RESUMO
BACKGROUND: Pain is challenging after recovery from total knee arthroplasty (TKA) and total hip arthroplasty (THA) procedures, and patients often receive prescription opioids. However, opioid consumption by patients remains unclear, and unused opioids may lead to risks including misuse and diversion. The objective of this systematic review and meta-analysis was to compare prescription size versus patient-reported consumption of opioids after discharge following TKA and THA. METHODS: PubMed and Embase were systematically searched for publications published between 2015 and 2022 on patient-reported consumption of opioids after TKA and THA. The primary outcome was opioid use in oxycodone 5-mg equivalents. Team members independently reviewed studies for screening, inclusion, data extraction, and risk of bias. RESULTS: Among the 17 included studies (15 TKA and 11 THA), discharge opioid prescribing exceeded consumption for both TKA (88.4 versus 65.0 pills at 6 weeks) and THA (64.0 versus 29.8 pills at 12 weeks). For both TKA and THA, the range of opioids prescribed varied significantly, by 1.6-fold for TKA and 2.8-fold for THA. Most studies reported pain outcomes (89%) and the use of nonopioid medications (72%). Of the 4 studies offering prescribing recommendations, the amounts ranged from 50 to 104 pills for TKA and 30 to 45 pills for THA. CONCLUSIONS: Opioid prescribing exceeds the amount consumed following TKA and THA. These findings serve as a call to action to tailor prescribing guidelines to how much patients actually consume while emphasizing the use of nonopioid medications to better optimize recovery from surgery.
Assuntos
Analgésicos Opioides , Artroplastia de Quadril , Artroplastia do Joelho , Dor Pós-Operatória , Alta do Paciente , Humanos , Artroplastia de Quadril/efeitos adversos , Analgésicos Opioides/uso terapêutico , Artroplastia do Joelho/efeitos adversos , Dor Pós-Operatória/tratamento farmacológico , Dor Pós-Operatória/etiologiaAssuntos
Analgésicos Opioides , Transtornos Relacionados ao Uso de Opioides , Humanos , Analgésicos Opioides/efeitos adversos , Transtornos Relacionados ao Uso de Opioides/diagnóstico , Transtornos Relacionados ao Uso de Opioides/prevenção & controle , Transtornos Relacionados ao Uso de Opioides/tratamento farmacológico , Dor Pós-Operatória/tratamento farmacológico , Estudos RetrospectivosRESUMO
INTRODUCTION: While identifying opioid prescriptions in claims data has been instrumental in informing best practises, studies have not evaluated whether certain methods of identifying opioid prescriptions yield better results. We compared three common approaches to identify opioid prescriptions in large, nationally representative databases. METHODS: We performed a retrospective cohort study, analyzing MarketScan, Optum, and Medicare claims to compare three methods of opioid classification: claims database-specific classifications, National Drug Codes (NDC) from the Centers for Disease Control and Prevention (CDC), or NDC from Overdose Prevention Engagement Network (OPEN). The primary outcome was discrimination by area under the curve (AUC), with secondary outcomes including the number of opioid prescriptions identified by experts but not identified by each method. RESULTS: All methods had high discrimination (AUC>0.99). For MarketScan (n=70,162,157), prescriptions that were not identified totalled 42,068 (0.06%) for the CDC list, 2,067,613 (2.9%) for database-specific categories, and 0 (0%) for the OPEN list. For Optum (n=61,554,852), opioid prescriptions not identified totalled 9,774 (0.02%) for the CDC list, 83,700 (0.14%) for database-specific categories, and 0 (0%) for the OPEN list. In Medicare claims (n=92,781,299), the number of opioid prescriptions not identified totalled 8,694 (0.01%) for the CDC file and 0 (0%) for the OPEN list. DISCUSSION: This analysis found that identifying opioid prescriptions using methods from CDC and OPEN were similar and superior to prespecified database-specific categories. Overall, this study shows the importance of carefully selecting the approach to identify opioid prescriptions when investigating claims data.