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Record W2727247231 · doi:10.1213/ane.0000000000002228

Anesthetic Management of Narcolepsy Patients During Surgery: A Systematic Review

2017· review· en· W2727247231 on OpenAlexafffund
Sally Hu, Mandeep Singh, Jean Wong, Dennis Auckley, Shelley Hershner, Rahul Kakkar, Michael J. Thorpy, Frances Chung

Bibliographic record

VenueAnesthesia & Analgesia · 2017
Typereview
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsToronto Western HospitalUniversity Health NetworkWomen's College HospitalUniversity of Toronto
FundersUniversity Health Network
KeywordsNarcolepsyMedicinePerioperativeSleep paralysisMEDLINESystematic reviewCataplexyRandomized controlled trialAnesthesiologyAnesthesiaAnestheticSurgeryModafinilPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Narcolepsy is a rare sleep disorder characterized by excessive daytime sleepiness, sleep paralysis, and/or hypnagogic/hypnopompic hallucinations, and in some cases cataplexy. The response to anesthetic medications and possible interactions in narcolepsy patients is unclear in the perioperative period. In this systematic review, we aim to evaluate the current evidence on the perioperative outcomes and anesthetic considerations in narcolepsy patients. METHODS: Electronic literature search of Medline, Medline in-process, Embase, Cochrane Database of Systematic Reviews databases, international conference proceedings, and abstracts was conducted in November 2015 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guideline. A total of 3757 articles were screened using a 2-stage strategy (title-abstract followed by full text). We included case studies/series, cohort studies, and randomized controlled trials of narcolepsy patients undergoing surgical procedures under anesthesia or sedation. Preoperative narcolepsy symptoms and sleep study data, anesthetic technique, and perioperative complications were extracted. Screening of articles, data extraction, and compilation were conducted by 2 independent reviewers and any conflict was resolved by the senior author. RESULTS: A total of 19 studies including 16 case reports and 3 case series were included and evaluated. The majority of these patients received general anesthesia, whereas a small percentage of patients received regional anesthesia. Reported complications of narcolepsy patients undergoing surgeries were mainly related to autonomic dysregulation, or worsening of narcolepsy symptoms intra/postoperatively. Narcolepsy symptoms worsened only in those patient populations where the preoperative medications were either discontinued or reduced (mainly in obstetric patients). In narcolepsy patients, use of depth of anesthesia monitoring and total intravenous technique may have some advantage in terms of safety profile. Several patients undergoing neurosurgery involving the hypothalamus or third or four ventricles developed new-onset narcolepsy. CONCLUSIONS: We found a paucity of prospective clinical trials in this patient population, as most of the studies were case reports or observational studies. Continuation of preoperative medications, depth of anesthesia monitoring, use of multimodal analgesia with short-acting agents and regional anesthesia techniques were associated with favorable outcomes. Obstetric patients may be at greater risk for worsening narcolepsy symptoms, possibly related to a reduction or discontinuation of medications. For neurosurgical procedures involving the hypothalamus or third and fourth ventricle, postoperative considerations should include monitoring for symptoms of narcolepsy. Future studies are needed to better define perioperative risks associated with anesthesia and surgery in this population of patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.117
GPT teacher head0.367
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2017
Admission routes2
Has abstractyes

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