The dynostatic algorithm accurately calculates alveolar pressure on-line during ventilator treatment in children

Peptide-Functionalized Electrospun Meshes for the Physiological Cultivation of Pulmonary Alveolar Capillary Barrier Models in a 3D-Printed Micro-Bioreactor

In vitro environments that realize biomimetic scaffolds, cellular composition, physiological shear, and strain are integral to developing tissue models of organ-specific functions. In this study, an in vitro pulmonary alveolar capillary barrier model is developed that closely mimics physiological functions by combining a synthetic biofunctionalized nanofibrous membrane system with a novel three-dimensional (3D)-printed bioreactor. The fiber meshes are fabricated from a mixture of polycaprolactone (PCL), 6-armed star-shaped isocyanate-terminated poly(ethylene glycol) (sPEG-NCO), and Arg-Gly-Asp (RGD) peptides by a one-step electrospinning process that offers full control over the fiber surface chemistry. The tunable meshes are mounted within the bioreactor where they support the co-cultivation of pulmonary epithelial (NCI-H441) and endothelial (HPMEC) cell monolayers at air-liquid interface under controlled stimulation by fluid shear stress and cyclic distention. This stimulation, which closely mimics blood circulation and breathing motion, is observed to impact alveolar endothelial cytoskeleton arrangement and improve epithelial tight junction formation as well as surfactant protein B production compared to static models. The results highlight the potential of PCL-sPEG-NCO:RGD nanofibrous scaffolds in combination with a 3D-printed bioreactor system as a platform to reconstruct and enhance in vitro models to bear a close resemblance to in vivo tissues.

Study of Tidal Volume and Positive End-Expiratory Pressure on Alveolar Recruitment Using Spiro Dynamics in Mechanically Ventilated Patients

Background and Aims

Ventilator setting in the intensive care unit patients is a topic of debate and setting of tidal volume (TV) should be patient-specific based on lung mechanics. In this study, we have evaluated to develop optimal ventilator strategies through continuous and thorough monitoring of respiratory mechanics during ongoing ventilator support to prevent alveolar collapse and alveolar injury in mechanically ventilated patients.

Methods

In our monocentric, randomized, observational study, we had recruited 60 patients and divided them into two groups of 30 each. In Group 1 patients, TV and positive end-expiratory pressure (PEEP) were set according to pressure-volume (P/V) curve obtained by the mechanical ventilator in a conventional manner (control group), and in Group 2, TV and PEEP were set according to P/V curve obtained by the mechanical ventilator using intratracheal catheter. PEEP and TV were set accordingly. TV, PEEP, and PaO₂/FiO₂ (P/F) ratio at days 1, 3, and 7, mortality within 7 days and mortality within 28 days were measured in each group and compared.

Results

We found a significant difference between PEEP and P/F ratio in both groups while intragroup comparison at days 1, 3, and 7. After the intergroup comparison of Group 1 and 2, we observed a significant difference of PEEP and P/F ratio between the groups at day 7 and not on day 1 or 3.

Conclusion

This study concludes that optimal PEEP is more accurate using an intratracheal catheter than the conventional method of deciding ventilator setting. Hence, it is recommended to use intratracheal catheter to obtain more accurate ventilator settings.

The pressure drop across the endotracheal tube in mechanically ventilated pediatric patients

BACKGROUND

During mechanical ventilation, the airway pressure (Paw) is usually monitored. However, Paw comprises the endotracheal tube (ETT)-related pressure drop (∆PETT ) and thus does not reflect the pressure in the patients' lungs. Therefore, monitoring of mechanical ventilation should be based on the tracheal pressure (Ptrach ). We systematically investigated potential factors influencing ∆PETT in pediatric ETTs.

METHODS

In this study, the flow-dependent pressure drop across pediatric ETTs from four manufacturers [2.0-4.5 mm inner diameter (ID)] was estimated in a physical model of the upper airways. Additionally, ∆PETT was examined with the ETTs shortened to 75% of their original length and at different curvatures. In nine healthy mechanically ventilated children (aged between 9 days and 29 months), Ptrach was compared to Paw .

