Individualized mechanical ventilation in a shared ventilator setting: limits, safety and technical details

Development and evaluation of a mechanical ventilator-sharing system

Background

During the COVID-19 pandemic surge in the hospitalization of critically ill patients and the global demand for mechanical ventilators, alternative strategies for device sharing were explored. We developed and assessed the performance of a system for shared ventilation that uses clinically available components to individualize tidal volumes under a variety of clinically relevant conditions. The feasibility of remote monitoring of ventilators was also assessed.

Methods

By using existing resources and off-the-shelf components, a ventilator-sharing system (VSS) that ventilates 2 patients simultaneously with a single device, and a ventilator monitoring system (VMS) that remotely monitors pulmonary mechanics were developed. The feasibility and effectiveness of VSS and VMS were evaluated in benchtop testing using 2 test lungs on a single ventilator, and then performance was assessed in translational swine models of normal and impaired lung function.

Results

In benchtop testing, VSS and VMS delivered the set individualized parameters with minimal % errors in test lungs under pressure- and volume-regulated ventilation modes, suggesting the highest precision and accuracy. In animal studies, the VSS and VMS successfully delivered the individualized mechanical ventilation parameters within clinically acceptable limits. Further, we found no statistically significant difference between the target and measured values.

Conclusion

The VSS adequately ventilated 2 test lungs or animals with variable lung conditions. The VMS accurately displayed mechanical ventilation settings, parameters, and alarms. Both of these systems could be rapidly assembled for scaling up to ventilate several critically ill patients in a pandemic or mass casualty disaster situations by leveraging off-the-shelf and custom 3D printed components.

Mechanical Ventilation, Past, Present, and Future

Mechanical ventilation (MV) has played a crucial role in the medical field, particularly in anesthesia and in critical care medicine (CCM) settings. MV has evolved significantly since its inception over 70 years ago and the future promises even more advanced technology. In the past, ventilation was provided manually, intermittently, and it was primarily used for resuscitation or as a last resort for patients with severe respiratory or cardiovascular failure. The earliest MV machines for prolonged ventilatory support and oxygenation were large and cumbersome. They required a significant amount of skills and expertise to operate. These early devices had limited capabilities, battery, power, safety features, alarms, and therefore these often caused harm to patients. Moreover, the physiology of MV was modified when mechanical ventilators moved from negative pressure to positive pressure mechanisms. Monitoring systems were also very limited and therefore the risks related to MV support were difficult to quantify, predict and timely detect for individual patients who were necessarily young with few comorbidities. Technology and devices designed to use tracheostomies versus endotracheal intubation evolved in the last century too and these are currently much more reliable. In the present, positive pressure MV is more sophisticated and widely used for extensive period of time. Modern ventilators use mostly positive pressure systems and are much smaller, more portable than their predecessors, and they are much easier to operate. They can also be programmed to provide different levels of support based on evolving physiological concepts allowing lung-protective ventilation. Monitoring systems are more sophisticated and knowledge related to the physiology of MV is improved. Patients are also more complex and elderly compared to the past. MV experts are informed about risks related to prolonged or aggressive ventilation modalities and settings. One of the most significant advances in MV has been protective lung ventilation, diaphragm protective ventilation including noninvasive ventilation (NIV). Health care professionals are familiar with the use of MV and in many countries, respiratory therapists have been trained for the exclusive purpose of providing safe and professional respiratory support to critically ill patients. Analgo-sedation drugs and techniques are improved, and more sedative drugs are available and this has an impact on recovery, weaning, and overall patients' outcome. Looking toward the future, MV is likely to continue to evolve and improve alongside monitoring techniques and sedatives. There is increasing precision in monitoring global "patient-ventilator" interactions: structure and analysis (asynchrony, desynchrony, etc). One area of development is the use of artificial intelligence (AI) in ventilator technology. AI can be used to monitor patients in real-time, and it can predict when a patient is likely to experience respiratory distress. This allows medical professionals to intervene before a crisis occurs, improving patient outcomes and reducing the need for emergency intervention. This specific area of development is intended as "personalized ventilation." It involves tailoring the ventilator settings to the individual patient, based on their physiology and the specific condition they are being treated for. This approach has the potential to improve patient outcomes by optimizing ventilation and reducing the risk of harm. In conclusion, MV has come a long way since its inception, and it continues to play a critical role in anesthesia and in CCM settings. Advances in technology have made MV safer, more effective, affordable, and more widely available. As technology continues to improve, more advanced and personalized MV will become available, leading to better patients' outcomes and quality of life for those in need.

Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning

Background and objective

In pandemic situations like COVID 19, real time monitoring of patient condition and continuous delivery of inspired oxygen can be made possible only through artificial intelligence-based system modeling. Even now manual control of mechanical ventilator parameters is continuing despite the ever-increasing number of patients in critical epidemic conditions. Here a suggestive multi-layer perceptron neural network model is developed to predict the level of inspired oxygen delivered by the mechanical ventilator along with mode and positive end expiratory pressure (PEEP) changes for reducing the effort of health care professionals.

Methods

The artificial neural network model is developed by Python programming using real time data. Parameter identification for model inputs and outputs is done by in corporating consistent real time patient data including periodical arterial blood gas analysis, continuous pulse oximetry readings and mechanical ventilator settings using statistical pairwise analysis using R programming.

Results

Mean square error values and R values of the model are calculated and found to be an average of 0.093 and 0.81 respectively for various data sets. Accuracy loss will be in good fit with validation loss for a comparable number of epochs.

Conclusions

Comparison of the model output is undertaken with physician’s prediction using statistical analysis and shows an accuracy error of 4.11 percentages which is permissible for a good predictive system.

Sharing Mechanical Ventilator: In Vitro Evaluation of Circuit Cross-Flows and Patient Interactions

During the COVID-19 pandemic, a shortage of mechanical ventilators was reported and ventilator sharing between patients was proposed as an ultimate solution. Two lung simulators were ventilated by one anesthesia machine connected through two respiratory circuits and T-pieces. Five different combinations of compliances (30–50 mL × cmH₂O⁻¹) and resistances (5–20 cmH₂O × L⁻¹ × s⁻¹) were tested. The ventilation setting was: pressure-controlled ventilation, positive end-expiratory pressure 15 cmH₂O, inspiratory pressure 10 cmH₂O, respiratory rate 20 bpm. Pressures and flows from all the circuit sections have been recorded and analyzed. Simulated patients with equal compliance and resistance received similar ventilation. Compliance reduction from 50 to 30 mL × cmH₂O⁻¹ decreased the tidal volume (V_T) by 32% (418 ± 49 vs. 285 ± 17 mL). The resistance increase from 5 to 20 cmH₂O × L⁻¹ × s⁻¹ decreased V_T by 22% (425 ± 69 vs. 331 ± 51 mL). The maximal alveolar pressure was lower at higher compliance and resistance values and decreased linearly with the time constant (r² = 0.80, p < 0.001). The minimum alveolar pressure ranged from 15.5 ± 0.04 to 16.57 ± 0.04 cmH₂O. Cross-flows between the simulated patients have been recorded in all the tested combinations, during both the inspiratory and expiratory phases. The simultaneous ventilation of two patients with one ventilator may be unable to match individual patient’s needs and has a high risk of cross-interference.

Multiplex Ventilation: Solutions for Four Main Safety Problems

BACKGROUND

The COVID-19 pandemic has led to an increased demand for mechanical ventilators and concerns of a ventilator shortage. Several groups have advocated for 1 ventilator to ventilate 2 or more patients in the event of such a shortage. However, differences in patient lung mechanics could make sharing a ventilator detrimental to both patients. Our previous study indicated failure to ventilate in 67% of simulations. The safety problems that must be solved include individual control of tidal volume (V_T), individual measurement of V_T, individualization of PEEP settings, and individual PEEP measurement. The purpose of this study was to evaluate potential solutions developed at our institution.

METHODS

Two separate lung simulators were ventilated with a modified multiplex circuit using pressure control ventilation. Parameters of the lung models used for simulations (resistance and compliance) were evidence-based from published studies. Individual circuit-modification devices were first evaluated for accuracy. Devices were an adjustable flow diverter valve, a prototype dual volume display, a PEEP valve, and a disposable PEEP display. Then the full modified multiplex circuit was assessed by ventilating 6 pairs of simulated patients with different lung models and attempting to equalize ventilation. Ventilation was considered equalized when V_T and end-expiratory lung volume were within 10% for each simulation.

RESULTS

The adjustable flow diverter valve allowed volume adjustment to 1 patient without affecting the other. The average error of the dual volume display was -17%. The PEEP valves individualized PEEP, but the PEEP gauge error ranged from 17% to 41%. Using the multiplex circuit, ventilation was equalized regardless of differences in resistance or compliance, reversing the "failure modes" of our previous study.

