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A machine learning algorithm for predicting prolonged postoperative opioid prescription after lumbar disc herniation
Hung-Kuan Yen1, Paul T Ogink2, Chuan-Ching Huang3
1Department of Orthopaedic Surgery, National Taiwan University Hospital, Taiwan. Address: No. 7, Zhongshan S. Rd., Zhongzheng Dist., Taipei City 100225, Taiwan (R.O.C.); Department of Medical Education, National Taiwan University Hospital, Hsin-Chu Branch, Taiwan. Address: No. 25, Ln. 442, Sec. 1, Jingguo Rd., North Dist., Hsinchu City 300001, Taiwan (R.O.C.); Department of Orthopaedic Surgery, National Taiwan University Hospital, Hsin-Chu Branch, Taiwan. Address: No. 25, Ln. 442, Sec. 1, Jingguo Rd., North Dist., Hsinchu City 300001, Taiwan (R.O.C.).
Background Context:
Preoperative prediction of prolonged postoperative opioid prescription helps identify patients for increased surveillance after surgery. The SORG machine learning model has been developed and successfully tested using 5,413 patients from the United States (US) to predict the risk of prolonged opioid prescription after surgery for lumbar disc herniation. However, external validation is an often-overlooked element in the process of incorporating prediction models in current clinical practice. This cannot be stressed enough in prediction models where medicolegal and cultural differences may play a major role.
Purpose:
The authors aimed to investigate the generalizability of the US citizens prediction model SORG to a Taiwanese patient cohort.
Study Design:
Retrospective study at a large academic medical center in Taiwan.
Patient Sample:
Of 1,316 patients who were 20 years or older undergoing initial operative management for lumbar disc herniation between 2010 and 2018.
Outcome Measures:
The primary outcome of interest was prolonged opioid prescription defined as continuing opioid prescription to at least 90 to 180 days after the first surgery for lumbar disc herniation at our institution.
Methods:
Baseline characteristics were compared between the external validation cohort and the original developmental cohorts. Discrimination (area under the receiver operating characteristic curve and the area under the precision-recall curve), calibration, overall performance (Brier score), and decision curve analysis were used to assess the performance of the SORG ML algorithm in the validation cohort. This study had no funding source or conflict of interests.
Results:
Overall, 1,316 patients were identified with sustained postoperative opioid prescription in 41 (3.1%) patients. The validation cohort differed from the development cohort on several variables including 93% of Taiwanese patients receiving NSAIDS preoperatively compared with 22% of US citizens patients, while 30% of Taiwanese patients received opioids versus 25% in the US. Despite these differences, the SORG prediction model retained good discrimination (area under the receiver operating characteristic curve of 0.76 and the area under the precision-recall curve of 0.33) and good overall performance (Brier score of 0.028 compared with null model Brier score of 0.030) while somewhat overestimating the chance of prolonged opioid use (calibration slope of 1.07 and calibration intercept of -0.87). Decision-curve analysis showed the SORG model was suitable for clinical use.
Conclusions:
Despite differences at baseline and a very strict opioid policy, the SORG algorithm for prolonged opioid use after surgery for lumbar disc herniation has good discriminative abilities and good overall performance in a Han Chinese patient group in Taiwan. This freely available digital application can be used to identify high-risk patients and tailor prevention policies for these patients that may mitigate the long-term adverse consequence of opioid dependence: https://sorg-apps.shinyapps.io/lumbardiscopioid/.
