PP157 Topic: AS15–Lung: Respiratory Support/Acute Respiratory Failure/Other: NON-INVASIVE VENTILATION IN PICU: A PRELIMINARY STUDY OF FACTORS ASSOCIATED WITH AIR LEAKS
Bibliographic record
Abstract
Aims & Objectives: Air leaks are omnipresent during non-invasive ventilation (NIV), a commonly used treatment in pediatric intensive care units (PICU). In adult population, an important amount of leaks is linked to NIV failure, but studies of the effect of these leaks in pediatric population remain sparse. Our aim was to describe elements associated with greater air leaks in PICU setting. Methods: Patients < 18 y.o admitted to CHU Sainte-Justine PICU between 2018-09-01 and 2023-09-01 and treated with NIV were included. SQL and R languages helped extract and analyze the continuous high-resolution data from the first 6 hours of each NIV episode. Associations between air leaks and different variables were explored through multivariate analysis using linear regressions, random forests and XGBoost. Importance of each variable was presented using SHapley Additive exPlanations (SHAP). Results: 1473 admissions were included, of which 41% female patients. The facial mask, the most popular interface, was used in 89% of episodes. The cohort’s median air leak was 74% (IQR 64%-83%). 685 admissions (47%) corresponded to patients aged ≤ 6 months, 331 (22%) were > 6 months & ≤ 2 y.o, and 457 (31%) were > 2 y.o. The median air leak for these groups was 80% (IQR 72%-86%), 72% (IQR 60%-82%) and 66% (IQR 50%-77%), respectively.Conclusions: A younger age may be associated with a larger median air leak during NIV according to our preliminary results. Additional associations, such as measured ventilation pressures and interface, as well as the impact of ventilatory leak on hypoxemia will be identified through further analysis. Keywords: non-invasive ventilation, air leaks, Children, interfaces, big data
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".