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Enregistrement W1798862520 · doi:10.1002/lary.23488

Does perioperative sleep disruption impact pain perception?

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

Notice bibliographique

RevueThe Laryngoscope · 2012
Typereview
Langueen
DomaineMedicine
ThématiquePediatric Pain Management Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineAnalgesicAnesthesiaPain tolerancePerioperativeSleep (system call)ActigraphyPhysical therapyThreshold of painInternal medicineCircadian rhythm

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,036
Tête enseignante GPT0,369
Écart entre enseignants0,333 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2012
Routes d'admission1
Résumé présentoui

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