Positive end expiratory pressure in patients with acute respiratory distress syndrome – The past, present and future

Biomedical Signal Processing and Control 7 (2012) 93–103

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Biomedical Signal Processing and Control journal homepage: www.elsevier.com/locate/bspc

Review

Positive end expiratory pressure in patients with acute respiratory distress syndrome – The past, present and future Ashwath Sundaresan ∗ , J. Geoffrey Chase Department of Mechanical Engineering, College of Engineering, University of Canterbury, Private Bag 8140, Christchurch, New Zealand

a r t i c l e

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Article history: Received 16 March 2010 Received in revised form 13 December 2010 Accepted 1 March 2011 Available online 3 April 2011 Keywords: Mechanical ventilation PEEP Model based methods Imaging methods

a b s t r a c t Though well studied, acute respiratory distress syndrome (ARDS) is still not fully understood. Mechanical ventilation (MV) has been a key treatment for ARDS. However, the optimal ventilation settings of basic MV parameters are still significantly debated. Only recently, were low tidal volumes shown to have lower mortality rates among ARDS patients. Despite over two decades of study, no standardisation of therapy or approach to MV appears on the horizon. This problem is likely due to the heterogeneity of the ARDS patient and ARDS affected lung. Currently, external MV parameters are set to try and treat an internal condition. There is no way to determine if more harm than good is being done. Hence, there is a tradeoff in between the risk of and benefit. What is required a method to assess that tradeoff and thus the potential risk. The use of positive end expiratory pressure (PEEP) and tidal volume has been identified as key ventilation parameters when treating ARDS patients. Although the impact of both parameters has been studied extensively, only the use of low tidal volumes has been conclusively determined. In contrast, the application of PEEP is still widely disputed. This review discusses two different approaches to ventilation management and the impact on optimal PEEP. The first approach examines the use of imaging techniques to determine regional lung mechanics. In the past, computed tomography (CT) was seen as a way to optimise PEEP, but the risks associated with it have limited it to a research tool. Newer methods such as lung ultrasound and electrical impedance tomography (EIT) seem to offer a less riskier approach to assessing regional mechanics. The second approach examines model-based approaches to ventilation management. Models that take ventilation data and depict a physical picture offer the potential to assess the risks on a patient-specific basis. Models offer the benefit of creating an approach to a highly heterogeneously and patient-specific problem in a non-invasive manner. Given the added dynamic of a patient’s evolution over time, a highly patient-specific approach is typical and also what is required. Although both approaches can potentially be used to help with clinical decision making with regard to PEEP, they both pose advantages and disadvantages. The use of a given approach will depend on the individual needs of each clinic. Although not currently deployed in the clinic, model-based methods represent a novel methodology in treating ARDS patients. Thus, model-based approaches represent a “state of possible” rather than currently practiced methods, and require further clinical validation before justifying their use in the clinic. © 2011 Elsevier Ltd. All rights reserved.

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MV metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Tidal volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Peep and recruitment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Static PV curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

∗ Corresponding author. Tel.: +64 03 364 2987x7486. E-mail addresses: [email protected], [email protected], [email protected] (A. Sundaresan), [email protected], [email protected] (J.G. Chase). 1746-8094/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.bspc.2011.03.001

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3.

Imaging methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 3.1. The past and present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 3.1.1. CT Scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 3.2. The future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 3.2.1. EIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 3.2.2. Vibration response imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4. Model-based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 The past and present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.1. 4.1.1. Finite element models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.2. The future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.2.1. Lumped parameter gas exchange models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 4.2.2. Lumped parameter recruitment models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.2.3. Stress strain models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

1. Introduction The formal definition of acute respiratory distress syndrome (ARDS) by Ashbaugh et al. [1] in 1967 identified positive pressure mechanical ventilation as the key component of care for patients with acute respiratory failure. The study recognised that ARDS was a consequence of a wide variety of illness’, which resulted in common symptoms among patients; a syndrome rather than a specific infection or virus. The study helped to increase the recognition of ARDS in the hospital and help with improving patient management systems [2]. Although this study and the changes it evoked did not directly impact mortality rates, it concluded that positive pressure ventilation was an essential component of patient care in ARDS and paved the way for future studies on ventilation strategies. Prior to the use of the intensive care unit (ICU), patients with ARDS typically did not survive long enough for detailed investigations. ARDS incidence rates have been reported to range from 3 to 74 cases per 100,000 of total population per year from various parts of the world [3–6]. Mortality for those hospitalised in the ICU with ARDS, ranges from 22 to 66% from moderate to severe ARDS [3–10], however mortality rates have plateaued since 1994 with no further improvements in therapy [11]. With the advance of technology and increasingly better equipped ICU’s, as well as the increased availability of positive pressure ventilators and trained staff, patients now survive much longer and in greater numbers [12,13]. The fundamental goal of mechanical ventilation (MV) is to aid patient’s respiratory effort and thus reduce the work of breathing to relieve respiratory distress and allow recovery. In particular, as ARDS attacks the ability to exchange gases and oxygenate blood, it is critical that as many lung units as possible be recruited without causing further damage to healthy and unaffected lung units. Thus, MV provides a means of improving patient care and the likelihood of survival for patients suffering from ARDS. The use of mechanical ventilation in treating ARDS has a significant associated cost. Dasta et al. reported that the mean cost of mechanical ventilation in the United States was a total of $1522 per patient per day [14]. However, proper ventilation management teams have been shown to be effective in reducing costs of mechanical ventilation down to $1303 per ventilated patient [15], with regular monitoring of patients. However, the daily costs remain high. There is still no set protocol on how to optimise and manage the individual parameters used in ventilation for a given patient. In addition, length of MV is still strongly associated with mortality in ICU patients [16–19], so that optimising its application would reduce both cost and mortality. Over the last few decades, a significant amount of research has been invested into determining critical ventilator settings for treating ARDS. Currently, the main parameters that have been identified