RESULTS

∆PETT was nonlinearly flow dependent. Low IDs corresponded to high ∆PETT . Differences between ETTs from different manufacturers were identified. Shortening of the ETTs' length by 25% reduced ∆PETT on average by 14% of the value at original length. Ventilation frequency and tube curvature did not influence ∆PETT to a relevant extent. In the pediatric patients, the root mean square deviation between Paw and Ptrach was 2.3 cm H2O.

CONCLUSION

Paw and Ptrach differ considerably (by ∆PETT ) during mechanical ventilation of pediatric patients. The ETTs' ID, tube length, and manufacturer type are significant factors for ∆PETT and should be taken into account when Paw is valuated. For this purpose, Ptrach can be continuously calculated with good precision by means of the Rohrer approximation.

A patient-specific airway branching model for mechanically ventilated patients

BACKGROUND

Respiratory mechanics models have the potential to guide mechanical ventilation. Airway branching models (ABMs) were developed from classical fluid mechanics models but do not provide accurate models of in vivo behaviour. Hence, the ABM was improved to include patient-specific parameters and better model observed behaviour (ABMps).

METHODS

The airway pressure drop of the ABMps was compared with the well-accepted dynostatic algorithm (DSA) in patients diagnosed with acute respiratory distress syndrome (ARDS). A scaling factor (α) was used to equate the area under the pressure curve (AUC) from the ABMps to the AUC of the DSA and was linked to patient state.

RESULTS

The ABMps recorded a median α value of 0.58 (IQR: 0.54-0.63; range: 0.45-0.66) for these ARDS patients. Significantly lower α values were found for individuals with chronic obstructive pulmonary disease (P < 0.001).

CONCLUSION

The ABMps model allows the estimation of airway pressure drop at each bronchial generation with patient-specific physiological measurements and can be generated from data measured at the bedside. The distribution of patient-specific α values indicates that the overall ABM can be readily improved to better match observed data and capture patient condition.

Linear Model and Algorithm to Automatically Estimate the Pressure Limit of Pressure Controlled Ventilation for Delivering a Target Tidal Volume

OBJECTIVE

To theoretically assess the viability of an automatic procedure to support the anesthesiologist in properly setting mechanical ventilators when the operating conditions are switched from volume controlled to pressure controlled ventilation whilst maintaining the preset tidal volume. The procedure is based on a simple linear model of the ventilator breathing system with constant parameters and utilizes the signals gathered by the ventilator without the need to add further equipment. After a short period of stable volume controlled ventilation with the desired tidal volume, the herewith described algorithm allows the calculation of the value of pressure limit to set in pressure controlled mode which assures the previously settled tidal volume with the same breathing frequency and inspiratory-expiratory time ratio.

METHODS

The algorithm allows the online identification of the four parameters necessary for the mathematical model that are obtained by means of a direct comparison between the pressure, flow and volume waveforms generated by the model and the analog signals provided by the ventilator. The theoretical approach was validated by two different ventilators, various settings, two breathing circuits, endotracheal tubes of various sizes and two mechanical simulators of the respiratory system operating in various conditions.

RESULTS

Errors usually less than 5% (p < 0.05) on the target tidal volume were obtained for various settings typically used for adult ventilation in less than 10 s. The theoretical approach shows its limitations (errors of 10+/- 5%, p < 0.05) at high breathing frequencies (30-40 bpm) and low tidal volumes (200-300 ml).

CONCLUSIONS

The proposed theoretical approach shows the viability, for adult settings, of one of the simplest mathematical model for mechanical ventilation in order to quickly and safely switch from volume controlled to pressure controlled ventilation. The algorithm could easily be in perspective implemented in the software of the ventilator providing the anesthesiologist with an indication on the value of pressure limit to set in order to safely switch ventilation mode.