Peer Reviewed Articles on Newer Venitlation Modes 2016 2017
Breathe (Sheff). 2017 Jun; 13(2): 84–98.
Trends in mechanical ventilation: are we ventilating our patients in the best possible way?
Raffaele L. Dellaca'
1Dipartimento di Elettronica, Informazione e Bioingegneria – DEIB, Politecnico di Milano Academy, Milan, Italy
Chiara Veneroni
aneDipartimento di Elettronica, Informazione due east Bioingegneria – DEIB, Politecnico di Milano Academy, Milan, Italy
Ramon Farre'
iiUnitat de Biofísica i Bioenginyeria, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Kingdom of spain
3CIBER de Enfermedades Respiratorias, Madrid, Spain
Abstract
This review addresses how the combination of physiology, medicine and engineering science principles contributed to the evolution and advancement of mechanical ventilation, emphasising the most urgent needs for comeback and the most promising directions of time to come development.
Several aspects of mechanical ventilation are introduced, highlighting on 1 side the importance of interdisciplinary research for further development and, on the other, the importance of grooming physicians sufficiently on the technological aspects of modernistic devices to exploit properly the great complexity and potentials of this handling.
Educational aims
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To learn how mechanical ventilation developed in contempo decades and to provide a better agreement of the actual applied science and practice.
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To learn how and why interdisciplinary research and competences are necessary for providing the best ventilation treatment to patients.
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To understand which are the nearly relevant technical limitations in modern mechanical ventilators that can touch on their performance in delivery of the treatment.
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To better sympathize and classify ventilation modes.
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To acquire the classification, benefits, drawbacks and time to come perspectives of automatic ventilation tailoring algorithms.
Short abstract
Physiology, medicine and engineering principles accept contributed to the advancement of mechanical ventilation http://ow.ly/Fjlb30bFpLl
Introduction
Even though the concept of mechanical ventilation dates back to the 14th century with Vesalius [one], it is only in the concluding century that it has been widely introduced into routine clinical do. Since their initial appearance, mechanical ventilators take become more than sophisticated and expanded their application from the intensive care unit (ICU) to the respiratory medicine ward and even to patients' homes for long-term treatments. This was the outcome of combining the advances in our understanding of respiratory physiology, pathophysiology and clinical direction of patients together with technological progress in mechanical, electronic and biomedical applied science.
Nowadays, this evolution is withal rapid, with new devices and an increased number of ventilation modes and strategies beingness introduced to improve outcomes, patient–ventilator interactions and patient care. Applied science has played and is still playing a relevant role in this process, not only in improving the technical functioning of the ventilators just also in contributing to a better understanding of respiratory physiology and pathophysiology, and of how different ventilation strategies interact with the respiratory system.
This review addresses how the combination of physiology, medicine and engineering principles has contributed to the development and advocacy of mechanical ventilation, highlighting the open issues and the most urgent needs for comeback with a view on the most promising directions of future development.
Evolution of mechanical ventilation
The employ of mechanical tools to assist ventilation dates dorsum to the tardily 19th century, when devices able to utilise an alternating subatmospheric pressure around the torso were used to restore ventilation past expanding the breast wall of patients [two]. Notwithstanding, it was simply after the introduction of positive-force per unit area ventilation during the reappearance of poliomyelitis in the 1950s, when Bjorn Ibsen demonstrated a dramatic reduction of bloodshed in patients manually ventilated by tracheostomy, that mechanical ventilation started to be widespread [three].
The first positive-pressure level mechanical ventilators became available in 1940. Though characterised by a meaning degree of sophistication, they were able to evangelize just a pre-set tidal volume at a given respiratory rate (volume-control ventilation mode) with no or very express capability to monitor ventilation variables (figure 1a).
Evolution of the concept of mechanical ventilation. a) The start mechanical ventilators were not equipped with sensors. b) Mechanical ventilators monitor all the ventilation parameters, allowing both closed-loop control of the generated waveform and providing information to the clinicians. c) Mechanical ventilators monitor the condition of the patients and automatically accommodate the ventilatory parameters on the basis of patients' needs.
At that time, respiratory physiology had already established its foundations and was apace growing. The application of mathematical modelling to draw the relationships between flow and pressure (a wonderful example of what today would be considered a biomedical engineering approach), which marked the dawn of the mechanics of breathing, was introduced by Dixon and Brodie [4] in 1903, who modelled the lung as resistance and compliance. Rohrer [v] introduced a relevant simplification by considering only ane force per unit area across the lung and one across the chest wall in 1915 (modelling confirmed past Grandead et al. [6] in 1970), improving our capability to depict and understanding the mechanics of the respiratory system during both physiological and artificial breaths. Rahn et al. [7] introduced pressure level–book diagrams of the lung and thorax, and the concept of relaxation curves, in 1946, creating the groundwork for the evolution of respiratory energetics. These and other studies constituted the physiological background that lead to the introduction of positive-pressure ventilation into clinical do.
