Five classification models were evaluated to predict persistent opioid use: logistic regression, random forest, neural network, balanced random forest, and balanced bagging. Synthetic Minority Oversampling Technique was used to improve class balance. The primary outcome was persistent opioid use, defined as patient reporting to use opioids after 3 months postoperatively. The data were split into a training and test set. Performance metrics were evaluated on the test set and included the F1 score and the area under the receiver operating characteristics curve (AUC). Feature importance was ranked based on SHapley Additive exPlanations (SHAP). RESULTS: After exclusion (patients with missing follow-up data), 2611 patients were included in the analysis, of which 1209 (46.3%) continued to use opioids 3 months after surgery. The balanced random forest classifiers had the highest AUC (0.877, 95% confidence interval [CI], 0.834–0.894) compared to neural networks (0.729, 95% CI, 0.672–0.787), logistic regression (0.709, 95% CI, 0.652–0.767), balanced bagging classifier (0.859, 95% CI, 0.814–0.905), and random forest classifier (0.855, 95% CI, 0.813–0.897). The balanced random forest classifier had the highest F1 (0.758, 95% CI, 0.677–0.839). Furthermore, the specificity, sensitivity, precision, and accuracy were 0.883, 0.700, 0.836, and 0.780, respectively. The features based on SHAP analysis with the highest impact on model performance were age, preoperative opioid use, preoperative pain scores, and body mass index. CONCLUSIONS: The balanced random forest classifier was found to be the most effective model for identifying persistent opioid use after spine surgery....

中文翻译：

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对大脊柱手术后持续使用阿片类药物的高风险患者进行分类：机器学习方法


评估了五种分类模型来预测阿片类药物的持续使用：逻辑回归、随机森林、神经网络、平衡随机森林和平衡装袋。使用合成少数过采样技术来改善类别平衡。主要结局是持续使用阿片类药物，定义为术后 3 个月后报告使用阿片类药物的患者。数据被分为训练集和测试集。在测试集上评估性能指标，包括 F1 分数和受试者工作特征曲线下面积 (AUC)。特征重要性根据 SHapley Additive exPlanations (SHAP) 进行排名。结果：排除（缺失随访数据的患者）后，2611 名患者纳入分析，其中 1209 名（46.3%）患者术后 3 个月继续使用阿片类药物。与神经网络（0.729，95% CI，0.672-0.787）、逻辑回归（0.709，95% CI，0.652）相比，平衡随机森林分类器具有最高的 AUC（0.877，95% CI，0.834-0.894） –0.767）、平衡装袋分类器（0.859，95% CI，0.814–0.905）和随机森林分类器（0.855，95% CI，0.813–0.897）。平衡随机森林分类器的 F1 最高（0.758，95% CI，0.677-0.839）。此外，特异性、敏感性、精密度和准确度分别为 0.883、0.700、0.836 和 0.780。基于 SHAP 分析的对模型性能影响最大的特征是年龄、术前阿片类药物使用、术前疼痛评分和体重指数。结论：发现平衡随机森林分类器是识别脊柱手术后持续使用阿片类药物的最有效模型。