Ventilation strategies and risk factors for intraoperative respiratory critical events and postoperative pulmonary complications in neonates and small infants: a secondary analysis of the NECTARINE cohort☆
Bibliographic record
Abstract
BACKGROUND: Optimal ventilation strategies and use of neuromuscular blocking agents (NMBAs) in neonates and small infants undergoing anaesthesia remain unclear. We examined the association of perioperative ventilation strategies and administration of NMBAs on respiratory adverse events in the NEonate-Children sTudy of Anaesthesia pRactice IN Europe (NECTARINE) cohort. METHODS: We performed a secondary analysis of NECTARINE, which included infants up to 60 weeks' postmenstrual age undergoing anaesthesia for surgical or diagnostic procedures. The primary endpoint was the association between ventilation mode and intraoperative respiratory adverse events. Secondary endpoints were use of NMBA, and 30-day postoperative pulmonary complications (PPCs). RESULTS: The dataset comprised 5609 patients undergoing 6542 procedures. Pressure-controlled ventilation was the primary ventilation modality, accounting for 52.4% (n=3428) of cases. The incidence of intraoperative respiratory critical events was 20.7% (95% confidence interval [CI] 19.7-21.7%), while PPCs were observed in 17% of cases (95% CI 16.0-18.1%). Preanaesthesia respiratory conditions and NMBA use after tracheal intubation were associated with higher incidence of PPCs. Of the children receiving NMBAs, reversal was reported in 29.8%. The absence of reversal was associated with a higher incidence of PPCs, with a relative risk of 1.50 (95% CI 1.17-1.93). Conversely, NMBA reversal was associated with a reduced relative risk of 0.43 (95% CI 0.26-0.70). CONCLUSIONS: Regardless of ventilation strategy used, mechanical ventilation and baseline respiratory conditions were risk factors for a greater incidence of adverse respiratory events and PPCs. Reversal of NMBAs before tracheal extubation was significantly associated with reduced PPCs in neonates and should be routine clinical practice. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov (NCT02350348).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".