Chen, Shin-FuShin-FuChenSu, Chih-ChiChih-ChiSuCHUAN-CHING HUANGOgink, Paul TPaul TOginkYen, Hung-KuanHung-KuanYenGroot, Olivier QOlivier QGrootMING-HSIAO HU2023-10-232023-10-232023-07-130929-6646https://scholars.lib.ntu.edu.tw/handle/123456789/636433Background/purpose: Identifying patients at risk of prolonged opioid use after surgery prompts appropriate prescription and personalized treatment plans. The Skeletal Oncology Research Group machine learning algorithm (SORG-MLA) was developed to predict the risk of prolonged opioid use in opioid-naive patients after lumbar spine surgery. However, its utility in a distinct country remains unknown. Methods: A Taiwanese cohort containing 2795 patients who were 20 years or older undergoing primary surgery for lumbar decompression from 2010 to 2018 were used to validate the SORG-MLA. Discrimination (area under receiver operating characteristic curve [AUROC] and area under precision-recall curve [AUPRC]), calibration, overall performance (Brier score), and decision curve analysis were applied. Results: Among 2795 patients, the prolonged opioid prescription rate was 5.2%. The validation cohort were older, more inpatient disposition, and more common pharmaceutical history of NSAIDs. Despite the differences, the SORG-MLA provided a good discriminative ability (AUROC of 0.71 and AURPC of 0.36), a good overall performance (Brier score of 0.044 compared to that of 0.039 in the developmental cohort). However, the probability of prolonged opioid prescription tended to be overestimated (calibration intercept of -0.07 and calibration slope of 1.45). Decision curve analysis suggested greater clinical net benefit in a wide range of clinical scenarios. Conclusion: The SORG-MLA retained good discriminative abilities and overall performances in a geologically and medicolegally different region. It was suitable for predicting patients in risk of prolonged postoperative opioid use in Taiwan.enAsiansMachine learningOpioid-related disordersOrthopedic proceduresValidation study[SDGs]SDG3[SDGs]SDG10journal article10.1016/j.jfma.2023.06.027374539002-s2.0-85165052524https://api.elsevier.com/content/abstract/scopus_id/85165052524