to lower mortality rates in ARDS patients are low tidal volumes (Vt ) [20–23], the application of positive end expiratory pressure (PEEP) [22,24–28], and patients being ventilated in prone position [29–36], where the last intervention improves oxygenation but is not a direct factor in providing MV. However, due to the heterogeneity of patients with ARDS and the dynamic evolution of their condition over time, a fixed approach or setting that is valid over all or large numbers of patients is not likely possible. One size will not fit all patients and a patient-specific method must be sought. More specifically, the application of MV is a delicate balance between risk and reward. The ultimate objective of MV is to maximise lung recruitment to prevent alveoli collapse. In contrast, excessive pressures or tidal volume to increase recruitment can cause additional lung damage and may result in ventilator induced lung injury (VILI) [37–41]. VILI itself can increase the risk of death [40,42] and is difficult to diagnose because it overlaps or is secondary to the actual disease (ARDS) being treated [43]. In particular, VILI, secondary to MV is a direct mechanical injury to the lung at the alveolar level that results in an inflammatory response that exacerbates the systemic inflammation typical of ARDS patients [10,37–46] and ICU patients in general [47–49]. Thus, the main problem is optimising tradeoffs between MV parameters to achieve and evolve optimal patient-specific solutions. This article discusses the relevant research and tradeoffs among these variables (PEEP and tidal volume). In this context, the ARDS management problem is formulated and presented as a patient-specific optimisation problem. The significant, largely clinical research to date is used to define and delineate these tradeoffs and provide the overall context of the problem. Two major approaches are discussed in this review. First, the potential for new imaging techniques as a bedside diagnostic are discussed with regard to ventilation management. More specifically, the use of imaging techniques, such as electrical impedance tomography (EIT) and lung ultrasound, to evaluate regional lung mechanics is explored, and the impact on patientspecific ventilation therapy is discussed. Second, the potential of model-based methods to ascertain a patient-specific recruitment and stress–strain state is presented as a means of defining a real time, adoptive optimisation problem that can be used to monitor and provide patient-specific ventilation therapy.

2. MV metrics The search for optimal parameters in treating patients with ARDS has led to various studies to evaluate the effects of different metrics for setting MV. Although there are many parameters that

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Nomenclature ARDS ICU MV Vt PEEP VILI EIT FRC SPV UIP LIP CT VRI FE TOP TCP dFRC

acute respiratory distress syndrome intensive care unit mechanical ventilation tidal volume positive end expiratory pressure ventilator induced lung injury electrical impedance tomography functional residual capacity static pressure volume upper inflection point lower inflection point computed tomography vibration response imaging finite element threshold opening pressure threshold closing pressure dynamic FRC

can be optimised, studies have indicated tidal volume and PEEP are shown to affect incidence and mortality, highlighting the salient importance of these parameters [22,23,28,50–53]. The dual importance of these two parameters is due to the idea that both lung volume and pressure can be used as a proxy to indicate the level of stress and strain in the lung and thus a proxy for lung damage and potential VILI. The following section summarises both tidal volume and PEEP, and examines previous studies undertaken to summarise the fundamental state of the art thinking around this problem. 2.1. Tidal volume Tidal volume (Vt ) is an important parameter in MV treatment. It represents the amount of air being delivered to the body by the ventilator during the inspiration phase of breathing. It is thus the net volume of breathing above the volume at PEEP. The application of tidal volume also implies that there is a level of strain occurring or being induced within the lung. Lung strain over the entire organ can defined as the ratio of tidal volume to the functional residual capacity (FRC) or Vt /FRC [54]. Thus, for patients with severe ARDS, who have lower FRC due to alveolar collapse, the lung stress and strain may be higher compared to healthier patients with greater recruitment or more healthy alveoli units. Therefore, having a larger tidal volume, which results in a higher strain, may result in added risk of VILI for the ARDS patient. This point is particularly valid for patients with significant ARDS affected areas heterogeneously distributed through the lung and surrounded by healthy tissues. Thus, the use of low tidal volume has been investigated as an optimisation parameter to provide safe, so called lung protective, MV strategies. Early interest in low tidal volume ventilation was spurred by Hickling and colleagues in an observational study [55]. Animal studies also showed that large tidal volumes resulted in the development of acute lung injury characteristics [42,56]. The results of animal trials provided researchers with the justification that low tidal volumes would be beneficial to patients, and in the late 1990s four controlled trials were conducted to evaluate the effect of low versus high tidal volumes [23,51–53]. Out of the four studies, only Amato et al. [23] showed a significant decrease in outcome mortality using lower tidal volumes. The remaining three studies by [51–53] showed minimal differences in mortality between patients on low and high tidal volumes. A later randomised trial [45] showed that low Vt reduced the inflammatory response, which is associated with mortality in ARDS and VILI.

