ArticleJournal of clinical monitoring and computing2021
An effective pressure-flow characterization of respiratory asynchronies in mechanical ventilation.
Article in Journal of clinical monitoring and computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Artificial Intelligence for the Detection of Patient-Ventilator Asynchrony.Respiratory care · 2025Review
- Let's get in sync: current standing and future of AI-based detection of patient-ventilator asynchrony.Intensive care medicine experimental · 2025Review
- Detection and quantitative analysis of patient-ventilator interactions in ventilated infants by deep learning networks.Pediatric research · 2024Observational
- Automated evaluation of typical patient-ventilator asynchronies based on lung hysteretic responses.Biomedical engineering online · 2023Article
- Neural Network-Enabled Identification of Weak Inspiratory Efforts during Pressure Support Ventilation Using Ventilator Waveforms.Journal of personalized medicine · 2023Article
- Pressure-flow breath representation eases asynchrony identification in mechanically ventilated patients.Journal of clinical monitoring and computing · 2022Article
- What is new in respiratory monitoring?Journal of clinical monitoring and computing · 2022Review
- Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model.Biomedical engineering online · 2022Article
- Timing of inspiratory muscle activity detected from airway pressure and flow during pressure support ventilation: the waveform method.Critical care (London, England) · 2022Article
- Identifying Patient-Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning.Sensors (Basel, Switzerland) · 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
Abstract
Ineffective effort during expiration (IEE) occurs when there is a mismatch between the demand of a mechanically ventilated patient and the support delivered by a Mechanical ventilator during the expiration. This work presents a pressure-flow characterization for respiratory asynchronies and validates a machine-learning method, based on the presented characterization, to identify IEEs. 1500 breaths produced by 8 mechanically-ventilated patients were considered: 500 of them were included into the training set and the remaining 1000 into the test set. Each of them was evaluated by 3 experts and classified as either normal, artefact, or containing inspiratory, expiratory, or cycling-off asynchronies. A software implementing the proposed method was trained by using the experts' evaluations of the training set and used to identify IEEs in the test set. The outcomes were compared with a consensus of three expert evaluations. The software classified IEEs with sensitivity 0.904, specificity 0.995, accuracy 0.983, positive and negative predictive value 0.963 and 0.986, respectively. The Cohen's kappa is 0.983 (with 95% confidence interval (CI): [0.884, 0.962]). The pressure-flow characterization of respiratory cycles and the monitoring technique proposed in this work automatically identified IEEs in real-time in close agreement with the experts.
Indexed as
Identifiers
31993892What OpenQuestion holds
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.