The article discusses the validation of a machine learning algorithm called SORG-MLA, developed by the Skeletal Oncology Research Group. The algorithm aims to predict the risk of prolonged opioid use in patients who are opioid-naive after undergoing lumbar spine surgery. The study used a Taiwanese cohort of 2795 patients to validate the algorithm’s performance. The results showed that the SORG-MLA had good discriminative ability and overall performance, although it tended to overestimate the probability of prolonged opioid prescription. The decision curve analysis suggested clinical net benefit in a wide range of scenarios. Overall, the SORG-MLA was found to be suitable for predicting patients at risk of prolonged postoperative opioid use in Taiwan
Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : most advanced spinal hospital in UK
Published article
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.
Lumbar Decompression Surgery Expert. Best Spinal Surgeon UK
J Formos Med Assoc. 2023 Jul 13:S0929-6646(23)00255-3. doi: 10.1016/j.jfma.2023.06.027. Online ahead of print.ABSTRACTBACKGROUND/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,
J Formos Med Assoc. 2023 Jul 13:S0929-6646(23)00255-3. doi: 10.1016/j.jfma.2023.06.027. Online ahead of print.
ABSTRACT
BACKGROUND/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.
PMID:37453900 | DOI:10.1016/j.jfma.2023.06.027
The London Spine Unit : most advanced spinal hospital in UK
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External validation of machine learning algorithm predicting prolonged opioid prescriptions in opioid-naïve lumbar spine surgery patients using a Taiwanese cohort