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To conclusively determine the effect of low tidal volume ventilation, a study was conducted by the ARDS Network on 861 patients in 2000 [50]. The study showed that patients with lower tidal volumes (6.2 ± 0.8 ml/kg) had a higher number of days free of mechanical ventilation and lower mortality rates than those with higher tidal volumes (11.8 ± 0.8 ml/kg). The result of this study has significantly improved ARDS therapy and provided a means of advancing patient management by choosing the appropriate tidal volume. However, with all the studies regarding the appropriate tidal volume, the tidal volume was fixed for a given patient during the course of the MV therapy. This methodology does not consider the lung strain, as strain is a function of patient weight or time as the state of ARDS evolves [6,57–60], nor does it consider evolution of therapy as condition evolves. Finally, it does not consider or account for variability in the individual patient response to therapy. Thus, to properly optimise the lung strain, an ideal tidal volume must constantly be re-evaluated based on patient-specific condition and response with a goal of minimising lung strain induced by Vt . 2.2. Peep and recruitment Another critical ventilation setting is the use of PEEP. PEEP represents the pressure that the lung experiences during the end of the expiration phase and is also set generally by the clinician. The objective of applying PEEP is to increase the level of oxygenation by increasing the number of recruited alveoli retained at end of expiration by lifting that pressure so they remain open. It must be noted that as PEEP rises, Vt falls, as total maximum or peak inspiratory pressures is restricted to avoid injury. The application of PEEP throughout the respiratory cycle has been shown to greatly improve oxygenation in patients with ARDS, as shown in early animal and human models [55,61–63]. As a result, the results of these early studies have led to the widespread use of PEEP in MV therapy [23,26,27,30,50,64–66]. However, the level of appropriate PEEP over a given cohort has never been properly established, despite a very large amount of research in the area (e.g. [23,50,67–70]). PEEP comes with tradeoffs and risks to a heterogeneous injured lung. Too much PEEP risks maintaining ARDS affect alveoli, but also injuring healthy lung units [46], while low levels of PEEP could cause repetitive opening and closing of alveoli [53]. Thus, the use of inappropriate PEEP can also cause VILI in critically ill patients. Several trials have been conducted to evaluate the benefit of high PEEP in the treatment of ARDS. Higher PEEP would, it was hypothesised, maintain recruitment of these units ARDS affected units preventing repetitive collapse. Amato et al. showed that patients with high PEEP had significantly lower mortality [23]. Villar et al. showed similar results for high PEEP [22]. Others showed that improved PEEP results in increased systemic inflammatory response [46,71–73]. However, the problem with these studies was the low mortality rate could either be attributed to high PEEP or low tidal volume because of the tradeoffs between these two variables in setting MV [74]. No study has controlled both variables to date, nor accounted for their potential to vary patient-specifically, both in general and over time. A study by the ARDS Network was conducted to determine the effect on mortality solely attributed to PEEP [28], by using only low tidal volumes. The study used low tidal volumes (6 ml/kg) and concluded that although high PEEP increases oxygenation, there was no significant difference in mortality rates or length of MV. The result of these and various other studies [22,23], thus provide no conclusive evidence on a set level of PEEP to be used to optimise recruitment across heterogeneous ICU cohorts. Although high PEEP can improve oxygenation, the risk of alveoli over-inflation is also increased at high PEEP for healthy alveoli in particular [39,44,75]. Thus, using the highest PEEP possible is not

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the best solution for optimum ventilation therapy as this may cause more harm than good and contribute to VILI. However, if the PEEP level is too low, then injury is induced by the repetitive opening and closing of alveoli units [41,76]. The predicament therefore lies in choosing an optimal value of PEEP by maximising recruitment and minimising additional damage to the lung and understanding that it might change or evolve over the time course of the disease. The ARDS lung has been shown to be significantly heterogeneous [1,2,77]. Thus, the distribution of damaged alveoli is not equal in all parts of the lung. The major problem in mechanical ventilation lies in the lack of standard clinical protocols for treating patients with a heterogeneous and dynamic condition. Currently, the clinical methodology is to use a low tidal volume and then to choose an appropriate PEEP based on the patient’s condition as assessed by the clinician [67]. However, an optimal solution would be to choose initial parameters that would then vary over clinically relevant timeframes to continually optimise the therapy for each patient, thus accounting for heterogeneity of the disease and variability in response by patients. Choosing an appropriate level of PEEP is dependent on the clinical objective of ventilation therapy. Depending on the patient’s condition, the objective is a combination of either maximising recruitment and gas exchange, and minimising lung stress. If the goal was to only maximise gas exchange at a given moment, then a very high PEEP would be appropriate. However, if lung stress and over-inflation are to be minimised, then a lower PEEP is most appropriate. Thus, the problem is defined as achieving a balance between these criteria in the PEEP selected and then optimising this trade-off as patient-specific disease state evolves and changes. 2.3. Static PV curve The use of PEEP to maximise alveolar recruitment has been thoroughly studied [24,69,75,78,79]. These studies highlighted the effect on recruitment as a function of PEEP. A major conclusion is that the amount of potentially recruitable lung is strongly linked with the patient-specific volume recruitment response to PEEP [24,77]. Because of this strong correlation, clinicians choose PEEP based on how much potentially recruitable lung is available. However, a problem arises when determining what level of PEEP produces the most amount of recruitment. The static pressure volume (SPV) curve provides a bedside method of obtaining an appropriate PEEP level, as these curves can be obtained directly from some ventilators [80,81] or from sensors on the mask, ET tube or tracheal end of the ET tube. In particular, SPV curves measure the steady state lung volume above the functional residual capacity (FRC) for a given PEEP and pressure in patients with ARDS on MV [80–83]. However, SPV curves provide a diagnostic tool that has been extensively used in the treatment and management of patients with ARDS [80,82,84–87], and directly avoids the issues associated with using computed tomography (CT) scans. The SPV curve is commonly measured by three different methods [80,81,88]: • The super syringe technique. • The constant flow method. • The multiple occlusion method. A SPV curve resembles a sigmoidal shape and is distinctly unique for each patient. The SPV is generally characterised by an upper and lower inflection point (UIP, LIP) and it is clear that along the SPV curve, compliance (slope of the curve) varies significantly. Fig. 1 shows these points in an example SPV curve. The physiological explanations of the UIP and LIP have been relatively thoroughly

