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.

IF 4.9 1区 医学 Q1 CLINICAL NEUROLOGY
Yu-Yung Chen, Hung-Kuan Yen, Jui-Yo Hsu, Ta-Chun Lin, Hao-Chen Lin, Chih-Wei Chen, Ming-Hsiao Hu, Olivier Q Groot, Joseph H Schwab
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引用次数: 0

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 multi-level 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 < 0.01). 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.

国际外部验证SORG机器学习算法用于预测前路颈椎椎间盘切除术和融合术后持续的阿片类药物处方,使用台湾队列1037例患者。
背景背景:前路颈椎椎间盘切除术和融合术(ACDF)被广泛用于治疗颈椎疾病,阿片类药物通常用于术后疼痛管理。然而,长期使用阿片类药物会带来重大风险,如依赖和不良健康影响。像SORG机器学习算法(sor - mla)这样的预测模型已经被开发出来,用于预测acdf后阿片类药物的长期使用。外部验证对于确保其在不同医疗保健环境和人群中的有效性至关重要。目的:本研究旨在评估sor - mla在台湾患者队列中预测ACDF后阿片类药物使用时间延长的普遍性。研究设计:回顾性队列研究,资料来自台湾某三级医疗中心。患者样本:纳入2010年至2018年期间接受ACDF的1037例患者。结局指标:主要结局是术后持续阿片类药物处方,定义为ACDF后持续使用阿片类药物至少90天。方法:采用鉴别指标(受试者工作特征曲线下面积(AUROC)和精密度-召回率曲线下面积(AUPRC))、标度、总体性能(Brier评分)和决策曲线分析对验证队列中sor - mla的性能进行评价。将验证队列与发展队列进行比较,发现sor - mla确定的人口统计学概况、医学法律框架、民族文化背景和术后阿片类药物使用的关键预测因素存在显著差异。台湾队列的特点是年龄较大,女性参与者比例较低,吸烟率较高,术前脊髓病和神经根病的发病率较高,手术前使用抗抑郁药的频率较高。相反,这些患者不太可能延长术前阿片类药物处方超过180天,接受多级ACDF手术,或同时使用β -2激动剂、加巴喷丁和ACE抑制剂等药物治疗。这项研究没有资金来源,也没有利益冲突。结果:该模型具有良好的判别能力,AUROC为0.78,AUPRC为0.35。校准曲线表明,该模型高估了阿片类药物长期使用的风险。这一差异可能归因于2000年至2018年美国发展队列中持续阿片类药物消费的发生率显著较高,比2010年至2018年台湾验证队列的发生率高出三倍(9.9%[270/2737]对3.3% [34/1037];P < 0.01)。Brier得分为0.033,比零模型得分0.040有所提高,表明整体性能稳健。决策曲线分析证实了该模型的临床效用,展示了不同决策阈值的净收益。结论:当应用于一个独特的台湾群组时,som - mla显示出强大的判别能力和整体表现。然而,该模型高估了阿片类药物长期使用的风险,这表明需要用更现代的数据进行重新校准,以反映当前的阿片类药物处方实践、种族和文化差异以及阿片类药物法规。在重新校准之后,应该在电子医疗保健系统中进行集成和前瞻性验证。这将使临床医生能够主动识别ACDF后长期使用阿片类药物风险增加的患者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Spine Journal
Spine Journal 医学-临床神经学
CiteScore
8.20
自引率
6.70%
发文量
680
审稿时长
13.1 weeks
期刊介绍: The Spine Journal, the official journal of the North American Spine Society, is an international and multidisciplinary journal that publishes original, peer-reviewed articles on research and treatment related to the spine and spine care, including basic science and clinical investigations. It is a condition of publication that manuscripts submitted to The Spine Journal have not been published, and will not be simultaneously submitted or published elsewhere. The Spine Journal also publishes major reviews of specific topics by acknowledged authorities, technical notes, teaching editorials, and other special features, Letters to the Editor-in-Chief are encouraged.
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