Patient–ventilator asynchrony during conventional mechanical ventilation in children
Bibliographic record
Abstract
BACKGROUND: We aimed (1) to describe the characteristics of patient-ventilator asynchrony in a population of critically ill children, (2) to describe the risk factors associated with patient-ventilator asynchrony, and (3) to evaluate the association between patient-ventilator asynchrony and ventilator-free days at day 28. METHODS: In this single-center prospective study, consecutive children admitted to the PICU and mechanically ventilated for at least 24 h were included. Patient-ventilator asynchrony was analyzed by comparing the ventilator pressure curve and the electrical activity of the diaphragm (Edi) signal with (1) a manual analysis and (2) using a standardized fully automated method. RESULTS: Fifty-two patients (median age 6 months) were included in the analysis. Eighteen patients had a very low ventilatory drive (i.e., peak Edi < 2 µV on average), which prevented the calculation of patient-ventilator asynchrony. Children spent 27% (interquartile 22-39%) of the time in conflict with the ventilator. Cycling-off errors and trigger delays contributed to most of this asynchronous time. The automatic algorithm provided a NeuroSync index of 45%, confirming the high prevalence of asynchrony. No association between the severity of asynchrony and ventilator-free days at day 28 or any other clinical secondary outcomes was observed, but the proportion of children with good synchrony was very low. CONCLUSION: Patient-ventilator interaction is poor in children supported by conventional ventilation, with a high frequency of depressed ventilatory drive and a large proportion of time spent in asynchrony. The clinical benefit of strategies to improve patient-ventilator interactions should be evaluated in pediatric critical care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".