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Record W1798862520 · doi:10.1002/lary.23488

Does perioperative sleep disruption impact pain perception?

2012· review· en· W1798862520 on OpenAlexaboutno aff
Kathleen Yaremchuk, Timothy Roehrs

Bibliographic record

VenueThe Laryngoscope · 2012
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnalgesicAnesthesiaPain tolerancePerioperativeSleep (system call)ActigraphyPhysical therapyThreshold of painInternal medicineCircadian rhythm

Abstract

fetched live from OpenAlex

The typical inpatient hospital experience results in multiple intrusions by hospital personnel, noise, and inadequate treatment of pain that prevents the necessary amount of sleep for hospitalized patients. Sleep disruption results in increased pain perception and lowered pain thresholds. In 1999, the Veterans Health Administration recognized the need to adequately treat pain and began using pain as the fifth vital sign to be recorded at every clinical encounter. The bidirectional relation of sleep disturbance and pain perception has been well studied in normal individuals and in patients with acute pain after surgery. Nine healthy males were randomly assigned in a double-blinded crossover study to undergo total sleep deprivation (TSD), rapid eye movement (REM) sleep interruption, or slow wave sleep (SWS) interruption. Tolerance levels to thermal and mechanical pain were then measured. Relative to baseline pain levels, TSD decreased mechanical pain thresholds by 8%. REM and SWS interruption tended to decrease mechanical pain thresholds. However, on the recovery sleep day after SWS interruption there was a significant increase in mechanical pain tolerance of 15%. The analgesic effect of SWS recovery was greater than the analgesia provided by level I (World Health Organization) analgesic compounds in mechanical pain experiments in healthy volunteers. Postoperative pain would be classified as mechanical in nature. Thermal pain thresholds were not impacted by TSD, REM, or SWS interruption.1 Poor sleep the night before surgery has been shown to cause increased pain perception postoperatively. Twenty-four patients scheduled for routine breast conservation surgeries for diagnosis or treatment of cancer wore actigraphy devices to provide objective, validated measures of sleep disruption and duration (low sleep efficiency). Lower sleep efficiency was a significant predictor of greater postoperative pain severity controlling for age, race, and perioperative analgesics.2 Sleep efficiency was not significantly related to measures of depressed mood, emotional upset, or relation assessed on the morning of surgery. Pain was assessed using the Brief Pain Inventory, a self-reported measure based on a 10-point scale. Scores could range from 0 (no pain) to 10 (pain as bad as you can imagine). Patients with lowest sleep efficiency had clinically higher levels of pain (>2 points) compared to patients with the highest sleep efficiency. Several studies have assessed sleep in the setting of acute pain postoperatively using polysomnography (PSG). Patients who underwent major abdominal surgery, herniorrhaphy, or minor unidentified surgery were evaluated with PSG for 1 to 6 nights postoperatively. Total sleep time was found to be reduced for 1 or 2 nights, with evidence of sleep fragmentation, frequent arousals, and awakenings. Regardless of type of surgery, SWS was decreased for up to 4 nights, and an absence of REM sleep was found for 2 nights postoperatively.3 A recent descriptional, correlational study using the Pittsburgh Sleep Quality Index and the McGill Pain Questionnaire-Short Form evaluated 75 orthopedic patients undergoing a major surgical procedure (total hip/knee arthroplasty, vertebra reconstruction–scoliosis/lordosis/kyphosis, bone tumor resection, and hemiarthroplasty). Results demonstrated a statistically significant correlation between the severity of the pain the patient experienced and sleep quality (P ≤ .05). Factors that were identified by patients in the postoperative period as disturbing sleep were pain (83%), noise (41%), hospital staff entering/leaving the room (24%), the hospital environment (20%), and room temperatures (15%).4 An important issue demonstrated in prospective studies of pain is the bidirectionality of the pain and sleep relation. Poor sleep has been shown to decrease pain thresholds, and increased pain causes further sleep disturbances through sleep fragmentation and decreased REM, SWS, and total sleep time. To be effective, treatment must be directed at alleviation of both the pain and sleep disturbance. Pain has been estimated to be undertreated in up to 80% of patients in some settings.5 Despite recognition of pain as the fifth vital sign by the Veterans Administration in 2006, the quality of pain management did not improve by providers. Sleep disruption, for any reason, results in a decrease in pain tolerance. Evidence shows that perioperative sleep disruption due to pain and the hospital experience results in a lowered threshold of pain. Hospital-related factors such as noise, interruptions by medical personnel, room temperature, and light have been implicated as causes of sleep disruptions. Based on previous research, perioperative sleep disruption can negatively impact pain perception. Pain can have consequences that result in side effects such as an increased risk of complications, delayed convalescence, poor physical and mental performance, and lower patient satisfaction. Changing rounding patterns to accommodate uninterrupted sleep and including discussion of quality of sleep in addition to a systematically collected pain score are important in reducing the patient's pain burden. The studies cited represent different levels of evidence. One study is level 1b, and the others are level 3b or 4.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.036
GPT teacher head0.369
Teacher spread0.333 · 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 designNot applicable
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".

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Citations4
Published2012
Admission routes1
Has abstractyes

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