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Register a new userMechanical Ventilator Milano (MVM): A Novel Mechanical Ventilator Designed for Mass Scale Production in Response to the COVID-19 Pandemic
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Presented here is the design of the Mechanical Ventilator Milano (MVM), a novel mechanical ventilator designed for rapid mass production in response to the COVID-19 pandemic to address the urgent shortage of intensive therapy ventilators in many countries, and the growing difficulty in procuring these devices through normal supply chains across borders. This ventilator is an electro-mechanical equivalent of the old and reliable Manley Ventilator, and is able to operate in both pressure-controlled and pressure-supported ventilation modes. MVM is optimized for the COVID-19 emergency, thanks to the collaboration with medical doctors in the front line. MVM is designed for large-scale production in a short amount of time and at a limited cost, as it relays on off-the-shelf components, readily available worldwide. Operation of the MVM requires only a source of compressed oxygen (or compressed medical air) and electrical power. Initial tests of a prototype device with a breathing simulator are also presented. Further tests and developments are underway. At this stage the MVM is not yet a certified medical device but certification is in progress.
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HEV is a low-cost, versatile, high-quality ventilator, which has been designed in response to the COVID-19 pandemic. The ventilator is intended to be used both in and out of hospital intensive care units, and for both invasive and non-invasive ventilation. The hardware can be complemented with an external turbine for use in regions where compressed air supplies are not reliably available. The standard modes provided include PC-A/C(Pressure Assist Control),PC-A/C-PRVC(Pressure Regulated Volume Control), PC-PSV (Pressure Support Ventilation) and CPAP (Continuous Positive airway pressure). HEV is designed to support remote training and post market surveillance via a web interface and data logging to complement the standard touch screen operation, making it suitable for a wide range of geographical deployment. The HEV design places emphasis on the quality of the pressure curves and the reactivity of the trigger, delivering a global performance which will be applicable to ventilator needs beyond theCOVID-19 pandemic. This article describes the conceptual design and presents the prototype units together with their performance evaluation.
Clinical decision support systems (CDSS) will play an in-creasing role in improving the quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have com-plete information on state of supporting physiologic monitor-ing devices, which can limit the input data available to CDSS. This is especially true in the use case of mechanical ventilation (MV), where current CDSS have no knowledge of critical ventilation settings, such as ventilation mode. To enable MV CDSS to make accurate recommendations related to ventilator mode, we developed a highly performant ma-chine learning model that is able to perform per-breath clas-sification of 5 of the most widely used ventilation modes in the USA with an average F1-score of 97.52%. We also show how our approach makes methodologic improvements over previous work and that it is highly robust to missing data caused by software/sensor error.
We propose the design of a ventilator which can be easily manufactured and integrated into the hospital environment to support COVID-19 patients. The unit is designed to support standard ventilator modes of operation, most importantly PRVC (Pressure Regulated Volume Control) and SIMV-PC (Synchronised Intermittent Mandatory Ventilation) modes. The unit is not yet an approved medical device and is in the concept and prototyping stage. It is presented here to invite fast feedback for development and deployment in the face of the COVID-19 pandemic.
Due to the recent coronavirus outbreak, many efforts and innovative solutions have surfaced to deal with the possible shortage of ventilators upon catastrophic surges of patients. One solution involves splitting one ventilator to treat multiple patients and is in principle easy to implement, but there are obvious risks, and little is known on how the technique would work on patients with ARDS from Covid-19. Previous studies have shown that multiple test lungs of equal characteristics can be successfully ventilated from one machine, but that large variations in tidal volume delivery occurs when lungs with different compliance are connected. In contribution to the discussion of the feasibility of the technique, a technical assessment was done including experiments expanding on the previous studies using two types of test lungs, different ventilator settings and test lung characteristics. Using two test lungs connected to a ventilator, the tidal volumes and pressures into both lungs were measured for different combinations of lung compliance, airway resistances, modes of ventilation, inspiratory and end-expiratory pressure levels. We found discrepancies in delivered tidal volumes for paired test lungs proportional with compliance differences, little influence from differences in airway resistances, and that changes in compliance of only one test lung would also change the tidal volume delivered to the other test lung when in volume controlled mode. For one of the test lung types, we also found that higher PEEP settings could strongly influence the tidal volume balance between the test lungs. From this study and from a technical point of view, we were not able to identify reliable settings, adjustments or any simple measures to overcome the hazards of this simple technique, and a more advanced solution is indicated for mitigating risks.
This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling the COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. The results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.
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