Fig. 1. Static pressure volume curve showing LIP and UIP.

studied [81,89–94], which has led to better understanding of alveoli recruitment behaviour as a function of PEEP. Gattinoni and Pesenti, through the use of CT scans, found that compliance in the linear portion, between the LIP and UIP, correlated only to normally aerated lung [77]. Thus, if only normally aerated lung was related to compliance, then the SPV curve actually represents the recruitment behaviour of the lung. Other studies using mathematical modelling [55] and in vivo microscopy [76,95] agreed with this conclusion. In addition, Jonson et al. [90] showed that increased recruitment does occur above LIP during inflation, fully showing that recruitment is continuously occurring throughout the SPV curve, rather than being concentrated at certain points. This last result is significant because traditionally, the linear portion of the curve was thought to be a result of isotropic balloon like expansion. Thus, conceptually setting PEEP near the LIP was deemed appropriate as it would capture all supposed recruitment and minimised any balloon like over inflation [55,91,96]. However, Jonson et al., and later others, showed that alveoli continue to be recruited above the LIP. While the LIP was said to be the point at which the majority of alveoli start to recruit, the UIP was said to represent the point at which alveoli start to significantly over-inflate [55,97]. Thus, a PEEP below the LIP was seen to indicate a setting at which alveoli would start to de-recruit. In contrast, a PEEP above the UIP indicated a setting at which alveoli would definitely be over-stretched and result in increased VILI. An implication of this set of conclusions was that researchers then started applying PEEP such that it was above the LIP and below the UIP so that it remained in the linear portion of the SPV [23,94,96]. Hence, based on the arguments above, these settings put these clinicians in a midpoint gaining some added recruitment. However, continued investigation into the use of these inflection points to determine or optimise PEEP yielded two distinct problems. First, PEEP is applied primarily to prevent de-recruitment, rather than or as much as to allow recruitment. Thus, using the LIP to determine a minimum PEEP is not going to yield the most optimal solution [68,98]. Second, although PEEP should be set between the LIP and UIP, the linear portion of the curve offers a very large range of PEEP values, such that repeated alveoli collapse and excessive added strain to healthy alveoli can be avoided [23,76,99]. Thus, although inflection points provide a guide for the clinician to select PEEP from a given range, they do not offer an exact single PEEP value. In addition, the static PV curve does not quantify

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alveolar recruitment. The current understanding of the ARDS lung is that it is: • Heterogeneous in lung units and types (normal or ARDS affected). • Heterogeneous in the affect of ARDS on any given alveoli or lung unit. • Heterogeneous in response to PEEP, with respect to the pressure (above PEEP) required opening or recruiting the alveoli. The SPV curve does not reflect the heterogeneous distribution of the ARDS lung and fails to quantify the recruitment of alveoli. In addition, and equally important, no other methods or approach have risen to replace the use of such SPV curve points. One final problem with the SPV curve is that the patient is temporarily disconnected from the ventilator [80,81]. In addition, currently, only certain ventilators have the functions required to measure the SPV curve without disconnection [100,101]. It also takes a fixed amount of time (10–15 min) if done via a super syringe, which may not be available for clinical staff and require specialised training to implement. Because not all hospitals will necessarily have these advanced ventilators, and the super syringe approach can be problematic, the SPV curve is not necessarily the most ideal method for regular monitoring of patient status and resulting PEEP selection. Finally, to accurately capture PV curves, patients may need to be sedated so they do not fight the procedure. The use of added sedation may not be clinically justifiable and provides another limitation in obtaining the static PV curve. Thus, the traditional method of PEEP selection using the static PV curve still poses problems that need to be overcome. To achieve a rapid patient-specific solution, new methods need to be developed that incorporate various the MV metrics. Patient-specific solutions can be broadly evaluated using two different methods – (1) patientspecific imaging techniques and (2) patient-specific models. The following review examines past methods of imaging and modelbased approaches to assist in ventilation management. Limitations of past methods are explored, and the future of both approaches is discussed. 3. Imaging methods Imaging techniques create an image of the lungs so clinicians can physically see the state of the lung. Once an image of the lung is obtained, the clinician can treat the disease state based on the observation. 3.1. The past and present 3.1.1. CT Scanning The use of CT scans has provided researchers with a gold standard imaging technique on measuring PEEP induced lung recruitment with ARDS [24,25,102]. In these studies, a full lung spiral CT is obtained at both a fixed PEEP and at zero end-expiratory pressure. Comparison and image processing thus directly defined the volume of recruited lung. The CT scan method utilises the attenuation of individual pixels to determine if the alveoli are recruited, de-recruited or overinflated. This status is evaluated by assigning each pixel within the CT a Hounsfield unit (HU) attenuation value. The HU measures the radiodensity of the concerned medium and describes the level of Xray radiation that passes through completely (i.e. air HU = −1000) or is completely absorbed (i.e. bone HU = 400). To measure the level of recruitment, a recruited lung unit is assigned a range between −900 and −500 HU, hyperinflated lung between −1000 and −900 HU, poorly aerated lung between −500 and −100 HU and non recruited lung between −100 and 100 HU [103]. Thus, a visual interpreta-