CONCLUSIONS

The results of this simulation-based study indicate that devices for individual control and display of V_T and PEEP are effective in extending the usability and potential patient safety of multiplex ventilation.

2020 Year in Review: Shared Ventilation for COVID-19

COVID-19 resulting from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a pandemic of respiratory failure previously unencountered. Early in the pandemic, concentrated infections in high-density population cities threatened to overwhelm health systems, and ventilator shortages were predicted. An early proposed solution was the use of shared ventilation, or the use of a single ventilator to support ≥ 2 patients. Spurred by ill-conceived social media posts, the idea spread in the lay press. Prior to 2020, there were 7 publications on this topic. A year later, more than 40 publications have addressed the technical details for shared ventilation, clinical experience with shared ventilation, as well as the numerous limitations and ethics of the technique. This is a review of the literature regarding shared ventilation from peer-reviewed articles published in 2020.

The eSpiro Ventilator: An Open-Source Response to a Worldwide Pandemic

OBJECTIVE

To address the issue of ventilator shortages, our group (eSpiro Network) developed a freely replicable, open-source hardware ventilator.

DESIGN

We performed a bench study.

SETTING

Dedicated research room as part of an ICU affiliated to a university hospital.

SUBJECTS

We set the lung model with three conditions of resistance and linear compliance for mimicking different respiratory mechanics of representative intensive care unit (ICU) patients.

INTERVENTIONS

The performance of the device was tested using the ASL5000 lung model.

MEASUREMENTS AND MAIN RESULTS

Twenty-seven conditions were tested. All the measurements fell within the ±10% limits for the tidal volume (V_T). The volume error was influenced by the mechanical condition (p = 5.9 × 10^-15) and the PEEP level (P = 1.1 × 10^-12) but the clinical significance of this finding is likely meaningless (maximum -34 mL in the error). The PEEP error was not influenced by the mechanical condition (p = 0.25). Our experimental results demonstrate that the eSpiro ventilator is reliable to deliver V_T and PEEP accurately in various respiratory mechanics conditions.

CONCLUSIONS

We report a low-cost, easy-to-build ventilator, which is reliable to deliver V_T and PEEP in passive invasive mechanical ventilation.

Simultaneous ventilation in the Covid-19 pandemic. A bench study

COVID-19 pandemic sets the healthcare system to a shortage of ventilators. We aimed at assessing tidal volume (V_T) delivery and air recirculation during expiration when one ventilator is divided into 2 test-lungs. The study was performed in a research laboratory in a medical ICU of a University hospital. An ICU (V500) and a lower-level ventilator (Elisée 350) were attached to two test-lungs (QuickLung) through a dedicated flow-splitter. A 50 mL/cmH₂O Compliance (C) and 5 cmH₂O/L/s Resistance (R) were set in both A and B test-lungs (A C50R5 / B C50R5, step1), A C50-R20 / B C20-R20 (step 2), A C20-R20 / B C10-R20 (step 3), and A C50-R20 / B C20-R5 (step 4). Each ventilator was set in volume and pressure control mode to deliver 800mL V_T. We assessed V_T from a pneumotachograph placed immediately before each lung, pendelluft air, and expiratory resistance (circuit and valve). Values are median (1^st-3^rd quartiles) and compared between ventilators by non-parametric tests. Between Elisée 350 and V500 in volume control V_T in A/B test- lungs were 381/387 vs. 412/433 mL in step 1, 501/270 vs. 492/370 mL in step 2, 509/237 vs. 496/332 mL in step 3, and 496/281 vs. 480/329 mL in step 4. In pressure control the corresponding values were 373/336 vs. 430/414 mL, 416/185 vs. 322/234 mL, 193/108 vs. 176/ 92 mL and 422/201 vs. 481/329mL, respectively (P<0.001 between ventilators at each step for each volume). Pendelluft air volume ranged between 0.7 to 37.8 ml and negatively correlated with expiratory resistance in steps 2 and 3. The lower-level ventilator performed closely to the ICU ventilator. In the clinical setting, these findings suggest that, due to dependence of V_T to C, pressure control should be preferred to maintain adequate V_T at least in one patient when C and/or R changes abruptly and monitoring of V_T should be done carefully. Increasing expiratory resistance should reduce pendelluft volume.