Mapping the current evidence on the anesthetic management of adult patients with neuromuscular disorders—a scoping review
Bibliographic record
Abstract
PURPOSE: Patients with neuromuscular disorders (NMDs) are at increased risk of perioperative complications. The objective of this scoping review was to examine emerging evidence from published studies, case reports, and review articles on anesthetic management of patients with NMDs, following the methodological frame for scoping reviews. SOURCES: We searched PubMed and EMBASE for articles published between 1 January 2000 and 14 July 2021. PRINCIPAL FINDINGS: Three prospective and 21 retrospective studies on altered pharmacokinetics and pharmacodynamics of neuromuscular blocking agents (NMBA) in NMD patients were included. Furthermore, 168 case reports/series reporting 212 anesthetics in 197 patients were included. These studies showed that preanesthetic neuromuscular monitoring can be used for precise NMBA dosing in myasthenia gravis patients. Sugammadex was associated with fewer postoperative myasthenic crises. Perioperative complications were not associated with specific anesthetic agents. Case reports/series showed that in 32% (67/212) of anesthetics, at least one complication was reported. Unexpected intensive care unit admission was a frequently reported complication. Patients with a complicated disease course may have had a higher use of succinylcholine (unadjusted relative risk, 0.13; 95% confidence interval [CI], 0.20 to 0.86) and volatile anesthetics (adjusted odds ratio [OR], 0.38; 95% CI, 0.20 to 0.73; P = 0.004). CONCLUSION: Evidence on the anesthetic management and perioperative complications of patients with NMDs is mainly based on small retrospective studies and case reports. Further clinical trials or large retrospective studies are required to investigate the choice of safe anesthetic agents. Main areas of interest are the potential benefits of neuromuscular monitoring and sugammadex and the risks possibly associated with volatile anesthetics and succinylcholine.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".