Optimizing neuromuscular blockade management for improving patient safety and peri-operative outcomes: a pilot phase of a quality improvement initiative
Bibliographic record
Abstract
INTRODUCTION: Although quantitative monitoring of neuromuscular blockade (NMB) is recommended, it is not routinely used in daily practice. The optimizing NMB management to improve patient safety and perioperative outcomes (OBISPO) quality improvement (QI) initiative intends to address this issue and change clinicians' behaviors. MATERIAL AND METHODS: A pilot phase of the prospective QI intervention was conducted. The primary objective was implement clinical practice change that emphasizes improving NMB monitoring in patients undergoing elective cardiac surgery who are eligible for fast-track extubation between February 2021 and December 2021. The secondary objective was to reduce the train-of-four ratio (TOFR) < 0.9 incidence before tracheal extubation to less than 20%. The intervention included educational sessions for teams. RESULTS: A total of 859 patients underwent elective cardiac surgery, 40% were eligible for fast-track extubation. From our cohort of fast-track cardiac cases, 69% had reported TOFR; 47% of them had residual paralysis (TOFR < 0.9) on arrival to PACU, 22% persisted with residual paralysis after extubation, and 27% were extubated without monitoring. The survey identified cognitive biases, knowledge gaps, unfamiliarity, and lack of trust in quantitative monitoring devices. Workflow disruptions imposed by COVID and changes in NMB monitoring devices have negatively affected our initiative. CONCLUSIONS: Our study showed that changes in clinician behavior are among the most challenging issues in perioperative medicine. Continuous teaching and QI initiatives, focused on quantitative NMB monitors and adequate reversal agent use, are mandatory to improve perioperative outcomes. Therefore, new proposals are required to promote changes in current practices.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".