Risk Factors of Extubation Failure in Intubated Preterm Infants at a Tertiary Care Hospital in Oman
Bibliographic record
Abstract
Objectives: To determine extubation failure (EF) rate among intubated preterm infants (<37 weeks gestational age [GA]) admitted to a tertiary care neonatal intensive care unit (NICU) in Oman and identify the risk factors associated with EF. Methods: Charts of all intubated preterm infants (<37 weeks GA) from January 2013 to December 2017 were retrospectively reviewed. EF was defined as reintubation within 7 days of planned extubation. Demographics, ventilation parameters, blood gas values and other possible risk factors of EF were collected. Statistical analysis included comparisons between EF and extubation success (ES) groups, and binary logistic regression analysis. Results: A total of 190 preterm infants were intubated during the study period, with 140 eligible for analysis. N=106 were successfully extubated; 34 (24.3%) failed extubation. GA <28 weeks (p=0.029), lower 1-minute APGAR score (p=0.023) and patent ductus arteriosus diagnosis (PDA) (p=0.018) were significantly associated with EF. After the multivariate analysis, only GA <28 weeks predicted EF with adjusted odds ratio (95% confidence interval) of 2.62 (1.17 – 6.15). Conclusions: EF rate in preterm infants admitted at our NICU in Oman, was within international rates. GA <28 weeks was the only predictor of extubation failure identified. Neonatal practitioners need to seriously consider extreme prematurity in extubation process and consider implementing strategies to decrease extubation failure in this group of fragile infants. Keywords: Premature Infants; Neonate; Airway Extubation; Extubation Failure, Risk Factors.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".