Clinical and Risk Analytics Associations With Extubation Failure in Children Following Congenital Cardiac Surgery: A Multicenter Retrospective Cohort Study, 2017–2020
Bibliographic record
Abstract
OBJECTIVES: The use of risk analytics indices alongside clinical factors has potential to assist clinicians in identifying children at high risk for extubation failure (EF). We investigated the association of two physiologic risk analytics indices with EF in children receiving mechanical ventilation (MV) after cardiac surgery: the probability of inadequate oxygen delivery (ID o2 ) and inadequate ventilation of carbon dioxide index (IV co2 ). A secondary aim was to evaluate clinical factors associated with EF. DESIGN: Multicenter retrospective cohort study. SETTING: Eight international pediatric cardiac ICUs. PATIENTS: Children between 1 month and 12 years old receiving MV for greater than 48 hours following cardiac surgery between January 1, 2017, and December 31, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Nine hundred twenty-two children were analyzed with 79 (8.6%) having EF (defined as reintubation within 48 hr). In multivariable analysis of clinical variables, preoperative MV (adjusted odds ratio [aOR], 1.78; 95% CI, 1.08-2.96; p = 0.03), receiving inhaled nitric oxide (iNO) at extubation (aOR, 2.22; 95% CI, 1.13-4.35; p = 0.02), and duration of postoperative MV (aOR, 1.03; 95% CI, 1.00-1.06; p = 0.03) were independently associated with EF. Seven hundred ninety-two patients (86%) had pre-extubation ID o2 data, 602 (65%) had pre-extubation IV co2 data, and 600 (65%) had both pre-extubation ID o2 and IV co2 data available. In multivariable analysis including these risk analytics algorithms, patients with either ID o2 greater than or equal to 5 or IV co2 greater than or equal to 50 before extubation had higher odds of EF (aOR, 2.06; 95% CI, 1.08-3.94; p = 0.03). CONCLUSIONS: The addition of risk analytics algorithms evaluating the probability of inadequate systemic oxygen delivery or inadequate ventilation to clinical factors (duration of ventilation or iNO delivery at extubation) is useful in assessing the risk for EF in children recovering from cardiac surgery.
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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 | 0.002 |
| 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 | 0.000 | 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".