Decreasing the Adverse Effects of Endotracheal Suctioning During Mechanical Ventilation by Changing Practice
Bibliographic record
Abstract
BACKGROUND: Little is known about the incidence of and risk factors for adverse effects from endotracheal suctioning. We studied the incidence and risk factors, and evaluated the effect of suctioning practice guidelines. METHODS: During a 3-month period, in 79 mechanically ventilated subjects, we recorded the adverse effects in 4,506 suctioning procedures. Then practice guidelines were implemented, and 1 year later, during another 3-month period, in 68 subjects, we recorded the adverse effects in 4,994 suctioning procedures. RESULTS: In the first period, adverse effects occurred frequently: oxygen desaturation in 46.8% of subjects and 6.5% of suctionings, hemorrhagic secretions in 31.6% of subjects and 4% of suctionings, blood pressure change in 24.1% of subjects and 1.6% of suctionings, and heart rate change in 10.1% of subjects and 1.1% of suctionings. After guidelines implementation, all complications, both separately and all together, were reduced. The incidence of all complications together decreased from 59.5% to 42.6% of subjects, and from 12.4% to 4.9% of procedures (both P < .05). PEEP > 5 cm H2O was an independent risk factor for oxygen desaturation. Receiving > 6 suctionings per day was a risk factor for desaturation and hemorrhagic secretions. The use of guidelines was independently associated with fewer complications. CONCLUSIONS: Endotracheal suctioning frequently induces adverse effects. Technique, suctioning frequency, and higher PEEP are risk factors for complications. Their incidence can be reduced by the implementation of suctioning guidelines.
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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 | 0.019 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".