International external validation of the SORG machine learning algorithm for predicting sustained postoperative opioid prescription after anterior cervical discectomy and fusion using a Taiwanese cohort of 1,037 patients
Journal
The Spine Journal
ISSN
1529-9430
Date Issued
2025-05
Author(s)
Yu-Yung Chen
Hung-Kuan Yen
Lin, Ta-Chun
Lin, Hao-Chen
Groot, Olivier Q
Schwab, Joseph H
Abstract
Background context: Anterior cervical discectomy and fusion (ACDF) is widely performed for cervical spine disorders, with opioids commonly prescribed postoperatively for pain management. However, prolonged opioid use carries significant risks such as dependency and adverse health effects. Predictive models like the SORG machine learning algorithm (SORG-MLA) have been developed to forecast prolonged opioid use post-ACDF. External validation is essential to ensure their effectiveness across different healthcare settings and populations.
Purpose: The study aimed to assess the generalizability of the SORG-MLA to a Taiwanese patient cohort for predicting prolonged opioid use after ACDF.
Study design: Retrospective cohort study utilizing data from a tertiary care center in Taiwan.
Patient sample: 1,037 patients who underwent ACDF between 2010 and 2018 were included.
Outcome measures: The primary outcome was sustained postoperative opioid prescription defined as continuous opioid use for at least 90 days following ACDF.
Methods: The performance of the SORG-MLA in the validation cohort was assessed using discrimination measures (area under the receiver operating characteristic curve [AUROC] and the area under the precision-recall curve [AUPRC]), calibration, overall performance (Brier Score), and decision curve analysis. Comparing the validation cohort to the developmental revealed significant differences in demographic profiles, medicolegal frameworks, ethnic cultural contexts and key predictors of postoperative opioid use identified by the SORG-MLA. The Taiwanese cohort was characterized by an older age demographic, a lower proportion of female participants, higher smoking prevalence, higher incidence of preoperative myelopathy and radiculopathy, and more frequent use of antidepressants prior to surgery. Conversely, these patients were less likely to have extended preoperative opioid prescriptions beyond 180 days, undergo multilevel ACDF procedures, or be treated with concurrent medications such as Beta-2 agonists, Gabapentin, and ACE inhibitors. This study had no funding source or conflict of interests.
Results: The model demonstrated good discriminative ability, with an AUROC of 0.78 and an AUPRC of 0.35. Calibration curves indicated that the model overestimated the risk of prolonged opioid use. This discrepancy may be attributed to the significantly higher incidence of sustained opioid consumption in the American development cohort, spanning from 2000 to 2018, which was threefold higher than that in the Taiwanese validation cohort between 2010 and 2018 (9.9% [270/2737] vs. 3.3% [34/1037]; p < .01). The Brier score was 0.033, which improved upon the null model's score of 0.040, indicating robust overall performance. Decision curve analysis confirmed the model's clinical utility, demonstrating net benefits across various decision thresholds.
Conclusions: The SORG-MLA has demonstrated robust discriminative abilities and overall performance when applied to a unique Taiwanese cohort. However, the model exhibited an overestimation of the risk of prolonged opioid use, suggesting the need for recalibration with more contemporary data to reflect current opioid prescription practices, ethnic and cultural differences, and opioid regulations. Following recalibration, integration and prospective validation within the electronic healthcare system should be pursued. This will enable clinicians to proactively identify patients at heightened risk of prolonged opioid use following ACDF.
Subjects
Anterior cervical discectomy and fusion
External validation
Machine learning
Opioid
SORG
Taiwanese cohort
SDGs
Publisher
Elsevier BV
Type
journal article