies in the process models used. Control and planning are two complementary and es- sential tasks that must be combined to design multi-level controllers for auto- matically supervising complex systems such as mechanical ventilators. Because the driving process is based on complex physiological models, it is important to avoid both over-simplifi cation and excessive complexity [51]. Strickland and Hasson attempted to develop a controller incorporating an active clinical strat- egy represented by production rules (IF conditions THEN actions), but their work was not followed by commercial development [52, 53]. Today, two control- and-planning systems are available for ventilators: adaptive support ventilation (ASV) [54, 55] and the Smart Care® system, an embedded version of the initial NeoGanesh system. ASV adapts breath frequency and pressure to deliver opti- mized ventilation, taking into account the optimal theoretical work of breath- ing, the need to minimize intrinsic positive end-expiratory pressure (PEEP), and safety limits. Clinical evaluation data remain limited [55]. The NeoGanesh (or Smart Care®) system drives the ventilator with pressure-support ventilation, keeps the patient within a zone of “respiratory comfort” as defi ned by respira- tory parameters, and superimposes an automated strategy for weaning [56–58].
The designers of the knowledge-based NeoGanesh system intended to build a closed-loop system that 1) was effi cient for automatically controlling mechani- cal support and planning of the weaning process, 2) could be evaluated with the goal of gradually improving its reasoning and planning capabilities, and 3) could be subjected at the bedside to performance measurements at each step of its operation. The NeoGanesh system is based on modeling of the medical ex- pertise required to perform mechanical ventilation in pressure-support mode. It does not include mathematical equations of a physiological model. Several types of evaluation have been performed: i) to determine how well the system adapts the level of assistance to the patient’s needs (evaluation of the control level), ii) to assess the extubation recommendation made by the system (evaluation of the strategic level), and iii) to estimate the impact on clinical outcomes. This system has been shown to reduce periods of excessive respiratory efforts and to predict the extubation time with considerable accuracy [57, 58]. It has been used safely during prolonged periods of mechanical ventilation [59] and has been shown recently to reduce the time spent on the ventilator [60].
Several parts of the ventilator are cumbersome, such as the screen and the humidifi cation device (for heated humidifi ers). In intensive care, monitoring the patient is essential but, rather than having several monitors for a given patient (respiratory monitoring, several screens for hemodynamic monitoring purpos- es, oximetry, transcutaneous or end-tidal CO2, etc.), a single screen displaying all the information on the patient could improve the effi ciency of the ICU work- station. The computer fi le could incorporate laboratory and radiology fi ndings and protocols. The screen could be made separate from the ventilator, in order to decrease the bulk of the machine. Humidifi ers could also be incorporated into the ventilator.
The ventilator interface could be improved [61]. Too many names and ac- ronyms exist for each ventilation mode. Although utopian, a standardized no- menclature for modes, settings, and parameters would be highly desirable. This simple measure would improve the ventilator interface and facilitate the teach- ing of mechanical ventilation. Another utopian scheme consists in keeping only those ventilation modes that are truly innovative or useful and in eliminating modes that have not been proven benefi cial or that might put patients at risk. For example, it is ironic to see that synchronized intermittent mandatory ventila- tion (SIMV) remains on all new ventilators, despite abundant evidence that this mode has many drawbacks and lengthens weaning [62–65].
What Can We Foresee for the Ventilator of Tomorrow?
Ventilator-driving systems based on the electromyographic (EMG) signal of the diaphragm can now be envisioned, given the promising results of animal and preliminary clinical studies [66, 67]. Neurally adjusted ventilatory assist offers the best possible synchrony between the patient’s neural drive and the ventila- tory assistance. This can eliminate delays generated by intrinsic PEEP or end-in- spiration asynchronies. Also, the ventilatory signal can be made proportional to the ventilatory demand estimated from the diaphragm EMG [67]. Except in rare cases of uncoupling between the brain signal and the muscle signal, it is very close to brain-driven ventilation. The main limitation is the need for esophageal probe placement, but considerable work has been done to facilitate capturing of the signal.
Proportional-assist ventilation was described as having enormous potential for ventilating patients because it was the only modality specifi cally designed to adapt to changes in ventilatory demand [68]. Unfortunately, it requires continu- ous adjustment to respiratory mechanics. However, measurements of respira- tory mechanics with adjustment of settings according to the results can now be done automatically [69, 70]. Although a bit further from the brain than the EMG signal, this method would provide an approximation of the diaphragm EMG ap- proach without requiring an esophageal probe.
Work performed over the last few years at the LDS Hospital (Salt Lake City, Utah, USA) to develop computerized protocols is relevant here [71, 72]. The algo- rithm-oriented approach chosen for the LDS Hospital studies leads to a complex logic with intricate temporal aspects. The general principle consists in imple- menting ventilatory protocols using an open-loop approach in which the per- sonnel interacts with the ventilator to resolve diffi cult problems in the most ef- fective and standardized manner possible. Computerized tools can facilitate the representation, verifi cation, and execution of such protocols.
Lastly, ventilator interfaces should be much more user-friendly and should offer advice to clinicians. When the ventilator detects asynchrony or inadequate settings, a dialogue should start with the user for the benefi t of the patient. Mod- ern ventilation should be more adept at meeting the patient’s changing needs. At the same time, separation from the ventilator could be considered as soon as the risk of separation and subsequent extubation is deemed no greater than the risk
of continuing the ventilatory assistance. All these actions require close moni- toring and prompt adjustment of ventilatory settings to the patient’s changing needs.
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