Postoperative Opioid Prescribing Practices and Patient Opioid Utilization in Pediatric Orthopaedic Surgery Patients.

INTRODUCTION: Amid a national opioid epidemic, it is essential to review the necessity of opioid prescriptions. Research in adults has demonstrated patients often do not use their entire postoperative opioid prescription. Limited data suggest that the trend is similar in children. This study investigated the prescription volume and postoperative utilization rate of opioids among pediatric orthopaedic surgery patients at our institution. METHODS: We identified pediatric patients (ages below 18 y old) who presented to our institution for operating room intervention from May 24, 2021, to December 13, 2021. Patient demographics and opioid prescription volume were recorded. Parents and guardians were surveyed by paper "opioid diary" or phone interview between postoperative days 10 to 15, assessing pain level, opioid use, and plans for remaining opioid doses. Wilcoxon rank-sum test, Independent t test, and Pearson correlation were used for the analysis of continuous variables. Multivariable logistic regression was used to control for patient demographic variables while analyzing opioid usage relationships. RESULTS: Prescription volume information was collected for 280 patients during the study period. We were able to collect utilization information for 102 patients (Group 1), whereas the remaining 178 patients contributed only prescription volume data (Group 2). Patients with upper extremity fractures received significantly fewer opioid doses at discharge compared with other procedure types ( P =0.036). Higher BMI was positively correlated with more prescribed opioid doses ( R2 =0.647, P <0.001). The mean opioid utilization rate was 22.37%. A total of 50.6% of patients prescribed opioids at discharge used zero doses. A total of 96.2% of patients used opioids for 5 days or less. Most families had not disposed of excess medication by postoperative day 10. CONCLUSIONS: We found significant differences in opioid prescribing practices based on patient and procedure-specific variables. In addition, although our pediatric orthopaedic surgery patients had low overall rates of postoperative opioid utilization, there was significant variation in opioid use among procedure types. These results provide insights that can guide opioid prescribing practices for pediatric orthopaedic patients and promote patient education to ensure safe opioid disposal.

Patient-Reported Data Augment Prediction Models of Persistent Opioid Use after Elective Upper Extremity Surgery.

BACKGROUND: Opioids play a role in pain management after surgery, but prolonged use contributes to developing opioid use disorder. Identifying patients at risk of prolonged use is critical for deploying interventions that reduce or avoid opioids; however, available predictive models do not incorporate patient-reported data (PRD), and it remains unclear whether PRD can predict postoperative use behavior. The authors used a machine learning approach leveraging preoperative PRD and electronic health record data to predict persistent opioid use after upper extremity surgery. METHODS: Included patients underwent upper extremity surgery, completed preoperative PRD questionnaires, and were prescribed opioids after surgery. The authors trained models using a 2018 cohort and tested in a 2019 cohort. Opioid use was determined by patient report and filled prescriptions up to 6 months after surgery. The authors assessed model performance using area under the receiver operating characteristic, sensitivity, specificity, and Brier score. RESULTS: Among 1656 patients, 19% still used opioids at 6 weeks, 11% at 3 months, and 9% at 6 months. The XGBoost model trained on PRD plus electronic health record data achieved area under the receiver operating characteristic 0.73 at 6 months. Factors predictive of prolonged opioid use included income; education; tobacco, drug, or alcohol abuse; cancer; depression; and race. Protective factors included preoperative Patient-Reported Outcomes Measurement Information System Global Physical Health and Upper Extremity scores. CONCLUSIONS: This opioid use prediction model using preintervention data had good discriminative performance. PRD variables augmented electronic health record-based machine learning algorithms in predicting postsurgical use behaviors and were some of the strongest predictors. PRD should be used in future efforts to guide proper opioid stewardship. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.

Preoperative Patient-Reported Data Indicate the Risk of Prolonged Opioid Use After Hand and Upper Extremity Surgeries.

PURPOSE: Opioids play an important role in pain management after surgery but also increase the risk of prolonged opioid use in patients. The identification of patients who are more likely to use opioids after intended short-term treatment is critical for employing alternative management approaches or targeted interventions for the prevention of opioid-related problems. We used patient-reported data (PRD) and electronic health record information to identify factors predictive of prolonged opioid use after surgery. METHODS: We used our institutional registry containing data on all patients who underwent elective upper extremity surgeries. We evaluated factors associated with prolonged opioid use in the cohort from the year 2018 to 2019. We then validated our results using the 2020 cohort. The predictive variables included preoperative PRD and electronic health record data. Opioid use was determined based on patient reports and/or filled opioid prescriptions 3 months after surgery. We conducted bivariate regression, followed by multivariable regression analyses, and model validation using area under the receiver operating curve. RESULTS: We included 2,114 patients. In our final model on the 2018-2019 electronic health records and PRD data (n = 1,589), including numerous patient-reported outcome questionnaire scores, patients who were underweight and had undergone trauma-related surgery had higher odds of being on opioids at 3 months. Additionally, each 5-unit decrease in the preoperative Patient-Reported Outcomes Measurement Information System Global Physical Health score was associated with a 30% increased odds of being on opioids at 3 months. The area under the receiver operating curve of our model was 70.4%. On validation using data from the 2020 cohort, the area under the receiver operating curve was 60.3%. The Hosmer-Lemeshow test indicated a good fit. CONCLUSIONS: We found that preoperative questionnaire scores were associated with prolonged postoperative opioid use, independent of other variables. Furthermore, PRD may provide unique patient-level insights, alongside other factors, to improve our understanding of postsurgical pain management. TYPE OF STUDY/LEVEL OF EVIDENCE: Prognostic II.