The main objective of mechanical ventilation was originally focused on restoring patients' ventilation. This concept expanded equally the other variables determining gas exchange were understood. Ashbaugh et al. [viii] first described astute respiratory distress syndrome (ARDS) in 1967 and identified the positive end-expiratory pressure (PEEP) equally "almost helpful in combating atelectasis and hypoxæmia". During the same menstruation, fauna studies showing oxygen toxicity when a high inspiratory oxygen fraction (F IO2 ) was used suggested increasing ventilation instead of F IO2 to care for hypoxaemia, leading to the (ab)apply of increased tidal volume (V T).
From the early 1970s, ventilators benefited from the progresses in electronics, and started incorporating more than avant-garde monitoring of menstruum and pressure variables (figure 1b). Improvements in monitoring also immune the possibility of using existent-time variables to control the action of the machine, with the intermittent mandatory ventilation fashion opening the development of assisted mechanical ventilation as a way to manage the weaning of patients from periods of volume-controlled ventilation [9].
In the meantime, our understanding of the mechanics of breathing further improved cheers to the introduction of the concept of dynamic compression in airways during forced expiration past Fry, Hyatt and co-workers [10]; three-dimensional analysis of pressure, menstruum and volume curves [10, xi]; and a meliorate understanding of chest wall mechanics from Campbell's diagram for the partitioning of elastic and resistive work [12], and the two-compartment model of the breast wall by Gonno and Mead [13].
In 1974, Webb and Tierney [fourteen] demonstrated in fauna models that ventilation with loftier distending pressures was leading per se to severe or even fatal pulmonary oedema, marking the beginning of a decades-long research effort aimed at understanding the adverse effects of mechanical ventilation and the mechanisms involved, and identifying appropriate strategies for preventing ventilation-induced lung injury (VILI), which despite the huge progress made so far, is still an open up upshot. Since so, major attention has been given to identifying the optimal ventilation strategy as a compromise between normalisation of blood gases and avoiding the development of VILI, resulting in the introduction of improved technologies for monitoring ventilation and lung conditions and the introduction of new avant-garde ventilation modes.
This progress was too possible cheers to the introduction of microprocessors in mechanical ventilators, starting in the early 1980s. Microprocessors are silicon electronic circuits able to execute programs; therefore, they can provide very complex functions that tin can be easily inverse by selecting different programs, allowing a mod mechanical ventilator to chop-chop modify its behaviour (due east.g. changing from a volume-control to a pressure level-command or a pressure level-aid ventilation fashion only by selecting unlike control algorithms executed past the device). In addition, microprocessors allow the implementation of sophisticated signal processing of the measurements provided by sensors, leading non only to better measurement accuracy and better rejection of noise and artefacts, merely besides to the generation of new data by appropriately computing and integrating several variables. Moreover, this expanded information can exist used past the ventilator itself to optimise the ventilation parameters on the basis of patient needs, creating the and so-called "airtight-loop" ventilation strategies or intelligent or smart ventilation modes (figure 1c).
The mechanical ventilator: the challenge of delivering accurate ventilation
Once a ventilation strategy is defined, the ventilator should deliver it to the patient in the nigh accurate way. To accomplish this, the machine must sense all variables that ascertain the animate design with high accuracy and adjust its action in real time. Modern ventilators achieve this by combining cut-edge engineering science of actuators, sensors and digital electronics together with sophisticated information processing algorithms. The basic components of a typical mechanical ventilator are shown in figure 2.
Basic structure and main functional components of a mechanical ventilator. ETT: endotracheal tube.
Pneumatic unit
The force per unit area source provides the energy required to overcome the elastic and resistive load imposed by the patient's respiratory organization, and information technology is used to reduce their work of breathing. The pressurised air is mixed with the appropriate amount of oxygen past the blender, and delivered to the patient by fast valves that modulate the amount of gas flowing to and from the patient. Thanks to the recent progress in electronics and electrical motors, instead of using valves, in some modern ventilators a fast-response, brushless-driven turbine acts as a variable pressurised air source, making the device independent of centralised medical compressed air distribution just still providing good performance [15].
Sensors
In modern mechanical ventilators, all relevant ventilation variables (pressure, menstruum and F IO2 ) are measured past appropriate sensors that provide information to the control unit of measurement in social club to adjust, in real time, the valves/turbines for delivering the desired ventilation way.
