High-frequency oscillatory ventilation in pediatric respiratory failure: A multicenter experience
Bibliographic record
Abstract
OBJECTIVE: The use of high-frequency oscillatory ventilation (HFOV) has increased dramatically in the management of respiratory failure in pediatric patients. We surveyed ten pediatric centers that frequently use high-frequency oscillation to describe current clinical practice and to examine factors related to improved outcomes. DESIGN: Retrospective, observational questionnaire study. SETTING: Ten tertiary care pediatric intensive care units. PATIENTS: Two hundred ninety patients managed with HFOV between January 1997 and June 1998. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patients were classified according to presence or absence of preexisting lung disease, symptomatic respiratory syncytial virus infection, or presence of cyanotic heart disease or residual right-to-left intracardiac shunt. In addition, patients for whom HFOV acutely failed were analyzed separately. Those patients with preexisting lung disease were significantly smaller, had a significantly higher incidence of pulmonary infection as the triggering etiology, and had a significantly greater duration of conventional ventilation before institution of HFOV compared with patients without preexisting lung disease. Stepwise logistic regression was used to predict mortality and the occurrence of chronic lung disease in survivors. In patients without preexisting lung disease, the model predicted a 70% probability of death when the oxygenation index (OI) after 24 hrs was 28 in the immunocompromised patients and 64 in the patients without immunocompromise. In the immunocompromised patients, the model predicted a 90% probability of death when the OI after 24 hrs was 58. In survivors without preexisting lung disease, the model predicted a 70% probability of developing chronic lung disease when the OI at 24 hrs was 31 in the patients with sepsis syndrome and 50 in the patients without sepsis syndrome. In the patients with sepsis syndrome, the model predicted a 90% probability of developing chronic lung disease when the OI at 24 hrs was 45. CONCLUSIONS: Given the number of centers involved and the size of the database, we feel that our results broadly reflect current practice in the use of HFOV in pediatric patients. These results may help in deciding which patients are most likely to benefit from aggressive intervention by using extracorporeal techniques and may help identify high-risk populations appropriate for prospective study of innovative modes of supporting gas exchange (e.g., partial liquid breathing or intratracheal pulmonary ventilation).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".