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Register a new userSplitting one ventilator for multiple patients -- a technical assessment
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Added by
Orjan Grottem Martinsen PhD
Publication date
2020
fields
Physics
and research's language is
English
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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.
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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.
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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Background: Elderly patients with MODS have high risk of death and poor prognosis. The performance of current scoring systems assessing the severity of MODS and its mortality remains unsatisfactory. This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS. Methods: The MIMIC-III, eICU-CRD and PLAGH-S databases were employed for model generation and evaluation. We used the eXtreme Gradient Boosting model with the SHapley Additive exPlanations method to conduct early and interpretable predictions of patients hospital outcome. Three types of data source combinations and five typical evaluation indexes were adopted to develop a generalizable model. Findings: The interpretable model, with optimal performance developed by using MIMIC-III and eICU-CRD datasets, was separately validated in MIMIC-III, eICU-CRD and PLAGH-S datasets (no overlapping with training set). The performances of the model in predicting hospital mortality as validated by the three datasets were: AUC of 0.858, sensitivity of 0.834 and specificity of 0.705; AUC of 0.849, sensitivity of 0.763 and specificity of 0.784; and AUC of 0.838, sensitivity of 0.882 and specificity of 0.691, respectively. Comparisons of AUC between this model and baseline models with MIMIC-III dataset validation showed superior performances of this model; In addition, comparisons in AUC between this model and commonly used clinical scores showed significantly better performance of this model. Interpretation: The interpretable machine learning model developed in this study using fused datasets with large sample sizes was robust and generalizable. This model outperformed the baseline models and several clinical scores for early prediction of mortality in elderly ICU patients. The interpretative nature of this model provided clinicians with the ranking of mortality risk features.
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