Modern pressure level transducers produce stable, precise and fast measurement of pressure but, despite this, pressure level variables provided past ventilators are at times still outside the tolerance range, even in absenteeism of leaks [sixteen]. Aimed at measuring an extensive variable, menstruum sensors (that besides provide book data by computing the time integral of menses) must exist placed in series with the patient in the breathing circuit and, therefore, are exposed to humidity, condensation and the patient'due south exhalation, making them potential sites of cross-contagion. Moreover, flow meters must guarantee a broad operating range of measurements, loftier accuracy and stability, adequate dynamic response, robustness to changes in gas composition, temperature and vapour condensation, and at the aforementioned fourth dimension, impose a low pneumatic resistance and dead space on the patient.
The demand to improve flow measurement technology is highlighted past the several comparative publications on performance of mechanical ventilators that quite often identify the delivered volume every bit the variable showing the everyman accuracy and repeatability [16–eighteen].
The nearly common approaches to measuring flow include variable-orifice sensors, pneumotachographs or hot-wire anemometers. Promising technologies for the hereafter are micromachined and fibreoptic-based menstruation meters [19]. In detail, thermal flow sensors micromachined on a unmarried silicon chip are already bachelor and used in some mechanical ventilators. They have the advantages of presenting high sensitivity and accurateness, a wide measurement range, fast response, low temperature and gas limerick sensitivity, low dead space and resistance [20], and the product cost could pb, in the future, to the availability of pre-calibrated disposable devices.
Ventilator circuit
Few studies have addressed the touch of ventilator circuits on overall performance in adult ventilation [21–23], while greater attention has been paid to this in paediatric and neonatal ventilation. The small V T of neonatal ventilation makes it crucial to consider the shunt compliance of the circuits when measuring volumes, either past excluding it by measuring menstruation later the circuit at the airways opening or past estimating and digitally compensating for it [24, 25]. Also, specific design of the ventilation circuits improves the transmission of ventilation waveforms to the patient with possible consequences on the overall treatment [26].
Digital signal processing algorithms
Compared to previous generations of mechanical ventilators, the apply of microprocessors allowed the evolution of sophisticated signal processing algorithms to compensate for intrinsic flaws in sensor technologies, consequently improving performance. As a drawback, these algorithms are mostly unknown to the users, who thus cannot empathize or predict the behaviour of the ventilator in situations and conditions different from these considered during the development of the algorithms.
Every bit book measurements are obtained past digital integration of the period signals, the signal processing also significantly contributes to volume accuracy by compensating for drift and leaks, specially during noninvasive ventilation, where leak interpretation and compensation algorithms play a major part. Recent studies have shown that, even if compensation algorithms tin can markedly reduce measurement errors, overall performance are not yet ideal [16, 27] and improvements are still needed, particularly during variable leaks [28].
Maintenance
Even if each mechanical ventilator undergoes accurate calibration and testing before being set in operation, preserving performance over time is the responsibleness of maintenance and servicing procedures, which have not ever shown to exist appropriate [29]. Even when the same model of mechanical ventilator is considered, wide variability in performance betwixt devices has been observed (figures 3 and four), both in the high-end machines used in the ICU [17] and in simpler units for habitation ventilation [30], highlighting the urgency of improving lifetime quality command of such devices, particularly for flow sensors.
Measured values of a) inspiratory volume, b) PEEP, c) F IO2 and d) respiratory rate delivered to a test lung by several devices in four intensive intendance units grouped past ventilator model. EVITA4: Draeger Evita 4 (25 machines); SERVOI: Siemens/Maquet Servo I (16 machines); SVC900C: Siemens SV900C (12 machines); SERVO300: Siemens/Maquet Servo 300 (seven machines); EVITAXL: Draeger Evita 40 (iii machines); SV900D: Siemens SV900D (one machine); EVITA2: Draeger Evita 2 (1 machine) and ENGSTROM: GE Engstrom (one machine). Reproduced and modified from [17] with permission from the publisher.
Human relationship between the number of unplanned hospital re-admissions of home-ventilated patients in the previous twelvemonth and the index of error of the dwelling ventilator. Reproduced and modified from [xxx] with permission from the publisher.
Ventilation modes
Fifty-fifty if the concept of mechanical ventilation is apparently simple, in that location are near infinite possible patterns of interaction between the ventilator and the patient, namely the modes of ventilation. Originally, the classification of these modes was relatively simple, with "controlled ventilation modes" beingness used when the ventilator was imposing its action to the patient and "assisted ventilation modes" used when the ventilator was supporting the animate of the patient. Yet, since the introduction of microprocessors, the number of modes of ventilation has grown exponentially. Moreover, in addition to the implicit complexity of this thing, many manufacturers accept introduced dissimilar (and often trademarked) names for similar modes, mostly for pursuing marketing and branding strategies, making information technology even more difficult for the user to understand the principles and ideal use of each ventilation mode available on a given ventilator.