The Curtis Hand Injury Matrix Score: Determining the Need for Specialized Upper Extremity Care.

PURPOSE: Given the limited impact of transfer guidelines and the lack of comparative metrics for upper extremity trauma, we introduced the Curtis Hand Injury Matrix (CHIM) score to evaluate upper extremity injury acuity from the specialist perspective. Our goal was to evaluate the CHIM score as an indicator of complexity and specialist need by correlating the score with arrival mode, length of stay (LOS), discharge disposition, and procedure location. METHODS: We identified all hand and upper extremity emergency room visits at our institution in 2018 and 2019. On initial evaluation, our institution's hand surgery team assigned each patient an alphanumeric score with a number (1-5) and letter (A-H) corresponding to injury severity and pathology, respectively. Patients were divided into 5 groups (1-5) with lower scores indicating greater severity. We compared age, LOS, discharge disposition, procedure location, transfer status, and arrival mode between groups and assessed the relationships between matrix scores and discharge disposition, procedure performed, and LOS. RESULTS: There were 3,822 patients that accounted for 4,026 upper extremity evaluations. There were significant differences in LOS, discharge dispositions, procedure locations, transfer status, and arrival modes between groups. Patients with more severe scores had higher rates of admission and more operating room procedures. Higher percentages of patients who arrived via helicopter, ambulance, or transfer had more severe scores. Patients with more severe scores were significantly more likely to have a procedure, hospital admission, and longer hospital stay. CONCLUSIONS: The CHIM score provides a framework to catalog the care and resources required when covering specialized hand and upper extremity calls and accepting transfers. This clinical validation supports considering broader use. TYPE OF STUDY/LEVEL OF EVIDENCE: Prognostic IV.

Disparities Limit the Effect and Benefit of a Web-Based Clinic Intake System.

With increasing value being placed on patient-centered care and the focus on efficiency and workflow in health care delivery, the authors have implemented a web-based system for demographic, medical history, and patient-reported outcomes data collection for every clinical visit at their specialty upper-extremity center. They evaluated initial success and disparities in use after 12 months. The authors evaluated questionnaire parameters from 2018 patients, focusing primarily on the new patient intake form. They analyzed form-completion time relative to appointment time and form-completion percentage at various times before the appointment. The authors grouped patients by age, sex, race, income, education, employment status, transportation access, self-reported pain, and quality-of-life scores. Waiting room time was evaluated. Of new patients, 94% used the web-based platform to complete the intake form. Of the 4898 completed forms, 69.7% were done more than 1 hour before appointment time, indicating that a personal device was used. When grouped by patient characteristics and controlling for all demographic factors, patients who were male, non-White, and older than 40 years; had lower family income; and had a high school education or less were significantly associated with later form completion. Of the 1136 patients for whom the authors had adequate waiting room time data, late form completion significantly increased odds of waiting more than 15 minutes to be placed into an examination room. These data indicate that the authors are reliably capturing important patient information before appointment time. This could improve clinical workflow and overall quality of care and also identify limits in access and online system use, providing opportunities to improve capture by developing targeted interventions for specific patient populations. [Orthopedics. 2021;44(3):e434-e439.].

Variation in Postoperative Opioid Prescribing Among Upper-Extremity Surgery Providers.

Purpose: To identify targets for corrective interventions and guide improved opioid stewardship, we studied opioid prescribing patterns of attending surgeons compared with surgical trainees for 2 upper-extremity surgeries: open reduction internal fixation (ORIF) of distal radius fractures (DRF), and carpal tunnel release (CTR). Methods: We retrospectively reviewed records for all patients who underwent CTR or DRF ORIF at 6 hospitals across a large health system from 2016 to 2018. We collected prescriber training level (attending vs trainee), analgesic prescribed, and amount initially prescribed after surgery converted to oral morphine equivalents (OMEs). Regression models evaluated OMEs by prescriber and surgery type. Our final models included an interaction term between prescriber training level and year of surgery to assess group changes over time. No prescription guidelines or formal training was provided during the study period. Results: We included 707 CTR and 383 DRF ORIF patients. Opioids prescribed by trainees ranged from 90 to 300 OMEs (median, 180 OMEs). Opioids prescribed by attendings ranged from 100 to 225 OMEs (median, 150 OMEs). Early in the analyses, trainees prescribed significantly more than attendings (320 versus 180). Over time, trainees reduced overprescribing significantly more, by an additional 40 OME/y. By the end of the analysis period, trainees were prescribing less OME than were attendings (112.5 vs 150). Both groups continued to prescribe more than recently suggested amounts for both procedures. Conclusions: Our study found that both attendings and trainees overprescribed opioids after surgery. Trainees prescribed more than attendings over the study period; however, when analyzing for improvement over time and with no formal intervention or training, trainees showed greater improvement, eventually dropping to levels at or below that of attendings. Considering that most change was seen at the trainee level, education for established providers may be an area in which more improvement can be made. Clinical relevance: Understanding which providers are more likely to overprescribe opioids can help guide interventions that improve opioid stewardship.