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Download poster here. Read more Weblink here. Results indicate that at the same ventilation efficiency same PaCO 2 level , we expect tidal volume dosage in the range of 7. For a neonatal RDS model, we expect 5. The algorithms usually adapt to the changing characteristics of the patient, such as mechanics resistance, compliance, and inspiratory effort or ventilatory pattern frequency and tidal volume , and choose an appropriate response.
One strategy to incorporate clinical knowledge into machine design is to use what is called an optimum targeting scheme [ 1 ], a term adapted from engineering control theory. An optimum targeting scheme is based on a mathematical model that attempts to minimize or maximize some desired outcome.
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In optimization theory, that model is also called a cost function. These criteria are based on actual patient characteristics e. Hence, the goal of an optimum targeting scheme is to find the ventilation pattern with the lowest cost. If this optimum pattern is found, it can be used to set values targets for the underlying controllers. In the first section of this paper, familiar ventilation parameters tidal volume, tidal pressure, and tidal power are used to derive cost functions. Also, the cost functions, which underlie the ventilation modes called adaptive support ventilation ASV , adaptive ventilation mode 2 AVM2 , and mid frequency ventilation MFV , are derived.
Next, we perform mathematical analyses to compare the characteristics of these optimum target schemes. These analyses tend to be complex and hard to interpret intuitively. For this reason, a summary of clinical evidence is presented along with mathematical simulations we performed to compare and visualize the results of the cost function minimization. Relation between cost functions and targets. Every cost function tidal volume, tidal pressure, tidal power, breathing power, inspiratory power, and inspiratory pressure was minimized analytically or using mathematical simulation. After that, the frequency was used to calculate the target tidal volume.
The pivotal study by the Acute Respiratory Distress Syndrome Network in established the notion that in patients with acute lung injury and acute respiratory distress syndrome, mechanical ventilation with a lower tidal volume dosage 6. There are also data to support the use of low V T in patients without pre-existing lung injury [ 3 , 4 , 5 , 6 ].
A recent study even suggests that lung protective ventilation might be considered a prophylactic therapy, rather than just a supportive therapy [ 7 ]. If we assume a value for the required alveolar minute volume MV A and simply desire to control the tidal volume V T dosage for a passive patient, we can derive the cost function as follows:.
However, we see that there is no definite minimum value because tidal volume converges to the dead space volume as frequency increases to infinity. In practice, the limit would be dependent on the volume delivery performance characteristics of the ventilator, because no ventilator is a perfect flow controller.
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Simply controlling the tidal volume dosage, independent of any consideration of lung mechanics, may have limited utility. Recent work has suggested that V T normalized to lung mechanics e. In patients having surgery, intra-operative high P T and changes in the level of PEEP that resulted in an increase of P T were associated with more postoperative pulmonary complications [ 13 ].
However, if we define an optimal targeting scheme as minimization of P T , we get the same result as minimizing to tidal volume because the tidal pressure is linked to driving pressure by compliance, C , which can be considered simply a scaling factor. If compliance only affects the scaling of the cost function, then it has no influence to the location of the minimum.
Gattinoni et al. The solution is obtained analytically by differentiating tidal power with respect to f and setting the result to zero. Solving Eq. If we express MV as the product of tidal volume and frequency, the optimal tidal volume i. As mentioned, driving power is connected by a scaling factor to tidal power.
Therefore, the condition of minimal driving power is fulfilled at the same optimal frequency and therefore yields the same optimal tidal volume. Cressoni et al. In , Otis et al. They made the assumption that the brain seeks an optimum frequency by minimizing breathing effort. To derive the cost function of breathing effort, they assumed a one compartment lung model with linear compliance and non-linear resistance:. On the basis of that model, breathing effort was defined as work rate or power. With the assumptions of Eqs. To find the optimal frequency at minimal breathing power, the following optimization problem must be solved.
Otis solved Eq. Instead of solving for f , he solved the equation for MV A to get a solution for the conditions of minimal breathing power. Later, Mead [ 19 ] simplified Eq. Then, he solved the optimization problem of Eq. As an alternative to Eq.
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Otis et al. In , Fleur T. Tehrani patented a targeting scheme based on Eq. Note that the development of this targeting scheme was almost a decade before intensive research on the role of tidal volume dosage on mortality. At that time, the concern was to avoid an excessively large tidal volume, not to minimize it.
Nevertheless, over the years, ASV has proven to be effective and results in relatively protective tidal volume delivery in the range of 8. Ventilation modes using adaptive targeting based on Eq. To reduce tidal volume and subsequently tidal pressure [ 10 ], we can derive the concept of mean inspiratory power [ 26 ]. Inspiratory power is defined as the sum of the resistive and tidal power which is transmitted from the ventilator to the patient assuming intrinsic PEEP equal zero:.
There are differences among inspiratory power, total power [ 15 ], elastic power, breathing power, and tidal power. Elastic power includes tidal power and PEEP power, inspiratory power includes tidal power and resistive power, and total power includes elastic power and resistive power. Note that power is defined as the work per unit time, which is calculated as the product of work and ventilatory frequency. Inspiratory work per breath is defined as the integral of inspiratory pressure with respect to inspiratory volume, or graphically, the area between the pressure curve and the volume axis as shown in Fig.
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Definition of the different power components of inspiration and expiration. Breathing power was introduced by Otis, resistive and elastic power was defined by Gattinoni, and Marini differentiated elastic power into its components PEEP power and tidal power. The authors now introduce the concept of inspiratory power which is composed of tidal and resistive power. Note that the figure shows work instead of power and power is the result of the product between work and ventilation frequency. Otis derived the mean power which is needed to breathe without the support of a ventilator with a sinusoidal muscle pressure waveform.
On the contrary, the concept of inspiratory power relies on the principle of how much power is delivered to the patient by a ventilator using a square pressure waveform assuming total PEEP equals zero. Inspiratory power is not intended to be another predictor for VILI. Instead, it serves as the basis for defining an alternative cost function which may be used to describe an optimal ventilation pattern.
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Inspiratory power includes not only tidal power which might be a better indicator for VILI but also includes resistive power. However, as we will see, minimizing inspiratory power converges for specific patient characteristics to the same result as minimizing tidal power which might be relevant for VILI prevention.
Inspiratory power can be calculated as:. To find the optimal frequency for minimal inspiratory power f IP , the following optimization problem must be solved:. Note that Eq. The optimum frequency is found using an iterative numerical process, starting with a seed value. It can be shown that Eq. Therefore, the optimal frequency for minimal inspiratory power is always equal to or less than the frequency for minimal tidal power. Marini et al. In , Chatburn and Mireles-Cabodevila extended this equation to predict alveolar minute volume as a function of frequency and invented a new optimal targeting scheme called mid-frequency ventilation MFV [ 28 ].