Dataset Information

Pre-clinical validation of a turbine-based ventilator for invasive ventilation-The ACUTE-19 ventilator.

ABSTRACT:

Background

The Severe Acute Respiratory Syndrome (SARS)-Coronavirus 2 (CoV-2) pandemic pressure on healthcare systems can exhaust ventilator resources, especially where resources are restricted. Our objective was a rapid preclinical evaluation of a newly developed turbine-based ventilator, named the ACUTE-19, for invasive ventilation.

Methods

Validation consisted of (a) testing tidal volume (V_T) delivery in 11 simulated models, with various resistances and compliances; (b) comparison with a commercial ventilator (VIVO-50) adapting the United Kingdom Medicines and Healthcare products Regulatory Agency-recommendations for rapidly manufactured ventilators; and (c) in vivo testing in a sheep before and after inducing acute respiratory distress syndrome (ARDS) by saline lavage.

Results

Differences in V_T in the simulated models were marginally different (largest difference 33ml [95%-confidence interval (CI) 31-36]; P<.001ml). Plateau pressure (P_plat) was not different (-0.3cmH₂O [95%-CI -0.9 to 0.3]; P=.409), and positive end-expiratory pressure (PEEP) was marginally different (0.3 cmH₂O [95%-CI 0.2 to 0.3]; P<.001) between the ACUTE-19 and the commercial ventilator. Bland-Altman analyses showed good agreement (mean bias, -0.29, [limits of agreement, 0.82 to -1.42], and mean bias 0.56 [limits of agreement, 1.94 to -0.81], at a P_plat of 15 and 30cmH₂O, respectively). The ACUTE-19 achieved optimal oxygenation and ventilation before and after ARDS induction.

Conclusions

The ACUTE-19 performed accurately in simulated and animal models yielding a comparable performance with a VIVO-50 commercial device. The acute 19 can provide the basis for the development of a future affordable commercial ventilator.

SUBMITTER: Alonso-Inigo JM

PROVIDER: S-EPMC9639442 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Pre-clinical validation of a turbine-based ventilator for invasive ventilation-The ACUTE-19 ventilator.

Alonso-Iñigo J M JM Mazzinari G G Casañ-Pallardó M M Redondo-García J I JI Viscasillas-Monteagudo J J Gutierrez-Bautista A A Ramirez-Faz J J Alonso-Pérez P P Díaz-Lobato S S Neto A S AS Diaz-Cambronero O O Argente-Navarro P P Gama de Abreu M M Pelosi P P Schultz M J MJ

Revista espanola de anestesiologia y reanimacion 20221013 9

<h4>Background</h4>The Severe Acute Respiratory Syndrome (SARS)-Coronavirus 2 (CoV-2) pandemic pressure on healthcare systems can exhaust ventilator resources, especially where resources are restricted. Our objective was a rapid preclinical evaluation of a newly developed turbine-based ventilator, named the ACUTE-19, for invasive ventilation.<h4>Methods</h4>Validation consisted of (a) testing tidal volume (V<sub>T</sub>) delivery in 11 simulated models, with various resistances and compliances; ...[more]

PMID: 36244956

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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.

Dataset Information

Pre-clinical validation of a turbine-based ventilator for invasive ventilation-The ACUTE-19 ventilator.

Background

Methods

Results

Conclusions

Publications

Pre-clinical validation of a turbine-based ventilator for invasive ventilation-The ACUTE-19 ventilator.

Similar Datasets

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