High-flow nasal cannula oxygenation reduces postoperative hypoxemia in morbidly obese patients: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Postoperative pulmonary complications (PPCs) are common in high-risk surgical patients. Postoperative ventilatory management may improve their outcome. Supplemental oxygen through a high-flow nasal cannula (HFNC) has become an alternative to classical oxygenation techniques, although the results published for postoperative patients are contradictory. We examined the efficacy of HFNC in postoperative morbidly obese patients who were ventilated intraoperatively with an open-lung approach (OLA). METHODS: We performed an open, two-arm, randomized controlled trial in 64 patients undergoing bariatric surgery (N.=32 in each arm) from May to November 2017 at the Hospital Clínico of Valencia. Patients were randomly assigned to receive HFNC oxygen therapy at the time of extubation or to receive conventional oxygen therapy, both applied during the first three postoperative hours. Intraoperatively, a recruitment maneuver and individualized positive end-expiratory pressure was applied in all patients. The primary outcome was postoperative hypoxemia. RESULTS: All patients were included in the final analysis. There were no significant differences between the baseline characteristics. Postoperative hypoxemia was less frequent in the HFNC group compared to those who received standard care (28.6% vs. 80.0%, relative risk [RR]: 0.35; 95%CI: 0.150-0.849, P=0.009). Prevalence of atelectasis was lower in the HFNC group (31% vs. 77%, RR: 0.39; 95%CI: 0.166-0.925, P=0.013). No severe PPCs were reported in any patient. CONCLUSIONS: Early application of HFNC in the operating room before extubation and during the immediate postoperative period decreases postoperative hypoxemia in obese patients after bariatric surgery who were intraoperatively ventilated using an OLA approach.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".