This is a relevant event that probably deserves more attending in order to improve our ability to provide the best ventilation to our patients, whichever ventilator is used. Starting in 1992, Robert Chatburn developed a structured approach that lead to a well-defined and structured taxonomy to proper name ventilation modes depending on their essential features, making it possible to understand the features of a ventilation modality from its name [31].
Chatburn'due south arroyo (figure 5) is based on the definition of a style of mechanical ventilation as the combination of three elements:
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ane) the ventilator breath control variable
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2) the breath sequence
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three) the targeting scheme
They used 10 maxims to progressively depict how to classify a given ventilation mode by understanding what it does (table 1). Menstruum charts are likewise proposed to advise a procedure for classifying a given ventilation fashion by applying the maxims. Unfortunately, the introduction of such a structured taxonomy into clinical practice requires relevant and simultaneous efforts both in the educational programmes of medical schools and in the implementation of this naming approach in the ventilators past the manufacturers. This is unlikely to occur without a firm and combined endeavor from all medical societies of anaesthesia, intensive care and respiratory medicine, which is still lacking.
Construction of the ventilation mode taxonomy suggested by Chatburn. The proper name of a ventilation fashion results from three elements. CMV: continuous mandatory ventilation; IMV: intermittent mandatory ventilation; CSV: continuous spontaneous ventilation. Reproduced and modified from [31] with permission from the publisher.
Tabular array 1
Chatburns' maxims for understanding ventilator functioning
| A breath is i cycle of positive flow (inspiration) and negative flow (expiration) defined in terms of the flow versus time curve |
| A breath is assisted if the ventilator provides some or all of the work of breathing |
| A ventilator assists animate using either force per unit area command or volume control based on the equation of motion for the respiratory system |
| Breaths are classified co-ordinate to the criteria that trigger (kickoff) and cycle (stop) inspiration |
| Trigger and wheel events can be either patient initiated or ventilator initiated |
| Breaths are classified equally spontaneous or mandatory based on both the trigger and wheel events |
| Ventilators deliver three basic breath sequences: CMV, IMV and CSV |
| Ventilators evangelize v basic ventilatory patterns: VC-CMV, VC-IMV, PC-CMV, PC-IMV and PC-CSV |
| Within each ventilatory pattern, there are several types that can exist distinguished by their targeting schemes (set-point, dual, biovariable, servo, adaptive, optimal and intelligent) |
| A manner of ventilation is classified according to its control variable, breath sequence and targeting schemes |
CMV: continuous mandatory ventilation; IMV: intermittent mandatory ventilation; CSV: continuous spontaneous ventilation; VC: volume command; PC: pressure level control. Reproduced and modified from [31] with permission from the publisher.
Since in-depth clarification of different ventilation modes is outside the goal of this review, the reader tin find detailed information elsewhere [32]. As most of the contempo innovation in mechanical ventilation is focused in developing new approaches for the implementation of a targeting scheme, the post-obit paragraphs are focused on this topic, using Chatburn's nomenclature equally reference (table 2).
Table 2
Nomenclature of different targeting schemes
| Scheme | Description | Advantage | Disadvantage |
| 1) Set-bespeak (s) | The operator sets all parameters of the pressure waveform (pressure level control modes) or volume and flow waveforms (volume command modes) | Simplicity | Irresolute patient conditions may make settings inappropriate |
| 2) Dual (d) | The ventilator tin can automatically switch betwixt volume command and pressure control during a single inspiration | It tin adjust to irresolute patient conditions and ensure either a pre-set 5 T or tiptop inspiratory pressure, whichever is deemed most important | Information technology may be complicated to set correctly and may demand constant readjustment if not automatically controlled past the ventilator |
| three) Servo (r) | The output of the ventilator (pressure/volume/menses) automatically follows a varying input | Back up by the ventilator is proportional to inspiratory attempt | It requires estimates of artificial airway and/or respiratory organization mechanical properties |
| iv) Adaptive (a) | The ventilator automatically sets target(due south) between breaths in response to varying patient weather condition | Information technology can maintain stable 5 T commitment with force per unit area control for changing lung mechanics or patient inspiratory try | Automatic adjustment may be inappropriate if algorithm assumptions are violated or if they practice non match physiology |
| 5) Biovariable (b) | The ventilator automatically adjusts the inspiratory pressure level or V T randomly | Information technology simulates the variability observed during normal breathing and may improve oxygenation or mechanics | Manually set range of variability may be inappropriate to reach goals |
| 6) Optimal (o) | The ventilator automatically adjusts the targets of the ventilatory pattern to either minimise or maximise some overall performance characteristic (due east.g. piece of work rate of animate) | It tin can accommodate to changing lung mechanics or patient inspiratory effort | Automatic adjustment may be inappropriate if algorithm assumptions are violated or if they do non match physiology |
| seven) Intelligent (i) | This is a targeting scheme that uses artificial intelligence programmes such every bit fuzzy logic, dominion-based practiced systems and artificial neural networks | Information technology can adjust to changing lung mechanics or patient inspiratory effort | Automated adjustment may be inappropriate if algorithm assumptions are violated or if they do not friction match physiology |
Reproduced and modified from [31] with permission from the publisher.
