Dataset Information

Robust, maintainable, emergency invasive mechanical ventilator.

ABSTRACT:

Objective

To develop a simple, robust, safe and efficient invasive mechanical ventilator that can be used in remote areas of the world or war zones where the practical utility of more sophisticated equipment is limited by considerations of maintainability, availability of parts, transportation and/or cost.

Methods

The device implements the pressure-controlled continuous mandatory ventilation mode, complemented by a simple assist-control mode. Continuous positive airway pressure is also possible. The consumption of compressed gases is minimized by avoiding a continuous flow of oxygen or air. Respiratory rates and inspiration/expiration time ratios are electronically determined, and an apnea/power loss alarm is provided.

Results

The pressure profiles were measured for a range of conditions and found to be adjustable within a ± 2.5cmH2O error margin and stable well within this range over a 41-hour period. Respiratory cycle timing parameters were precise within a few percentage points over the same period. The device was tested for durability for an equivalent period of four months. Chemical and biological tests failed to identify any contamination of the gas by volatile organic compounds or microorganisms. A ventilation test on a large animal, in comparison with a well established ventilator, showed that the animal could be adequately ventilated over a period of 60 minutes, without any noticeable negative aftereffects during the subsequent 24-hour period.

Conclusion

This ventilator design may be viable, after further animal tests and formal approval by the competent authorities, for clinical application in the abovementioned atypical circumstances.

SUBMITTER: Fonte PJR

PROVIDER: S-EPMC9749093 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Robust, maintainable, emergency invasive mechanical ventilator.

Fonte Paulo J R PJR Martinho Alberto A Pereira Américo A Gomes Andreia A Neves Ângela Â Abrunhosa Antero A Bugalho António A Gabriel-Santos António A Grilo António A Carmo Carlos C Maltez Elsa E Nascimento João Agostinho do JAD Goes João J Martins João J Oliveira João Pedro JP Pimenta Jorge J Santos José Paulo JP Gil Luís C LC Lopes Luís L Pimenta Mário M Moreira Olga O Cunha Orlando O Sousa Pedro Pinheiro de PP Póvoa Pedro P Cavaco-Gonçalves Sandra S Barroso Susana S Santos Telmo G TG

Revista Brasileira de terapia intensiva 20221104 3

<h4>Objective</h4>To develop a simple, robust, safe and efficient invasive mechanical ventilator that can be used in remote areas of the world or war zones where the practical utility of more sophisticated equipment is limited by considerations of maintainability, availability of parts, transportation and/or cost.<h4>Methods</h4>The device implements the pressure-controlled continuous mandatory ventilation mode, complemented by a simple assist-control mode. Continuous positive airway pressure is ...[more]

PMID: 36351067

Similar Datasets

Project description:BackgroundOne of the significant changes in intensive care medicine over the past 2 decades is the acknowledgment that improper mechanical ventilation settings substantially contribute to pulmonary injury in critically ill patients. Artificial intelligence (AI) solutions can optimize mechanical ventilation settings in intensive care units (ICUs) and improve patient outcomes. Specifically, machine learning algorithms can be trained on large datasets of patient information and mechanical ventilation settings. These algorithms can then predict patient responses to different ventilation strategies and suggest personalized ventilation settings for individual patients.ObjectiveIn this study, we aimed to design and evaluate an AI solution that could tailor an optimal ventilator strategy for each critically ill patient who requires mechanical ventilation.MethodsWe proposed a reinforcement learning-based AI solution using observational data from multiple ICUs in the United States. The primary outcome was hospital mortality. Secondary outcomes were the proportion of optimal oxygen saturation and the proportion of optimal mean arterial blood pressure. We trained our AI agent to recommend low, medium, and high levels of 3 ventilator settings-positive end-expiratory pressure, fraction of inspired oxygen, and ideal body weight-adjusted tidal volume-according to patients' health conditions. We defined a policy as rules guiding ventilator setting changes given specific clinical scenarios. Off-policy evaluation metrics were applied to evaluate the AI policy.ResultsWe studied 21,595 and 5105 patients' ICU stays from the e-Intensive Care Unit Collaborative Research (eICU) and Medical Information Mart for Intensive Care IV (MIMIC-IV) databases, respectively. Using the learned AI policy, we estimated the hospital mortality rate (eICU 12.1%, SD 3.1%; MIMIC-IV 29.1%, SD 0.9%), the proportion of optimal oxygen saturation (eICU 58.7%, SD 4.7%; MIMIC-IV 49%, SD 1%), and the proportion of optimal mean arterial blood pressure (eICU 31.1%, SD 4.5%; MIMIC-IV 41.2%, SD 1%). Based on multiple quantitative and qualitative evaluation metrics, our proposed AI solution outperformed observed clinical practice.ConclusionsOur study found that customizing ventilation settings for individual patients led to lower estimated hospital mortality rates compared to actual rates. This highlights the potential effectiveness of using reinforcement learning methodology to develop AI models that analyze complex clinical data for optimizing treatment parameters. Additionally, our findings suggest the integration of this model into a clinical decision support system for refining ventilation settings, supporting the need for prospective validation trials.

Project description:IntroductionPatient-ventilator asynchrony is common during the entire period of invasive mechanical ventilation (MV) and is associated with worse clinical outcomes. However, risk factors associated with asynchrony are not completely understood. The main objectives of this study are to estimate the incidence of asynchrony during invasive MV and its association with respiratory mechanics and other baseline patient characteristics.Methods and analysisWe designed a prospective cohort study of patients admitted to the intensive care unit (ICU) of a university hospital. Inclusion criteria are adult patients under invasive MV initiated for less than 72 hours, and with expectation of remaining under MV for more than 24 hours. Exclusion criteria are high flow bronchopleural fistula, inability to measure respiratory mechanics and previous tracheostomy. Baseline assessment includes clinical characteristics of patients at ICU admission, including severity of illness, reason for initiation of MV, and measurement of static mechanics of the respiratory system. We will capture ventilator waveforms during the entire MV period that will be analysed with dedicated software (Better Care, Barcelona, Spain), which automatically identifies several types of asynchrony and calculates the asynchrony index (AI). We will use a linear regression model to identify risk factors associated with AI. To assess the relationship between survival and AI we will use Kaplan-Meier curves, log rank tests and Cox regression. The calculated sample size is 103 patients. The statistical analysis will be performed by the software R Programming (www.R-project.org) and will be considered statistically significant if the p value is less than 0.05.Ethics and disseminationThe study was approved by the Ethics Committee of Instituto do Coração, School of Medicine, University of São Paulo, Brazil, and informed consent was waived due to the observational nature of the study. We aim to disseminate the study findings through peer-reviewed publications and national and international conference presentations.Trial registration numberNCT02687802; Pre-results.

Dataset Information

Robust, maintainable, emergency invasive mechanical ventilator.

Objective

Methods

Results

Conclusion

Publications

Robust, maintainable, emergency invasive mechanical ventilator.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets