Comprehensive Summary
This nationwide cohort study examined over 1.1 million surgical patients in South Korea using data from the Korean National Health Insurance Service to identify risk factors and develop prediction models for postoperative chronic opioid use. Postoperative chronic opioid use, defined as filling at least 10 opioid prescriptions or receiving more than 120 days of opioid supply between 91 and 365 days after surgery, occurred in 0.84 percent of patients. Individuals who developed chronic opioid use were generally older, more often male, had more medical and psychiatric comorbidities, experienced longer hospital stays, received higher amounts of opioids during hospitalization, and were more likely to have used opioids before surgery. Certain surgical procedures, particularly lung surgery, general spinal surgery, and total knee arthroplasty, were associated with a higher risk of chronic opioid use. When prediction approaches were compared, a gradient boosting machine learning model slightly outperformed traditional logistic regression, with age, length of hospital stay, in hospital opioid consumption, and preoperative opioid use identified as the most important predictors. Overall, the study shows that machine learning models can help identify patients at higher risk for chronic opioid use after surgery and support more individualized and safer postoperative pain management.
Outcomes and Implications
The implications of this study are significant for improving postoperative pain management and reducing the risk of long term opioid dependence. By identifying clear risk factors such as older age, prior opioid use, longer hospital stays, and higher in hospital opioid consumption, clinicians can better recognize patients who may need closer monitoring or alternative pain management strategies after surgery. The use of machine learning technology, particularly gradient boosting models, demonstrates how large scale health data from the Korean National Health Insurance Service can be leveraged to detect complex patterns that traditional statistical methods may miss. This approach allows for more accurate and individualized risk prediction, supporting clinical decision making before and after surgery. In practice, these models could be integrated into hospital systems to guide opioid prescribing, encourage multimodal pain control, and ultimately reduce chronic opioid use, opioid related complications, and long term mortality among surgical patients.