Targeting schemes 1 and 2: set-betoken and dual schemes
The set-signal and dual schemes are both manual targeting schemes in which the operator directly adjusts the controlled output [33]. The bulk of ventilation modalities is included in this category. These modes tin exist divided into synchronised or non-synchronised ventilation modes.
In synchronised modes, the advances of technologies resulted in improved trigger performance [34–36]. While in adults, the employ of either the patient's flow or force per unit area triggering results in not-clinically pregnant difference in performance [37], in neonatal ventilation, where 5 T is extremely small and respiratory rates loftier, flow triggering improves synchrony [38]. Yet, the patient's triggering is still an open up issue, especially in presence of leaks and during noninvasive ventilation [39, forty].
Targeting scheme 3: servo targeting schemes
Servo targeting ventilation modes are characterised by the ventilator generating an output that follows a varying input in real time. Automatic tube compensation (ATC), neurally adjusted ventilator assist (NAVA), proportional assistance ventilation (PAV) and PAV-plus are examples of this category. They allow high degree of synchrony with patients and support the work of breathing, assigning to the patient'due south neural respiratory control the definition of the ventilation waveform.
ATC mathematically estimates the pressure drop due to the endotracheal tube (ETT) from the patient flow and continuously adjusts the pressure at the airway opening in order to maintain the pressure level at the tip of the ETT at the set value, compensating (only) for the resistive workload added to the patient past the high resistance of the tube. NAVA measures the electrical action of the diaphragm by transoesophageal electromyography and applies an airway opening pressure proportional to information technology to the patient. It allows improve synchronisation with the patient, the delivery of physiological waveforms and it is robust in case of leaks and machine-PEEP [41, 42]. PAV and PAV-plus back up both the elastic and resistive work of the patient, and are based on the awarding of the equation of motion of the respiratory system, therefore requiring the noesis of the patient'southward respiratory resistance (R dyn) and compliance (C dyn). PAV-plus continuously monitors lung mechanics in order to suit R dyn and C dyn to follow changes in patient's weather condition and resulting in ameliorate operation [33].
Targeting scheme 4: adaptive targeting
Adaptive targeting allows the ventilator to automatically set up one ventilation variable to maintain some other ventilation variable at a predefined value. In this fashion, the ventilator can cope with changes in patient conditions. Examples of ventilation modes using this scheme are: mandatory rate ventilation, in which the inspiratory pressure is modified to maintain the set respiratory frequency; adaptive menstruum/adaptive inspiratory time, in which the inspiratory time and inspiratory flow are automatically adjusted to maintain an inspiratory/expiratory ratio of 1/2; adaptive pressure level control, in which inspiratory pressure level is adapted to evangelize a target V T regardless of any alter in lung mechanics or patient effort; and mandatory minute ventilation, in which minute ventilation is maintained close to a target value past increasing/decreasing mandatory breaths or past an automatic adjustment of the inspiratory pressure. It is worthy to underline that these ventilation modes cannot be considered forms of automated weaning [33].
Targeting scheme five: biovariable targeting
Variable ventilation is a ventilation modality that provides a variable V T or peak pressure by adding random fluctuations to the set value in social club to mimic the natural variability of the respiration. The rationale is based on the racket-enhanced amplification of a useful signal in a nonlinear system by stochastic resonance. Indeed, including appropriately designed noise in mechanical ventilators improves gas exchange [43]. Moreover, variability in the respiratory system has been plant to be important for cell activity, as it positively modulates surfactant secretion [44, 45]; it is also related to the maturation of breathing control in infants [46, 47] and correlates with weaning success [48–50]. Creature studies have shown that variability improves ventilation efficiency and lung compliance in the preterm lung without increasing lung inflammation or lung injury [51, 52].
