ArticleJournal of clinical monitoring and computing2014
A knowledge- and model-based system for automated weaning from mechanical ventilation: technical description and first clinical application.
Article in Journal of clinical monitoring and computing, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Artificial intelligence in mechanical ventilation: a narrative review of clinical applications and research gaps.Journal of thoracic disease · 2026Review
- Role of artificial intelligence in perioperative monitoring in anaesthesia.Indian journal of anaesthesia · 2024Article
- Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network.Heliyon · 2023Article
- Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery.Anesthesiology clinics · 2021Review
- Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations.Anesthesiology · 2020Review
- Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform.International journal of biological sciences · 2018Article
- Journal of clinical monitoring and computing 2014 end of year summary: respiration.Journal of clinical monitoring and computing · 2015Review
- The European Society for Computing and Technology in Anaesthesia and Intensive Care (ESCTAIC): a special issue of full papers (Erlangen meeting 2011) and conference abstracts (Timisoara, meeting 2014).Journal of clinical monitoring and computing · 2014Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To describe the principles and the first clinical application of a novel prototype automated weaning system called Evita Weaning System (EWS). EWS allows an automated control of all ventilator settings in pressure controlled and pressure support mode with the aim of decreasing the respiratory load of mechanical ventilation. Respiratory load takes inspired fraction of oxygen, positive end-expiratory pressure, pressure amplitude and spontaneous breathing activity into account. Spontaneous breathing activity is assessed by the number of controlled breaths needed to maintain a predefined respiratory rate. EWS was implemented as a knowledge- and model-based system that autonomously and remotely controlled a mechanical ventilator (Evita 4, Dräger Medical, Lübeck, Germany). In a selected case study (n = 19 patients), ventilator settings chosen by the responsible physician were compared with the settings 10 min after the start of EWS and at the end of the study session. Neither unsafe ventilator settings nor failure of the system occurred. All patients were successfully transferred from controlled ventilation to assisted spontaneous breathing in a mean time of 37 ± 17 min (± SD). Early settings applied by the EWS did not significantly differ from the initial settings, except for the fraction of oxygen in inspired gas. During the later course, EWS significantly modified most of the ventilator settings and reduced the imposed respiratory load. A novel prototype automated weaning system was successfully developed. The first clinical application of EWS revealed that its operation was stable, safe ventilator settings were defined and the respiratory load of mechanical ventilation was decreased.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.