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tion of the recruitment behaviour is presented for a given PEEP level. Because the CT method distinguishes between overinflated and recruited units, the method also provides a tool to predict a more optimum PEEP to mitigate VILI or lung damage at any given time. By choosing the PEEP where the level of overinflated, and thus potentially damaged, lung units is minimal compared to recruitment, clinicians can choose an appropriate setting for individual patients that recruits lung and minimises overinflation damage thus maximising the efficacy of the therapy. Thus, if clinically practicable, one could define, at a given time in the patient’s stay and evolution, a curve between recruitment or overinflation and damage versus PEEP, creating a tradeoff definition to be optimised as clinically desired. However, CT methods have obvious clinical limitations that reduce the viability of this method in regular, everyday clinical use. First, CT scanning requires the patient to be transported from the ICU to a radiology unit, which has several attendant risks. In particular, many MV patients are less effectively ventilated in the CT scanner due to the extension tubing required to reach the mask, thus potentially skewing results. Second, the CT scanner provides additional radiation doses that might otherwise be avoided in normal care, which is generally avoided where possible [104,105]. Finally, CT scans are time consuming, costly and heavily dependent on other clinical resources or demand, thus reducing access on the regular basis that might be accepted. Hence, although CT scans provide exact and valuable information, it is not clinically practical or viable, and is thus limited to use as a research tool. In particular, it is not a bedside tool by any means. This last point clearly illustrates the need to have a simpler approach to minimise clinical burden and cost that can be located with the ventilator at the bedside. 3.2. The future The problems associated with CT scanning have motivated researchers to develop new imagining techniques that can be used at the bedside without any additional radiation doses. Although the following methods are not commonplace across all ICU’s in the world, their use in determining lung heterogeneity has been shown in concept. Thus, the potential to help determine optimal PEEP can also be shown. 3.2.1. EIT One of the most promising new methods of lung imaging is the use of electrical impedance tomography (EIT). EIT uses a series of adhesive electrodes attached to the patient in the thoracic region and constructs an image of the lung by evaluating impedance changes. Because, different tissues exhibit different conductivities, an image can be constructed based on the resulting measured impedance. An electric current is applied to two or more electrodes and the resulting voltage is measured by the remaining electrodes [106–110]. As the conductivity of the tissues changes, the resulting voltage output is varied. Using the output voltage, lung impedance can be calculated and displayed in an image. Under varying ventilation conditions, different images can be compared to evaluate changes in impedance and thus, changes in lung status. The use of EIT with regard to applications in ventilation is not new [111], but the use of EIT as a bedside diagnostic is a relatively new technique [106,107,109,112–115]. Early research using animal models and EIT showed that impedance changes correlated closely to the PV relationships in pigs [116]. In addition, EIT showed that impedance changes could also correlate very closely to lung volume changes when compared with CT scans [111]. These results lead to

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EIT being used to measure regional ventilation distribution within the lung [106,107]. Early studies examining PV relationships with EIT concluded that LIP and UIP could be accurately predicted using EIT measurements [114,116]. These studies compared UIP and LIP measurements from conventional static PV curves with EIT. The studies showed the limitation of global PV curves as they did not represent lung homogeneity. In contrast, LIP and UIP derived from EIT gave an indication of regional distribution and would be a better method to set UIP and LIP. Although using EIT to determine inflection points gives more information on how regional ventilation, the method still does not yield a patient-specific PEEP. Although to date, no studies have used EIT to optimise PEEP in ARDS patients, a study by Erlandsson et al. [117] optimised PEEP in morbidly obese patients following laparoscopic gastric surgery. A decremental PEEP trial was conducted and PEEP was optimised based on changes in lung volume. The concept of measuring regional ventilation using EIT can be extended to estimating the amount of hyper extended and collapsed lung in ARDS patients [107]. The study combined the effects of regional compliance with global airway pressure measurements to assess the state of alveoli. In particular, the authors hypothesised that an increase in compliance with a decrease in PEEP resulted in a decrease in hyper extension, while a decrease in compliance resulted in alveolar collapse. Thus, the use of a decremental PEEP trial can be used to assess the impact on regional compliance. Although EIT has a lower resolution than CT scans [107,109,111], it avoids the complicated problems associated with CT. In particular, EIT does not require patient transport and is a radiation free imaging method. The research into EIT over the past decade suggests that a bedside tool is not far off. Given that EIT can estimate regional ventilation distribution and potential recruitment, the method could be extended to allow PEEP optimisation in patients with significant lung damage. 3.2.2. Vibration response imaging Vibration response imaging (VRI) is another new imagining technique that measures vibration energy of lung sounds during respiration and MV therapy [118,119]. Surface sensors are attached to the lung and measure vibrations that propagate through lung tissue which are produced when air enters the lung. The resulting image shows the left and right lung, and areas with the greatest vibration energy shown in black (lung tissue) and the least energy in white (air) [112]. Ongoing studies have examined the effect of lung vibration on regional lung distribution [118,119] and shown strong correlation with CT scans. In addition, VRI has also been used to assess PEEP induced recruitment in patients with ARDS [120,121] as a means to visualise regional distribution. Although VRI offers a non-invasive, radiation free imaging method, current VRI recordings can only be done when patients are near sitting [112] and may prove to be limited for patients who need to be in supine position. In addition, the resolution is much lower than EIT and CT, and to date, VRI has only been used as a research tool. 4. Model-based methods 4.1. The past and present The overall goal of model-based approaches is to determine the appropriate PEEP level in patients without the complications or other issues associated with the SPV curve or CT scans. To minimise clinical burden and cost, an optimal solution would introduce no significant new hardware or systems nor require excessive cost, clinical time or effort. Thus, some researchers have

strived to develop mathematical models to help aid clinical decision making. Although the development of various model-based approaches to select PEEP is very recent [122–127], the underlying physiological principles are used in the clinic regularly. Many model-based methods build on current physiological principles such as the static PV curve or gas exchange principles. Although the fundamental theories are well understood, the application of model approaches is relatively new. 4.1.1. Finite element models Models of lung mechanics can be categorised into finite element models or lumped parameter models, and each of these has distinct advantages and limitations associated with it. Finite element (FE) models of pulmonary gas flow offer significant understanding of the underlying physiology and offer detailed resolution of complex systems [128–132]. FE models also offer the advantage of allowing a patient-specific solution as each model can represent a unique geometry for a given patient. However, the main barrier for FE as a clinical tool is the cost associated with computational power and length of time to run a simulation. A second major barrier is that a patient-specific geometry would require a CT scan with additional costs and risk. Finally, identifying recruitment status with a model of such high resolution would be problematic and computationally very expensive. Thus, although FE models are very well suited to helping understand the underlying physiology, they are limited to being a research tool and do not provide a suitable bedside method for modelling patient-specific lung mechanics. Although very simple models based on this approach would not necessarily be precluded, they have not been tried to date. In contrast, lumped parameter models offer a simple and relatively inexpensive method of assessing lung mechanics and capturing essential dynamics. Lumped parameter models do not necessarily require excessive computational power and can thus be utilised directly at the bedside in clinically realistic time frames. However, a significant limitation of these types of models is the lack of physiological detail compared, for example, with FE models. Lumped parameter models can be made to be more physiologically complex, but the direct clinical benefit of the complex model must be compared with the computation power and costs associated with complexity. In contrast, such lumped parameters models represent the same external “view” the clinician has, and are thus readily defined in terms of similarly readily available measurements. Lumped parameter models can be broadly classed into gas exchange models and recruitment models. Both types of models can be used to assist in ventilation support and are used to model the effects of PEEP. Gas exchange models are used to study the effect on gas exchange in the airways and alveoli. In contrast, recruitment models take a more mechanical view and are typically used to evaluate the effect on alveoli recruitment. Lumped parameter models are a fairly new tool and have only recently been used as a means to titrate PEEP. 4.2. The future 4.2.1. Lumped parameter gas exchange models Gas exchange models are used in conjunction with MV to determine how various parameters affect the oxygenation status of the blood. Ranging through different mathematical complexities, gas exchange models offer detailed descriptions of the diffusion process of oxygen using principles of mass balance. Ben-Tal reviewed them extensively noting up to three different types of gas models [133]. Though not directly related to recruitment, the models can respond to PEEP changes by evaluating the changes in gas exchange and can give an indication of recruitment status. The uses of clini-

