Literature DB >> 32740234

Does Surgical Intensity Correlate With Opioid Prescribing?: Classifying Common Surgical Procedures.

Hoyune E Cho1, Hsou-Mei Hu1,2, Vidhya Gunaseelan1,2, Jung-Sheng Chen3, Michael J Englesbe1,2, Kevin C Chung1, Jennifer F Waljee1,2.   

Abstract

OBJECTIVE: To examine the relationship between aspects of surgical intensity and postoperative opioid prescribing. SUMMARY OF BACKGROUND DATA: Despite the emergence of postoperative prescribing guidelines, recommendations are lacking for many procedures. identifying a framework based on surgical intensity to guide prescribing for those procedures in which guidelines may not exist could inform postoperative prescribing.
METHODS: We used clustering analysis with 4 factors of surgical intensity (intrinsic cardiac risk, pain score, median operative time, and work relative value units) to devise a classification system for common surgical procedures. We used IBM MarketScan Research Database (2010-2017) to examine the correlation between this framework with initial opioid prescribing and rates of refill for each cluster of procedures.
RESULTS: We examined 2,407,210 patients who underwent 128 commonly performed surgeries. Cluster analysis revealed 5 ordinal clusters by intensity: low, mid-low, mid, mid-high, and high. We found that as the cluster-order increased, the median amount of opioid prescribed increased: 150 oral morphine equivalents (OME) for low-intensity, 225 OME for mid-intensity, and 300 OME for high-intensity surgeries. Rates of refill increased as surgical intensity also increased, from 17.4% for low, 26.4% for mid, and 48.9% for high-intensity procedures. The odds of refill also increased as cluster-order increased; relative to low-intensity procedures, high-intensity procedures were associated with 4.37 times greater odds of refill.
CONCLUSION: Surgical intensity is correlated with initial opioid prescribing and rates of refill. Aspects of surgical intensity could serve as a guide for procedures in which guidelines based on patient-reported outcomes are not available.
Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

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Year:  2020        PMID: 32740234     DOI: 10.1097/SLA.0000000000004299

Source DB:  PubMed          Journal:  Ann Surg        ISSN: 0003-4932            Impact factor:   13.787


  2 in total

1.  Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.

Authors:  Jaewon Hur; Shengpu Tang; Vidhya Gunaseelan; Joceline Vu; Chad M Brummett; Michael Englesbe; Jennifer Waljee; Jenna Wiens
Journal:  Am J Surg       Date:  2021-03-26       Impact factor: 3.125

2.  Opioid prescribing practices at hospital discharge for surgical patients before and after the Centers for Disease Control and Prevention's 2016 opioid prescribing guideline.

Authors:  Catherine L Chen; Zhonghui Guan; Erica Langnas; Andrew Bishara; Rhiannon Croci; Rosa Rodriguez-Monguio; Elizabeth C Wick
Journal:  BMC Anesthesiol       Date:  2022-05-11       Impact factor: 2.376

  2 in total

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