Adjusting mechanical ventilation for patients' condition and needs: optimal and intelligent targeting schemes
When, in 1998, Dreyfuss and Saumon demonstrated that the key ventilation variable responsible for the development of VILI is not the absolute pressure level applied to the lung only the V T, it became clear that, in gild to avoid VILI, the settings of ventilator parameters must exist accurately tailored to the individual patient. Still, fifty-fifty if the Five T was physiologically corrected by adjusting it for body weight, many patients were injured anyway. A significant advancement in our understanding of VILI was provided by the utilize of computed tomography (CT) to study ARDS. Information technology was thanks to CT scans that nosotros learned that ARDS is not a homogeneous, simply a highly heterogeneous, disease [53, 54]. 3-dimensional lung images showed that in a diseased lung, in that location are nonaerated regions due to either a complete filling of the alveolar spaces with liquid and cells, and/or to the collapse of potentially recruitable pulmonary units (atelectasis) [55]. This has major consequences in tailoring mechanical ventilation: a V T estimated from the torso size of a given patient might be excessive as the actual aerated lung can be much smaller than its anatomical dimensions (a concept chosen "baby lung"). The translation of this concept to clinical exercise was demonstrated by the ARDSNet trial in 2000, in which a significant reduction of mortality was shown when a V T of 6 mL⋅kg−i was used instead of 12 mL⋅kg−1 [56]. This and other studies underlined the importance of applying a lung-protective ventilation strategy in which the minimisation of side-effects of ventilation gains priority when applying this treatment. Notwithstanding, fifty-fifty if we now understand much more of the pathophysiology of ventilation, we are yet non able to fully implement these concepts in the clinical practice. For example, we learned that ventilation parameters should be adjusted on the amount of the aerated lung of the patient and not simply on his/her body weight. However, the size of the ventilated lung changes from patient to patient and in the same patient with fourth dimension. Moreover, as role of the nonaerated regions can be due to atelectasis, the application of different PEEP, ventilatory patterns and lung volume history can markedly bear on the dimension of the baby lung by recruiting or de-recruiting atelectatic regions, thus making the baby lung size a highly dynamic variable and not a static one [57, 58], thus requiring continuous adjustment of ventilator parameters. Therefore, PEEP should be optimised aiming to maximise the fraction of recruited lung [59, 60], thus reducing inspiratory pressures as much as possible to minimise over-distention of lung tissues [56]. Unfortunately, the only clinically bachelor validated applied science for assessing lung volume recruitment is CT, which is obviously unsuitable for long-term bedside monitoring.
This highlights the importance of both a proper understanding of the pathophysiological mechanisms and quantification of related relevant variables in optimising ventilation. Similar reasoning applies to other specific weather condition, such as the presence of intrinsic PEEP in obstructed patients and the controlled unloading of the respiratory muscles during weaning.
Even if these physiopathology concepts are now well understood, the fundamental variables needed to tailor ventilation are not yet measurable at the bedside, leading to a significant gap between what we know nigh an platonic ventilation strategy and what can be applied in the clinic. For this reason, inquiry efforts have been devoted in recent years to improving technologies for assessing physiological variables at the bedside and to comprise into mechanical ventilators these enhanced monitoring capabilities for providing the clinicians with the information needed to properly tailor mechanical ventilation. Moreover, availability of measurements of specific physiological variables enables the implementation of automatic tailoring algorithms.
Monitoring physiological weather condition
Pressure at the airway opening, ventilation volumes and F IO2 are monitored past all modern mechanical ventilators. Beside oxygenation, which tin can be easily measured at bedside past pulse oximetry, distribution of lung ventilation and mechanics are among the most relevant variables for optimising ventilation. Therefore, recently developed noninvasive techniques for lung function monitoring are now becoming available in mechanical ventilators, allowing bedside monitoring of new relevant variable for optimising ventilation.
Monitoring distribution of ventilation: electrical impedance tomography
Afterwards decades of research, electrical imperdance tomography (EIT) is now commercially available to monitor the pattern of regional ventilation of the lungs during mechanical ventilation, and to show how it changes by modifying ventilation fashion and parameters [61]. Recent studies showed that EIT can exist an constructive tool to titrate optimal PEEP [62–65].
Lung mechanics: forced oscillation technique
With the forced oscillation technique (FOT), a small-amplitude, high-frequency oscillatory pressure is applied to the airway opening, and the study of the relationship betwixt the applied pressure and the resulting flow at unlike frequencies (the so-chosen respiratory impedance) allows the characterisation of specific features of the lung mechanical properties, even in presence of spontaneous breathing and without requiring oesophageal manometry [66]. In particular, recent studies have demonstrated potential usefulness of FOT for monitoring lung mechanics during mechanical ventilation [67–70], proved that it is sensitive and specific to changes in peripheral lung mechanics [71–73], and showed that FOT can be used as bedside tool for monitoring recruitment/derecruitment [74]. Specifically, monitoring lung mechanics by FOT during a decremental PEEP trial allows the identification of the open-lung PEEP, defined as the minimum PEEP level required to prevent lung derecruitment afterward a recruitment manoeuvre, with high sensitivity and specificity when compared with CT [75, 76]. FOT may also be a useful tool for optimising PEEP in obstructed patients as it allows the monitoring of the presence of tidal expiratory flow limitation [71, 72] and, in general, can be used to monitor the status and evolution of respiratory mechanics in the ventilated patient.
