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Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.

BACKGROUND: Prescription opioid use at high doses or over extended periods of time is associated with adverse outcomes, including dependency and abuse. The aim of this study was to identify mediating variables that predict chronic opioid use, defined as three or more prescriptions after orthopedic s...

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Autores principales: Rhon, Daniel I, Snodgrass, Suzanne J, Cleland, Joshua A, Sissel, Charles D, Cook, Chad E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6249901/
https://www.ncbi.nlm.nih.gov/pubmed/30479746
http://dx.doi.org/10.1186/s13741-018-0105-8
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author Rhon, Daniel I
Snodgrass, Suzanne J
Cleland, Joshua A
Sissel, Charles D
Cook, Chad E
author_facet Rhon, Daniel I
Snodgrass, Suzanne J
Cleland, Joshua A
Sissel, Charles D
Cook, Chad E
author_sort Rhon, Daniel I
collection PubMed
description BACKGROUND: Prescription opioid use at high doses or over extended periods of time is associated with adverse outcomes, including dependency and abuse. The aim of this study was to identify mediating variables that predict chronic opioid use, defined as three or more prescriptions after orthopedic surgery. METHODS: Individuals were ages between 18 and 50 years and undergoing arthroscopic hip surgery between 2004 and 2013. Two categories of chronic opioid use were calculated based on individuals (1) having three or more unique opioid prescriptions within 2 years and (2) still receiving opioid prescriptions > 1 year after surgery. Univariate elationships were identified for each predictor variable, then significant variables (P > 0.15) were entered into a multivariate logistic regression model to identify the most parsimonious group of predictor variables for each chronic opioid use classification. Likelihood ratios were derived from the most robust groups of variables. RESULTS: There were 1642 participants (mean age 32.5 years, SD 8.2, 54.1% male). Nine predictor variables met the criteria after bivariate analysis for potential inclusion in each multivariate model. Eight variables: socioeconomic status (from enlisted rank family), prior use of opioid medication, prior use of non-opioid pain medication, high health-seeking behavior before surgery, a preoperative diagnosis of insomnia, mental health disorder, or substance abuse were all predictive of chronic opioid use in the final model (seven variables for three or more opioid prescriptions; four variables for opioid use still at 1 year; all< 0.05). Post-test probability of having three or more opioid prescriptions was 93.7% if five of seven variables were present, and the probability of still using opioids after 1 year was 69.6% if three of four variables were present. CONCLUSION: A combination of variables significantly predicted chronic opioid use in this cohort. Most of these variables were mediators, indicating that modifying them may be feasible, and the potential focus of interventions to decrease the risk of chronic opioid use, or at minimum better inform opioid prescribing decisions. This clinical prediction rule needs further validation.
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spelling pubmed-62499012018-11-26 Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule. Rhon, Daniel I Snodgrass, Suzanne J Cleland, Joshua A Sissel, Charles D Cook, Chad E Perioper Med (Lond) Research BACKGROUND: Prescription opioid use at high doses or over extended periods of time is associated with adverse outcomes, including dependency and abuse. The aim of this study was to identify mediating variables that predict chronic opioid use, defined as three or more prescriptions after orthopedic surgery. METHODS: Individuals were ages between 18 and 50 years and undergoing arthroscopic hip surgery between 2004 and 2013. Two categories of chronic opioid use were calculated based on individuals (1) having three or more unique opioid prescriptions within 2 years and (2) still receiving opioid prescriptions > 1 year after surgery. Univariate elationships were identified for each predictor variable, then significant variables (P > 0.15) were entered into a multivariate logistic regression model to identify the most parsimonious group of predictor variables for each chronic opioid use classification. Likelihood ratios were derived from the most robust groups of variables. RESULTS: There were 1642 participants (mean age 32.5 years, SD 8.2, 54.1% male). Nine predictor variables met the criteria after bivariate analysis for potential inclusion in each multivariate model. Eight variables: socioeconomic status (from enlisted rank family), prior use of opioid medication, prior use of non-opioid pain medication, high health-seeking behavior before surgery, a preoperative diagnosis of insomnia, mental health disorder, or substance abuse were all predictive of chronic opioid use in the final model (seven variables for three or more opioid prescriptions; four variables for opioid use still at 1 year; all< 0.05). Post-test probability of having three or more opioid prescriptions was 93.7% if five of seven variables were present, and the probability of still using opioids after 1 year was 69.6% if three of four variables were present. CONCLUSION: A combination of variables significantly predicted chronic opioid use in this cohort. Most of these variables were mediators, indicating that modifying them may be feasible, and the potential focus of interventions to decrease the risk of chronic opioid use, or at minimum better inform opioid prescribing decisions. This clinical prediction rule needs further validation. BioMed Central 2018-11-22 /pmc/articles/PMC6249901/ /pubmed/30479746 http://dx.doi.org/10.1186/s13741-018-0105-8 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Rhon, Daniel I
Snodgrass, Suzanne J
Cleland, Joshua A
Sissel, Charles D
Cook, Chad E
Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.
title Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.
title_full Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.
title_fullStr Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.
title_full_unstemmed Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.
title_short Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.
title_sort predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6249901/
https://www.ncbi.nlm.nih.gov/pubmed/30479746
http://dx.doi.org/10.1186/s13741-018-0105-8
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