Interventions guided by analysis of quality indicators decrease the frequency of laryngospasm during pediatric anesthesia
Bibliographic record
Abstract
INTRODUCTION: Clinical outcomes in pediatric anesthesia have improved significantly over the last 20-30 years but unexpected laryngospasm that is difficult to treat can still result in patient morbidity, increased postoperative medical management and unnecessary hospital admission. The incidence of laryngospasm in pediatric anesthesia is difficult to determine with incidences from 0.9% to as high as 14% quoted in the literature. Clinical experience in our institution suggests that laryngospasm is one of the more frequent unanticipated complications that occur under general anesthesia. Therefore, we applied quality improvement (QI) methodology to: (i) identify the etiology and contributing factors that lead to unanticipated incidents during pediatric anesthesia care; and (ii) decrease the incidence of laryngospasm during pediatric anesthesia care by focusing on awareness, preparedness, education and knowledge translation. MATERIALS & METHODS: We conducted a 30-month improvement project. Twelve months of baseline data describing unanticipated events during pediatric anesthesia care were collected prospectively in a single institution. Data were analyzed to identify leading causes of these unanticipated events and to identify key drivers to improve overall quality of care. Interventions focused on raising awareness of the impact of laryngospasm on quality of patient care, knowledge dissemination and the creation of a knowledge translation tool to encourage future early learning. The primary objective was to decrease the incidence of unanticipated calls for help due to laryngospasm by 50% over a 12-month period. RESULTS: During the 12-month baseline data period, laryngospasm was responsible for 33 instances (50%) of the 65 'calls for help' identified. The incidence of laryngospasm for which help was sought was 0.25% of all anesthetics performed during the baseline data period. After the introduction of our interventions, 16 (24%) of the 68 'calls for help' over the subsequent 16 months were attributed to laryngospasm. The incidence of laryngospasm for which help was sought during the second time period was 0.09% of all anesthetics performed. CONCLUSIONS: We applied QI methodology to identify potential improvements in the quality of anesthesia care we deliver to our patients. By designing a number of key drivers and interventions specifically focused on laryngospasm, we decreased the incidence of unanticipated calls for help due to laryngospasm by 50% and maintained this improvement to clinical care across a 12-month period.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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 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".