Development of a clinical risk calculator for prolonged opioid use after shoulder surgery.
Academic Article
Overview
abstract
BACKGROUND: Understanding risk factors associated with prolonged opioid use to help mitigate abuse and develop presurgical screening programs to identify at-risk patients is paramount. The purpose of this study was to develop and validate a clinical risk assessment tool to preoperatively predict prolonged opioid use after shoulder surgery. METHODS: A total of 561 patients who underwent shoulder surgery within a tertiary health care system were identified, and opioid prescription data were retrospectively collected from the Connecticut Prescription Monitoring and Reporting System. The inclusion criteria were patients aged 18 years or older, and the exclusion criteria were patients not registered in the Connecticut Prescription Monitoring and Reporting System. Quantities of opioids prescribed were documented. Demographic characteristics, surgery type, medications, and medical comorbidities were identified by chart abstraction. Logistic regression was used to calculate odds ratios of patients using opioids longer than 6 weeks, and multivariate analysis was performed on 10 identified patient factors. A concordance index was used to calculate the discriminatory ability of a nomogram to predict prolonged opioid use. RESULTS: Multivariate analysis demonstrated that opioid use prior to surgery, insurance type, procedure type, body mass index, smoking status, and psychiatric disorders were responsible for prolonged opioid use. The prediction accuracy of this model was good, with a calculated concordance index of 0.766 (95% confidence interval, 0.736-0.820). CONCLUSIONS: We present a preoperative predictive calculator to help identify at-risk patients and quantify their risk of prolonged opioid use after shoulder surgery. This is a valuable clinical decision-making tool to identify patients benefitting from referral to pain management specialists and to possibly reduce the risk of opioid abuse and addiction.
