Trial reportAnnals of biomedical engineering2019
Predictive Virtual Patient Modelling of Mechanical Ventilation: Impact of Recruitment Function.
Trial report in Annals of biomedical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed, 50 citations in OpenAlex.
- Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.Critical care (London, England) · 2026Review
- Computer model-based injury prediction and evaluation of lung function in mice with acute and ventilator-induced lung injuries.American journal of physiology. Lung cellular and molecular physiology · 2025Article
- 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
- Quantifying neonatal patient effort using non-invasive model-based methods.Medical & biological engineering & computing · 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
- High fidelity blood flow in a patient-specific arteriovenous fistula.Scientific reports · 2021Article
- Authors' Response to Drs. Ece Salihoglu and Ziya Salihoglu's Letter to the Editor.Annals of biomedical engineering · 2020Article
- A Nonlinear Hysteretic Model for Automated Prediction of Lung Mechanics during Mechanical Ventilation.IFAC-PapersOnLine · 2020Article
- Three Alveolar Phenotypes Govern Lung Function in Murine Ventilator-Induced Lung Injury.Frontiers in physiology · 2020Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
Mechanical ventilation is a life-support therapy for intensive care patients suffering from respiratory failure. To reduce the current rate of ventilator-induced lung injury requires ventilator settings that are patient-, time-, and disease-specific. A common lung protective strategy is to optimise the level of positive end-expiratory pressure (PEEP) through a recruitment manoeuvre to prevent alveolar collapse at the end of expiration and to improve gas exchange through recruitment of additional alveoli. However, this process can subject parts of the lung to excessively high pressures or volumes. This research significantly extends and more robustly validates a previously developed pulmonary mechanics model to predict lung mechanics throughout recruitment manoeuvres. In particular, the process of recruitment is more thoroughly investigated and the impact of the inclusion of expiratory data when estimating peak inspiratory pressure is assessed. Data from the McREM trial and CURE pilot trial were used to test model predictive capability and assumptions. For PEEP changes of up to and including 14 cmH
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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.