Validating an opioid prescribing algorithm in post-operative pediatric surgical oncology patients

Purpose: We developed an algorithm to decrease opioid prescriptions for pediatric oncology patients at discharge following surgery, based on a retrospective analysis to decrease variability and over-prescribing. The aim of this study was to prospectively test the algorithm. Methods: Opioid-naive patients undergoing surgery for tumor resection at a single institution were included. A prescribing algorithm was developed based on surgical approach, day of discharge, and inpatient opioid use. Prospectively collected data included outpatient opioid consumption and patient/family satisfaction. Total home dose prescribed was equal to that used in the 8 or 24 h, depending on length of stay and operative approach, prior to discharge, divided into 0.15 mg/kg doses. Results: The algorithm was used in 121 patients and correctly predicted outpatient opioid requirements for 102 patients (84.3%). For 15 (12.4%) patients, the algorithm over-estimated opioid need by an average of 0.38 OME/kg. Four (3.3%) patients required additional opioids. Using this algorithm, we decreased overall opioid prescriptions from 6.17 to 021 OME/kg (p < 0.001 ), and all but one patientifamily reported being satisfied with postoperative pain control. Conclusion: Using an algorithm based on inpatient opioid use, outpatient opioid needs can be accurately predicted, thereby reducing excess opioid prescriptions without detriment to patient satisfaction. (C) 2020 Elsevier Inc. All rights reserved.