| Autor: |
Shin-Fu Chen, Chih-Chi Su, Chuan-Ching Huang, Paul T. Ogink, Hung-Kuan Yen, Olivier Q. Groot, Ming-Hsiao Hu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of the Formosan Medical Association, Vol 122, Iss 12, Pp 1321-1330 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
0929-6646 |
| DOI: |
10.1016/j.jfma.2023.06.027 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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