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cal gas exchange models have been developed but provide different outputs and are used in patients with different clinical prognosis [122–124,127,134,135]. The ALPE model (preceded by the INVENT system) uses clinical measurements from the ICU to help determine parameters such as ventilation perfusion mismatch and pulmonary shunt [124,134,135]. Although the model uses clinical measurements from the ventilator such as PEEP and inspiratory expiratory ratios, it does not determine the effect of ventilation on recruitment. In addition, the models were tested on patients with COPD or cardiac injuries, and did not specifically target ARDS patients where recruitment is the primary mechanism. However, the ALPE system provides a quick and easy estimate of gas exchange impairments which could be used to optimise ventilation parameters. Another method used to evaluate gas exchange parameters is the SOPAVENT model [122,123,127]. The SOPAVENT model uses the concept of fuzzy logic to evaluate blood gas parameters and the estimation of pulmonary shunt fractions. The original SOPAVENT model provided an invasive means to estimate steady state blood gas predictions using ventilator inputs and then was further developed to incorporate other parameters such as pulmonary shunt [122,123]. However, the original models required an invasive measurement and also proved to be computationally expensive. More recently, the SOPAVENT models have been developed to provide a continuous real-time non-invasive prediction of blood gas parameters which has been validated in the ICU [127]. The gas exchange models provide a useful clinical diagnostic to aid clinicians to set ventilation parameters. By examining how successful gas is exchanged in the lungs, the models provide a tool for clinicians to then choose additional ventilation settings. However, the models fail to address the mechanism of recruitment which is common in patients with damaged lungs. Thus, patients who have ARDS may experience improved gas exchange but may still have plenty of additional recruitment available. Therefore, models of alveolar recruitment are also required to help optimise ventilation. 4.2.2. Lumped parameter recruitment models Mathematical models of lung mechanics have driven new areas of research in the hope of being able to choose the most appropriate PEEP setting for patients. Although gas exchange models provide a means of finding optimal gas exchange with respect to PEEP, this may not result in increasing alveolar recruitment which a primary end objective when treating ARDS patients [24,77]. In addition, gas exchange models do not indicate the potential for recruitment, which is currently what the primary clinical goal is as gas exchange is presumed to follow recruitment. Models of lung mechanics have been developed with the aim of studying recruitment behaviour of alveoli. A mathematical model by Hickling aimed to simulate and understand the shape of the PV curve in an ARDS lung [55]. The model confirmed that recruitment occurs above the LIP, and that the fundamental shape of the PV curve changes with recruitment. Although this paper did not adequately predict the volume change resulting from a change in PEEP, it provided information on the effect of the recruitment behaviour on the static PV curve. Although Hickling’s model readily predicts the static PV curve during a recruitment manoeuvre, it has only been used as a research tool to date. One of the earliest contributions to mathematically model the pressure volume curve was the Venegas equation [136], which modelled the PV curve as a sigmoid function. This equation allows the SPV to be obtained based on geometric properties of the curve, while still maintaining good physiological accuracy as defined by its ability to capture vital capacity, maximal inspiratory volume, compliance at different inflation pressures, inflection pressure, and upper and lower corner pressures by fitting the sigmoid equation parameters to measured data. The Venegas equation

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has shown very tight correlation with clinical data and has been validated in its ability to match or fit data in several studies [87,137–141]. More recently, animal studies by Schiller et al. [76] using in vivo microscopy showed that ARDS affected alveoli can be characterised into three distinct groups, with each group exhibiting different compliance and recruitment. Although, mathematical models that account for all unit types have been developed [142], these have been found to contain too many parameters for ready identification at the bedside [143]. Hence, they are not uniquely identifiable with the limited data available and very time consuming to model [143]. To create a clinically viable mathematical model, Yuta et al. developed a single unit type model [126,143]. This model worked on the principle of threshold opening pressure (TOP) and threshold closing pressure (TCP) distributions. During the breathing cycle, different alveoli have different opening and closing pressures, which for all units are distributed over the pressure range and have a given volume once open. Thus, the model captures this effect by assigning a threshold opening and threshold closing pressure distribution. Each distribution is defined by two parameters, a mean and standard deviation, and the values of the distributions give an indication of the severity of the disease state. In particular, a higher mean indicates more pressure to open alveoli, and a broader standard deviation, indicating greater spread, represents a greater heterogeneity of opening pressures as found in ARDS [76]. Although this type of model may not be as physiologically representative as a three unit type model, it has been shown to have very good clinical correlation [126]. In particular, it was shown to accurately predict the volume response or recruitment due to changes in PEEP from independent clinical data. It was also readily identifiable based on limited data available at the bedside using a minimum of two PV loops at different PEEP values. In addition, the model used PV curves and the principle of TOP and TCP mean shifts to assess optimal PEEP in a series of clinical trials [144]. The shift in TOP and TCP distributions as a function of PEEP can also be used to predict and quantify the level of recruitment for a given pressure. By providing information on the volume responsiveness of the patient, clinicians can use the model to get a relatively accurate estimation of the new level of recruitment for a given PEEP. In particular, it does so at the bedside with little time or effort using only data from a standard ventilator [144]. Hence, it could be applied several times a day to monitor the patient and optimise the MV. This outcome is particularly important given the relatively recent study by Gattinoni et al. [24]. The primary conclusion was that mortality and length of MV could be reduced only by recruiting the lung that is available to be recruited. To achieve this goal, the study used CT scans to determine the amount of recruitable lung available. Thus, in order to decide the level of PEEP, the clinician must know the volume response to PEEP, and in particular, the potential in volume responsiveness. Hence, per [24] a clinician would seek to increase PEEP for patient whose TOP mean shift is high, but might refrain from changing PEEP when the TOP mean shift does not change much. The major limitation of this model is a relative lack of physiological detail. The model does not predict the individual response of the three different alveoli groups. However, the study by Schiller et al. [76] showed three distinct compliance curves for the different alveoli types which could be later incorporated into the model at the cost of computational time. 4.2.3. Stress strain models An alternate approach to recruitment models using the work by Chiumello et al. [57] provides a means to estimate PEEP induced dynamic functional residual capacity (dFRC) using lung stress and

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5. Conclusions

Fig. 2. Dynamic FRC on a PV curve.

