Extubation Failure in Neonates Following Congenital Cardiac Surgery: Multicenter Retrospective Cohort, 2017–2020
Bibliographic record
Abstract
OBJECTIVES: Extubation failure (EF) in neonates recovering from congenital cardiac surgery is associated with morbidity and mortality. Adding continuous physiologic monitoring data and risk analytics algorithms to clinical factors has the potential to assist clinicians in identifying those neonates at high risk for EF. We aimed to evaluate the association of two physiologic risk analytics algorithms evaluating the probability of inadequate delivery of oxygen index (ID o2 ) and inadequate ventilation of carbon dioxide index (IV co2 ) with EF in neonates receiving mechanical ventilation (MV) after cardiac surgery. A secondary aim was to evaluate the clinical factors associated with EF. DESIGN: Multicenter retrospective cohort study. SETTING: Eight international pediatric cardiac ICUs. PATIENTS: Neonates (age < 1 mo at the time of surgery) receiving MV for longer than 48 hours following cardiac surgery between January 1, 2017, and December 31, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Data from 736 neonates were analyzed with 102 (13.9%) having EF (defined as reintubation within 48 hr of extubation). In multivariable analysis (odds ratio [OR] and 95% CI), preoperative respiratory support (OR, 1.72 [95% CI, 1.11-2.67]) was associated with greater odds of EF. In all, 611 neonates had pre-extubation ID o2 data and 478 neonates had both pre-extubation ID o2 and IV co2 data. In multivariable analysis of patients with both pre-extubation ID o2 and IV co2 data, single ventricle anatomy (OR, 2.50 [95% CI, 1.27-4.92]) and high ID o2 (≥ 25) or high IV co2 (≥ 50) in the 2 hours preceding extubation (OR, 1.77 [95% CI, 1.01-3.12]) were associated with greater odds of EF. CONCLUSIONS: In this 2017-2020 cohort, EF is high in post-cardiac surgery neonates receiving at least 48 hours of MV. The ID o2 and IV co2 algorithms may be useful in assessing risk of EF in such neonates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".