These are only examples of how cutting-border technologies have resulted in new monitoring tools for optimising ventilation. Other physiological variables and measurement technologies have been studied for monitoring blood gases, lung mechanics, proinflammatory mediators, etc. [77].
Computational tools
The idea of automating mechanical ventilation was suggested in 1957 [78]. The rationale of "intelligent" ventilators is to better patient management past analysing and integrating data coming from large number of sources, and guaranteeing continuous adjustment of the ventilation fifty-fifty when skillful personnel are non constantly available, improving patients' handling and minimising clinical errors. Moreover, these tools can be also used for educational purposes.
However, ventilation direction is a circuitous procedure involving the analysis of multiple parameters, subjective strategies and multiple goals (gas commutation, lung protection and weaning). Physiology, every bit well as the peculiarity of the pathology, must be taken into account, several clinical data must be gathered from many sources and the condition of the patient must be monitored continuously. Finally, priorities of goals, clinical preferences and local clinical protocols must be besides considered.
Until now, different variables have been considered equally input for optimising ventilation strategies. They include variables describing the general status of the patients, haemodynamic variables, gas exchange, lung mechanics and the work of breathing [79]. When implementing these strategies in mechanical ventilators, at that place is the demand to identify a merchandise-off between the consummate description of the patient condition and the complexity of managing many variables, availability of the data, invasiveness of measurements and conquering of data from different equipment.
The aforementioned can be said for the output variables: the simplest system controls only ane variable. This is the case for algorithms that accommodate F IO2 to maintain target saturation. Ideally, on the basis of the condition of the patient, algorithms should exist able to control not only a single variable simply all the ventilator settings and even suggest specific manoeuvres or pharmacological treatments to the clinician.
Technology has developed several computational tools that commencement from a set up of input (measured) variables and tin can determine the desired outputs (settings) [79]. They include mathematical models and classical controllers (east.g. proportional, integrative and derivative controllers), rule-based practiced systems (due east.g. systems that use coded clinical protocols), and other bogus intelligence techniques (fuzzy logic, neural networks and genetic algorithms) or hybrid systems (combinations of the same systems). All these engineering technologies tin can be used to model the cardiorespiratory system and/or the decisional process of the clinicians to automatically identify an optimal ventilation strategy.
Mathematical models have the advantage of describing pathophysiological processes. However, in club to be patient-specific, the identification of the parameters of the model is required. This is often difficult every bit these parameters are commonly non measurable with noninvasive techniques and estimation errors, beingness unavoidable, must be considered. Classical command science controllers are normally used to modify one or few ventilator parameters to maintain i or few physiological variables within a predefined range.
Rule-based expert systems are built using the available ventilation protocols and clinician expertise. They have the disadvantages of existence related to protocols that may be subjective and specific for single centres.
Artificial intelligence techniques practice not crave modelling the arrangement on a mechanistic basis. They are able to handle large number of variables and they can be trained on large existing data sets. As a limitation, their outputs are hard to predict every bit they are determined past the complex interaction of many rules.
All these tools have been and are currently applied to implement both open up-loop (decision support) systems and closed-loop systems. Closed-loop systems have the major advantage of allowing a continuous adaptation of ventilation to the patient without requiring the intervention of clinicians. However, as they employ changes to a patient's therapy without involving, or at least requiring, the acquittance of a clinician, they open up serious issues of safe and accountability [79] that will require, in some countries, compliance with electric current legislation.
Targeting scheme half-dozen: optimal targeting schemes
These schemes let automatic adjustment of ventilator settings to either minimise or maximise some overall operation characteristic. Different optimal targeting schemes take been proposed in the literature [79]. They include:
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schemes involving a simple controller used for the automatic adjustment of F IOii to maintain patient saturation inside an optimal range, or to adjust inspiratory pressure or minute ventilation on the footing of carbon dioxide exchange
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more complex procedures including mathematical models and explicit objective functions for optimising more than than 1 variable (due east.g. optimising respiratory rate, 5 T, inspiratory/expiratory fourth dimension and PEEP for minimising the elastance of the respiratory system and regulating end-tidal carbon dioxide)
A ventilation modality available in commercial mechanical ventilators that follows this scheme is adaptive support ventilation (ASV). ASV is designed to minimise the piece of work of breathing. Its inputs are trunk weight, F IOii , PEEP and %MV to exist supported by the machine, and the target is to automatically adjust respiratory rate and V T, considering resistance, compliance and auto-PEEP. Some skilful rules are used to maintain frequency and V T in safe ranges and reduce the risk of motorcar-PEEP. Because of these rules, ASV can be better classified equally a grade of hybrid scheme that combines mathematical modelling and artificial intelligence. Studies accept shown that ASV tin can select specific Five T and respiratory rate settings [80] to attain the same arterial partial force per unit area of carbon dioxide equally the clinicians do and allowing weaning automation [81].
