MP34-15 INPATIENT METRICS PREDICT POST-DISCHARGE OPIOID USE: INDIVIDUALIZING PRESCRIBING AFTER RADICAL PROSTATECTOMY

Abstract

You have accessJournal of UrologyHealth Services Research: Quality Improvement & Patient Safety II (MP34)1 Sep 2021MP34-15 INPATIENT METRICS PREDICT POST-DISCHARGE OPIOID USE: INDIVIDUALIZING PRESCRIBING AFTER RADICAL PROSTATECTOMY Russell Becker, Zhou Su, Mitchell Huang, Michael Biles, Kelly Harris, Kevin Koo, Misop Han, Mohamad Allaf, Amin Herati, and Hiten Patel Russell BeckerRussell Becker More articles by this author , Zhou SuZhou Su More articles by this author , Mitchell HuangMitchell Huang More articles by this author , Michael BilesMichael Biles More articles by this author , Kelly HarrisKelly Harris More articles by this author , Kevin KooKevin Koo More articles by this author , Misop HanMisop Han More articles by this author , Mohamad AllafMohamad Allaf More articles by this author , Amin HeratiAmin Herati More articles by this author , and Hiten PatelHiten Patel More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002043.15AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Judicious opioid stewardship is imperative, and ideally matches each patient’s prescription to their true medical necessity. However, minimal objective data exist to guide prescribers in fulfilling this mission. We evaluated in-hospital parameters as predictors of post-discharge opioid utilization in a large cohort of patients undergoing radical prostatectomy (RP), to help provide objective evidence-based guidance for individualized prescribing. METHODS: A prospective cohort of 443 patients who underwent open or robotic RP between August 2017 and November 2018 were followed in the IRB-approved Opioid Reduction Intervention for Open, Laparoscopic, and Endoscopic Surgery (ORIOLES) initiative. Baseline demographics, clinical variables, patient-reported pain scores (scale 0-10), and inpatient and post-discharge pain medication utilization were tabulated from electronic medical records and planned 30-day follow-up physician telephone calls. All opioid medications were converted to oral morphine equivalents (OMEQ). Predictive factors for post-discharge opioid utilization were analyzed by univariate and multivariate linear regression, adjusting for opioid reduction interventions performed in the ORIOLES initiative. RESULTS: Of 443 patients (102 open and 341 robotic RP), 374 (84%) were discharged on post-operative day 1. On univariable analysis, the factors most strongly associated with post-discharge opioid utilization included inpatient opioid utilization (overall, average per day, and in the 12 hours prior to discharge; Pearson’s correlation coefficients r=0.34-0.38, p<0.001), maximum patient-reported pain scores (24 hours, 12 hours, and final score prior to discharge; r=0.26-0.32, p<0.001), and history of prior opioid use. On multivariable analysis, inpatient opioid use (+0.7 post-discharge OMEQ per 1 inpatient OMEQ) and maximum pain score (+5.5 post-discharge OMEQ per 1 point) in the final 12 hours prior to discharge remained significantly correlated with post-discharge utilization. A final predictive model to guide post-discharge prescribing was constructed (C-statistic for predicting various levels of post-discharge use ranged from 0.71-0.76). CONCLUSIONS: Following RP, inpatient opioid use, patient-reported pain scores, and prior opioid use are strongly correlated with post-discharge opioid utilization. These data can help guide individualized opioid prescribing at hospital discharge to more reliably meet individual needs while minimizing risks of overprescribing. Source of Funding: N/A © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e622-e622 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Russell Becker More articles by this author Zhou Su More articles by this author Mitchell Huang More articles by this author Michael Biles More articles by this author Kelly Harris More articles by this author Kevin Koo More articles by this author Misop Han More articles by this author Mohamad Allaf More articles by this author Amin Herati More articles by this author Hiten Patel More articles by this author Expand All Advertisement Loading ...