Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation
Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation
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DOI：
10.1016/j.spinee.2019.06.002
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发表时间：
2019-11-01
期刊：
影响因子：
4.5
通讯作者：
Schwab, Joseph H.
中科院分区：
文献类型：
--
作者：
Karhade, Aditya, V;Ogink, Paul T.;Schwab, Joseph H.
背景：脊柱手术已被确定为术后长期使用阿片类药物的风险因素。阿片类药物使用的术前预测可以改善风险分层、共同决策和术前患者咨询。目的：本研究的主要目的是开发预测腰椎间盘突出症术后延长阿片类药物处方的算法。研究设计/设置：在五个医疗中心进行的回顾性病例对照研究。患者样本：对2000年1月1日至2018年3月1日期间接受腰椎间盘突出症手术的患者进行病历回顾。主要结果是术后持续阿片类药物处方至术后至少90至180天。开发了五种模型（弹性网络惩罚逻辑回归，随机森林，随机梯度提升，神经网络和支持向量机）来预测长期阿片类药物处方。预测的解释提供了全球（所有患者的平均值）和本地（为个别患者）。结果：总体而言，5,413例患者被确定，持续术后阿片类药物处方416（7.7%）在术后90至180天。弹性网络惩罚逻辑回归模型具有最好的区分度（c-统计量0.81）和良好的校准和整体性能;三个最重要的预测因素是：仪器，术前阿片类药物处方的持续时间和抑郁症的合并症。最终的模型被纳入一个开放访问的网络应用程序，能够提供预测以及患者特定的解释所产生的算法的结果。该应用程序可以在这里找到：www.example.com：术前预测长期术后阿片类药物处方可以帮助确定手术后增加监测的候选人。以患者为中心的预测解释可以提高共同决策和护理质量。(C)2019由Elsevier Inc.出版
BACKGROUND CONTEXT: Spine surgery has been identified as a risk factor for prolonged postoperative opioid use. Preoperative prediction of opioid use could improve risk stratification, shared decision-making, and patient counseling before surgery.PURPOSE: The primary purpose of this study was to develop algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation.STUDY DESIGN/SETTING: Retrospective, case-control study at five medical centers.PATIENT SAMPLE: Chart review was conducted for patients undergoing surgery for lumbar disc herniation between January 1, 2000 and March 1, 2018.OUTCOME MEASURES: The primary outcome of interest was sustained opioid prescription after surgery to at least 90 to 180 days postoperatively.METHODS: Five models (elastic-net penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict prolonged opioid prescription. Explanations of predictions were provided globally (averaged across all patients) and locally (for individual patients).RESULTS: Overall, 5,413 patients were identified, with sustained postoperative opioid prescription of 416 (7.7%) at 90 to 180 days after surgery. The elastic-net penalized logistic regression model had the best discrimination (c-statistic 0.81) and good calibration and overall performance; the three most important predictors were: instrumentation, duration of preoperative opioid prescription, and comorbidity of depression. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found here: https://sorg-apps.shinyapps.io/lumbardiscopioid/CONCLUSION: Preoperative prediction of prolonged postoperative opioid prescription can help identify candidates for increased surveillance after surgery. Patient-centered explanations of predictions can enhance both shared decision-making and quality of care. (C) 2019 Published by Elsevier Inc.