The new modality Intellivent-ASV adds to the ASV controller (ventilation controller) an oxygenation controller that adjusts PEEP and F IOii , depending on the ARDS network tables to maintain oxygen saturation measured by pulse oximetry (S pO2 ) within a pre-prepare range. Moreover, it includes a test for probing weaning readiness of patients [82].
Targeting scheme 7: intelligent targeting schemes
Several systems were described in literature for setting ventilation parameters based on some form of artificial intelligence. Examples of this targeting scheme are a fuzzy-logic arrangement used for F IOtwo control, or for optimising respiratory rate and V T on the basis of lung mechanics, S pOtwo , carbon dioxide, body temperature, end-tidal carbon dioxide, and the patient's body weight and height or to implement the "open-lung approach"; and bogus neural networks, genetic algorithms, fuzzy-logic systems and their combination for the support of ventilation management specific to some common lung pathologies [79].
Studies applying rule-based expert systems include those by Fiftyaubscher et al. [83], which was included in the development of ASV, and Dojat et al. [84], which was integrated in a commercial ventilator under the proper noun of Smartcare. Smartcare is a rule-based expert arrangement for automatic control of pressure support ventilation. It processes respiratory rate, end-tidal carbon dioxide, pressure support and V T. Information technology aims to maintain the patient in a "comfort zone" defined by a combination of V T, respiratory frequency and end-tidal carbon dioxide, and to progressively subtract the inspiratory pressure. Studies assessing the impact of using this ventilation mode found that information technology has ameliorate or similar operation to weaning managed by experienced personnel [85–89]. However, it is not well suited for patients with depression spontaneous breathing activity or during worsening of airway obstacle [82].
Educational perspective
This review has mainly focused on describing how the combination of physiology, medicine and engineering concepts and tools are being used to improve mechanical ventilation. To become further, 2 kinds of advancements are required. The first is the development of cost-effective, accurate, miniaturised and reliable sensors to measure patient variables (e.g. pressure, flow and oxygenation) and actuators to build machines reliably providing the desired pressure/menstruum to patients (e.g. blowers and valves). The 2d is the application of mathematical models, control systems and artificial intelligence to tailor the action of the mechanical ventilator to each patient's needs. While progress in sensors and actuators is more often than not a technological issue (relatively contained of respiratory pathophysiology), application of control/intelligence tools to the specific case of mechanical ventilation is something that goes across the pure discipline of engineering, since it requires a deep knowledge of the pathophysiology of the respiratory organisation. This is a challenging job that must be carried out past a truly interdisciplinary approach, joining the knowledge and expertise of bioengineers and physicians. Although this kind of arroyo has already been followed "informally" since the beginning of mechanical ventilation, interdisciplinary efforts are more and more necessary equally the complexity of modern mechanical ventilators increases equally these machines are among the about complex medical devices in terms of integrating and controlling a diversity of input and output variables.
Information technology goes without proverb that bioengineers focused on designing and building mechanical ventilators must be deeply educated on respiratory pathophysiology. However, it is nowadays not then obvious that physicians dealing with mechanical ventilation take been sufficiently educated on the technological aspects of these complex medical devices. Physicians attending patients nether acute or chronic mechanical ventilation are faced with considerable difficulties to properly understand how these devices piece of work. As mentioned before in this review, a major difficulty is the intrinsic complication of modern control/intelligence algorithms implemented in current devices. This difficulty is increased by the lack of standardisation and the fact that, for marketing and commercial strategies, companies building ventilators tend to add new definitions of ventilation modes and variants without disclosing the algorithms with sufficient item for the user to actually empathise how the car is working. The result is that in most cases the doc is using a sort of "black box" as therapeutic device. Appropriately, it seems necessary that, when being specifically trained to utilize mechanical ventilation, physicians should exist as well provided with a sufficient didactics on the technological issues involved in these mod medical devices. In that way, they volition be in optimal professional person conditions to select the best therapeutic choice for each individual patient.
Conclusions
Patients' needs are irresolute with time considering of the evolution of both pathology severity and treatments. Therefore, an optimal ventilation strategy is an adaptive process aiming to care for the acute condition and either back up the gradual weaning from the ventilator or adjust to the natural fluctuations of chronic disorders. Such a tailoring could improve outcomes and comfort, reduce agin events, and avoid prolonging ventilation, with its associated risks and costs. To achieve these goals, advancement of our understanding in pathophysiology, medicine and applied science must be combined with an interdisciplinary approach to fully address the complication of mechanical ventilation for further improving patients' handling.
Disclosures
Footnotes
References
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