Predicting Acute Pain After Surgery: A Multivariate Analysis.


Journal

Annals of surgery
ISSN: 1528-1140
Titre abrégé: Ann Surg
Pays: United States
ID NLM: 0372354

Informations de publication

Date de publication:
01 02 2021
Historique:
pubmed: 13 6 2019
medline: 20 2 2021
entrez: 13 6 2019
Statut: ppublish

Résumé

To identify perioperative practice patterns that predictably impact postoperative pain. Despite significant advances in perioperative medicine, a significant portion of patients still experience severe pain after major surgery. Postoperative pain is associated with serious adverse outcomes that are costly to patients and society. The presented analysis took advantage of a unique observational data set providing unprecedented detailed pharmacological information. The data were collected by PAIN OUT, a multinational registry project established by the European Commission to improve postoperative pain outcomes. A multivariate approach was used to derive and validate a model predictive of pain on postoperative day 1 (POD1) in 1008 patients undergoing back surgery. The predictive and validated model was highly significant (P = 8.9E-15) and identified modifiable practice patterns. Importantly, the number of nonopioid analgesic drug classes administered during surgery predicted decreased pain on POD1. At least 2 different nonopioid analgesic drug classes (cyclooxygenase inhibitors, acetaminophen, nefopam, or metamizol) were required to provide meaningful pain relief (>30%). However, only a quarter of patients received at least 2 nonanalgesic drug classes during surgery. In addition, the use of very short-acting opioids predicted increased pain on POD1, suggesting room for improvement in the perioperative management of these patients. Although the model was highly significant, it only accounted for a relatively small fraction of the observed variance. The presented analysis offers detailed insight into current practice patterns and reveals modifications that can be implemented in today's clinical practice. Our results also suggest that parameters other than those currently studied are relevant for postoperative pain including biological and psychological variables.

Sections du résumé

OBJECTIVES
To identify perioperative practice patterns that predictably impact postoperative pain.
BACKGROUND
Despite significant advances in perioperative medicine, a significant portion of patients still experience severe pain after major surgery. Postoperative pain is associated with serious adverse outcomes that are costly to patients and society.
METHODS
The presented analysis took advantage of a unique observational data set providing unprecedented detailed pharmacological information. The data were collected by PAIN OUT, a multinational registry project established by the European Commission to improve postoperative pain outcomes. A multivariate approach was used to derive and validate a model predictive of pain on postoperative day 1 (POD1) in 1008 patients undergoing back surgery.
RESULTS
The predictive and validated model was highly significant (P = 8.9E-15) and identified modifiable practice patterns. Importantly, the number of nonopioid analgesic drug classes administered during surgery predicted decreased pain on POD1. At least 2 different nonopioid analgesic drug classes (cyclooxygenase inhibitors, acetaminophen, nefopam, or metamizol) were required to provide meaningful pain relief (>30%). However, only a quarter of patients received at least 2 nonanalgesic drug classes during surgery. In addition, the use of very short-acting opioids predicted increased pain on POD1, suggesting room for improvement in the perioperative management of these patients. Although the model was highly significant, it only accounted for a relatively small fraction of the observed variance.
CONCLUSION
The presented analysis offers detailed insight into current practice patterns and reveals modifications that can be implemented in today's clinical practice. Our results also suggest that parameters other than those currently studied are relevant for postoperative pain including biological and psychological variables.

Identifiants

pubmed: 31188202
pii: 00000658-202102000-00015
doi: 10.1097/SLA.0000000000003400
pmc: PMC8130578
mid: NIHMS1696081
doi:

Substances chimiques

Analgesics 0

Types de publication

Journal Article Observational Study Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

289-298

Subventions

Organisme : NIGMS NIH HHS
ID : K23 GM111657
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM137936
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM138353
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM089626
Pays : United States

Informations de copyright

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

Déclaration de conflit d'intérêts

The authors report no conflicts of interest.

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Auteurs

Quentin Baca (Q)

Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA.

Florian Marti (F)

Department of Anesthesiology, Intensive Care, Rescue and Pain Medicine, Kantonsspital, Solothurn.

Beate Poblete (B)

Clinic for Anesthesia, Rescue Medicine and Pain Therapy, Kantonsspital, Luzern, Switzerland.

Brice Gaudilliere (B)

Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA.

Nima Aghaeepour (N)

Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA.

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