Background: Numerous attempts have been made to decrease the incidence of opioid dependence after orthopedic surgeries. However, no effective means of preoperative risk stratification currently exists. The purpose of this study was to determine the ability of the Opioid Risk Tool (ORT) to predict the rate of opioid dependence 2 years after arthroscopic rotator cuff repair (ARCR). Methods: We prospectively evaluated all patients undergoing primary ARCR at a single institution over a 1.5 year period with a minimum of 2-year follow-up. All patients completed the ORT prior to surgery and were stratified into Low, Moderate, and High risk categories. The primary outcome was postoperative opioid dependence, defined as receiving a minimum of 6 opioid prescriptions within 2 years following surgery. Secondary outcomes included the total number of morphine milligram equivalents prescribed, total number of opioid prescriptions filled, and total number of opioid pills prescribed during this time interval. All outcome variables were compared amongst Low, Moderate, and High risk groups. Assessment of a statistical correlation between each outcome variable and individual numerical ORT scores (1-9) was performed. Results: A total of 137 patients were included for analysis. No statistically significant difference was noted in any primary or secondary outcome variable when compared between Low, Moderate, and High risk groups. The total cohort demonstrated a 19% rate of post-operative opioid dependence. No correlation was identified between any outcome variable and individual numerical ORT scores. A greater rate of dependence and quantity of opioids prescribed was noted amongst patients with a history of prior opioid use. Conclusion: The ORT was not predictive of the risk of opioid dependence or quantity of opioids prescribed after ARCR. Attention should be focused on alternative means of identification and management of patients at risk for opioid dependence after orthopedic procedures, including those with a history of prior opioid use.
Background: Numerous attempts have been made to decrease the incidence of opioid dependence after orthopedic surgeries. However, no effective means of preoperative risk stratification currently exists. The purpose of this study was to determine the ability of the Opioid Risk Tool (ORT) to predict the rate of opioid dependence 2 years after arthroscopic rotator cuff repair (ARCR). Methods: We prospectively evaluated all patients undergoing primary ARCR at a single institution over a 1.5 year period with a minimum of 2-year follow-up. All patients completed the ORT prior to surgery and were stratified into Low, Moderate, and High risk categories. The primary outcome was postoperative opioid dependence, defined as receiving a minimum of 6 opioid prescriptions within 2 years following surgery. Secondary outcomes included the total number of morphine milligram equivalents prescribed, total number of opioid prescriptions filled, and total number of opioid pills prescribed during this time interval. All outcome variables were compared amongst Low, Moderate, and High risk groups. Assessment of a statistical correlation between each outcome variable and individual numerical ORT scores (1-9) was performed. Results: A total of 137 patients were included for analysis. No statistically significant difference was noted in any primary or secondary outcome variable when compared between Low, Moderate, and High risk groups. The total cohort demonstrated a 19% rate of post-operative opioid dependence. No correlation was identified between any outcome variable and individual numerical ORT scores. A greater rate of dependence and quantity of opioids prescribed was noted amongst patients with a history of prior opioid use. Conclusion: The ORT was not predictive of the risk of opioid dependence or quantity of opioids prescribed after ARCR. Attention should be focused on alternative means of identification and management of patients at risk for opioid dependence after orthopedic procedures, including those with a history of prior opioid use.
Authors: Ted Jones; Todd Moore; Jacob L Levy; Susan Daffron; Joe H Browder; Leslie Allen; Steven D Passik Journal: Clin J Pain Date: 2012-02 Impact factor: 3.442