ReviewAnnual reviews in control2019
Optimising mechanical ventilation through model-based methods and automation.
Review in Annual reviews in control, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- VENTILA2: A Fuzzy Logic-Based Simulation and Decision-Support Framework for Pressure-Controlled Ventilation-A Proof of Concept.Healthcare (Basel, Switzerland) · 2026Article
- Monitoring of invasive assisted mechanical ventilation: a good clinical practice document by the Italian Society of Anesthesia, Analgesia, Resuscitation, and Intensive Care (SIAARTI).Journal of anesthesia, analgesia and critical care · 2025Review
- Tracking control of air flow based on a fractional-order model of the lung impedance.Scientific reports · 2025Article
- Respiratory pressure and flow data collection device providing a framework for closed-loop mechanical ventilation.HardwareX · 2025Article
- Safe automation of interfering medical treatments via control barrier functions and reachability analysis: a fluid resuscitation-sedation-vasopressor infusion case study.Journal of clinical monitoring and computing · 2025Article
- Vertebral Column Pathology Diagnosis Using Ensemble Strategies Based on Supervised Machine Learning Techniques.Healthcare (Basel, Switzerland) · 2024Article
- Obstructive respiratory disease simulation device.HardwareX · 2024Article
- Article
- Respiratory pressure and split flow data collection device with rapid occlusion attachment.HardwareX · 2023Article
- Automated evaluation of typical patient-ventilator asynchronies based on lung hysteretic responses.Biomedical engineering online · 2023Article
- Airflow and dynamic circumference of abdomen and thorax for adults at varied continuous positive airway pressure ventilation settings and breath rates.Scientific data · 2023Article
- Hypertonic treatment of acute respiratory distress syndrome.Frontiers in bioengineering and biotechnology · 2023Article
- Article
- Estimating the incidence of spontaneous breathing effort of mechanically ventilated patients using a non-linear auto regressive (NARX) model.Computer methods and programs in biomedicine · 2022Article
- Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model.Biomedical engineering online · 2022Article
- Stochastic integrated model-based protocol for volume-controlled ventilation setting.Biomedical engineering online · 2022Article
- Model-based patient matching for in-parallel pressure-controlled ventilation.Biomedical engineering online · 2022Article
- Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients.Annals of biomedical engineering · 2021Observational
- Model-based Patient Matching for in-parallel Multiplexing Mechanical Ventilation Support.IFAC-PapersOnLine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.
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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.