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Pain

Analysis of predictors of opioid-free analgesia for management of acute post-surgical pain in the United States

, , , , , , , & show all
Pages 283-289 | Received 07 Feb 2018, Accepted 23 May 2018, Published online: 26 Jun 2018
 

Abstract

Objectives: Utilization of opioid-free analgesia (OFA) for post-surgical pain is a growing trend to counter the risks of opioid abuse and opioid-related adverse drug events (ORADEs). However, utilization patterns of OFA have not been examined. In this study, we investigated the utilization patterns and predictors of OFA in a surgical population in the United States.

Methods: Analysis of the Cerner Health Facts database (January 2011 to December 2015) was conducted to describe hospital and patient characteristics associated with OFA. Baseline characteristics, such as age, gender, race, discharge status, year of admission and chronic comorbidities at index admission were collected. Hospital characteristics and payer type at index admission were collected as reported in the electronic health record database. Descriptive statistics and logistic regression were used to identify statistically significant predictors of OFA on patient and institutional levels.

Results: The study identified 10,219 patients, from 187 hospitals, who received post-surgical OFA and 255,196 patients who received post-surgical opioids. OFA rates varied considerably by hospital. Patients more likely to receive OFA were older (OR = 1.06, 95% CI [1.03, 1.10]; p < .001), or had neurological disorders (OR = 1.24, 95% CI [1.10, 1.39]; p < .001), diabetes (OR = 1.20, 95% CI [1.08, 1.33]; p = .001) or psychosis (OR = 1.18, 95% CI [1.01, 1.37]; p = .030). Patients with obesity and depression were less likely to receive OFA (OR = 0.80, 95% CI [0.67, 0.95]; p = .010 OR = 0.85, 95% CI [0.73, 0.98]; p = .030, respectively).

Conclusions: Use of post-surgical OFA was limited overall and was not favored in some patient groups prone to ORADEs, indicating missed opportunities to reduce opioid use and ORADE incidence. A substantial proportion of OFA patients was contributed by a few hospitals with especially high rates of OFA, suggesting that hospital policies, institutional structure and cross-functional departmental commitment to reducing opioid use may play a large role in the implementation of OFA.

Transparency

Declaration of funding

This study was supported by Mallinckrodt Pharmaceuticals. The study sponsor was involved in the development of the study design, interpretation and analysis of data, writing the manuscript, and submission for publication.

Author contributions: All authors meet ICMJE authorship criteria. Conception/design of the work: all authors. Acquisition of data: V.K., B.H.N. Data interpretation: all authors. Drafting the work, revising it critically for intellectual content, final approval of the version to be published: all authors. Agreement to be accountable for all aspects of the work: all authors.

Declaration of financial/other relationships

E.A.B., R.F., G.J.W., B.L. and J.C. have disclosed that they are employees of Mallinckrodt Pharmaceuticals. A.T.P. has disclosed that she was an employee of Mallinckrodt Pharmaceuticals at the time of the study. V.K. has disclosed that he is a consultant with Boston Strategic Partners Inc., which contracted with Mallinckrodt Pharmaceuticals for this study. B.H.N. has disclosed that he is an employee of OptiStatim LLC and was contracted for this study by Boston Strategic Partners Inc. R.D.U. has disclosed that he has received funding from Mallinckrodt for an unrelated study and an honorarium.

A CMRO peer reviewer declares receiving fees as speaker or consultant from the following companies: Grunenthal, Mundipharma, Molteni, Angelini, Pfizer. Other CMRO peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Acknowledgements

The authors thank Jeffrey R. Skaar and Sloka Iyengar at Boston Strategic Partners Inc. for editorial support. The authors acknowledge Xuan Zhang, Boston Strategic Partners Inc, Boston, MA, for contributions to data analysis.

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