ArticleData in brief2025
Dataset of bag-valve-mask ventilation parameters in patients with out-of-hospital cardiac arrest cared for by basic life support teams.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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Who cites it
2 citing papers in PubMed.
- Accuracy of ventilation feedback devices compared with a reference standard during asynchronous ventilation with continuous chest compressions in a simulated cardiac arrest: a prospective non-randomized cross-over study.Resuscitation plus · 2026Article
- Real-world adoption and use of a ventilation feedback device during out-of-hospital cardiac arrest: a population-based observational study from a French prehospital system.Resuscitation plus · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This dataset was created as part of a study investigating the cardiopulmonary resuscitation (CPR) phase during out-of-hospital cardiac arrest (OHCA) interventions managed by basic life support (BLS) teams. Historically, the confined use of real-time monitoring of ventilatory parameters has limited the evaluation of BLS manual ventilation practices. The dataset includes ventilatory parameters recorded using a dedicated device connected to the bag-valve-mask (BVM) during OHCA treatment. Collected data comprise insufflated and tidal volumes (Vi and Vt), inspiratory and expiratory times (Ti and Te), leakage volumes and ratios, and intervals between ventilations. These parameters, saved in .csv format, were securely transferred for processing. Python scripts were utilized to ensure consistency, reduce noise, and facilitate further analysis. The dataset allows the identification of the first and second ventilations in each 30:2 CPR cycle. Distinguishing between ventilations, chest compressions, and noise remains challenging due to field conditions that may exceed the device's guidelines. Nevertheless, the data offer valuable insights into CPR practices, enabling researchers to monitor ventilations, assess adherence to guidelines, and examine the efficiency of ventilation techniques during resuscitation.
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Registered trials
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