strain [125,145,146]. In particular, using lung stress and strain as a proxy for pressure and volume, would incorporate these clinically validated [57] measures directly into the model and its clinical use. Dynamic FRC could be useful because it captures the same volume change with PEEP. This potential alternative approach is shown in Fig. 2, where two levels of PEEP exhibit a different dFRC [125,146]. The model of Fig. 2 used a single global patient parameter and the patient’s volume response to PEEP to predict the dFRC for a given PEEP. The global parameter, ˇ, is a function of PEEP and captures physiological parameters of functional residual capacity (FRC), lung and respiratory elastance. The advantage of this model is its ability to predict dFRC in a clinical setting [144]. Clinicians can then also predict the amount of extra volume that can be recruited due to an increase in PEEP before application. In contrast, the model of Yuta et al. [126,143] requires the dFRC to be explicitly measured before the model can be used. Thus, the both the lung mechanics model and the dFRC model can be used in conjunction with each other to predict the dFRC and then to predict recruitment behaviours. Another method of determining optimal PEEP uses the stress index. One primary conclusion is that the stress index is a good indicator on which to titrate PEEP [147–151]. The stress index describes the shape of the pressure time curve and describes how the rate of change of pressure reflects changes in lung elastance. In these studies, PEEP titration was performed for each patient based on the value of the stress index. The main objective was to minimise damage to alveoli and mitigate the risk of VILI from excessive lung stress. However, this method is limited as it can only be used when a constant flow waveform is used [152]. Although model-based methods can be software and hardware intensive, in essence, a model-based approach could be implemented with only a standard laptop and existing hardware. For example, with respect to recruitment models, the only additional requirement is a new algorithm. Existing ventilators already have the required hardware to measure PV curves. It is not difficult to implement the new algorithms into ventilators, and ventilator companies often perform software upgrades to existing equipment. Thus, any new model-based approach could effectively be implemented by upgrading ventilator software. In contrast, imaging techniques often require new technology, which may not be available in the clinic, and additional software to allow image processing.

The formal definition of ARDS has revolutionised the way mechanical ventilation is carried out on patients. Of the two main parameters in setting MV, all else equal, various studies quickly showed that a low tidal volume is preferred for ARDS patients. However, the choice of PEEP and how to determine it remain hotly debated. Various methods to choose PEEP have been reviewed and discussed in this analysis, including a formal basic problem statement outlining the fundamental tradeoffs in the issue. In particular, the ultimate purpose of choosing PEEP is to optimise ventilation therapy so that patients quality of health care is increased and their length of MV (and thus cost) are decreased. The use of imaging and model-based methods highlights two different approaches being developed for guiding ventilation therapy. However, as resources vary between ICU’s across the world, the individual methods used may vary. Resource strapped hospitals may adopt to using model-based approaches. Although lacking detailed physiological detail, model-based approaches allow rapid parameter identification and yield sufficient information noninvasively to help guide PEEP selection. In contrast, hospitals with more resources may opt to use imaging techniques such as EIT or lung ultrasound. While imaging techniques require specialist training to use, they give a clearer picture of lung heterogeneities which is crucial in ARDS patients and PEEP titration. The development of new imaging techniques and model-based methods for direct clinical use is a nascent field in general and in critical care. Although imaging techniques such as CT scans have been developed, radiation free methods are relatively new and are only now showing clinical promise. The new imaging methods discussed show the potential use in the clinic but are still not wide spread. In addition, for the specific therapy of MV in ARDS no model based methods have yet been used or deployed clinically in practice. The models presented represent the very first steps into a potential future clinical practice. Thus, a state of possible, rather than a state of current practice is reviewed with further clinical trials needed to justify use in the clinic. Conflict of interest No conflict of interests. References [1] D.G. Ashbaugh, et al., Acute respiratory distress in adults, Lancet 2 (7511) (1967) 319–323. [2] T.L. Petty, D.G. Ashbaugh, The adult respiratory distress syndrome, Chest 60 (3) (1971) 233–239. [3] A.D. Bersten, et al., Incidence and mortality of acute lung injury and the acute respiratory distress syndrome in three Australian states, Am. J. Respir. Crit. Care Med. 165 (4) (2002) 443–448. [4] O.R. Luhr, et al., The impact of respiratory variables on mortality in non-ARDS and ARDS patients requiring mechanical ventilation, Intensive Care Med. 26 (5) (2000) 508–517. [5] F. Manzano, et al., Incidence of acute respiratory distress syndrome and its relation to age, J. Crit. Care 20 (3) (2005) 274–280. [6] H.N. Reynolds, et al., Acute respiratory distress syndrome: estimated incidence and mortality rate in a 5 million-person population base, Crit. Care 2 (29) (1998) 29–34. [7] A. Esteban, et al., Characteristics and outcomes in adult patients receiving mechanical ventilation: a 28-day international study, JAMA 287 (3) (2002) 345–355. [8] N.D. Ferguson, et al., Airway pressures, tidal volumes, and mortality in patients with acute respiratory distress syndrome, Crit. Care Med. 33 (1) (2005) 21–30. [9] M.R. Suchyta, et al., Increased mortality of older patients with acute respiratory distress syndrome, Chest 111 (1997) 1334–1339. [10] Zilberberg, D. Marya, K. Scott, Epstein, Acute lung injury in the medical ICU. Comorbid conditions, age, etiology, and hospital outcome, Am. J. Respir. Crit. Care Med. 157 (4) (1998) 1159–1164. [11] J. Phua, et al., Has mortality from acute respiratory distress syndrome decreased over time? A systematic review, Am. J. Respir. Crit. Care Med. 179 (3) (2009) 220–227.

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