PDF(Pubmed)
Abstract：
BACKGROUND: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure\'s expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility.
METHODS: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables\' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application.
RESULTS: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively.
CONCLUSIONS: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.
METHODS: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables\' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application.
RESULTS: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively.
CONCLUSIONS: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.
摘要：
背景：自2011年以来，颈椎前路椎间盘切除术和融合术（ACDF）的频率增加了400％，这突显了在术前预测术后不良结局的必要性。我们的研究旨在实现两个目标：首先，开发一套可解释的机器学习（ML）模型，能够预测ACDF手术后的不良术后结果，其次,将这些模型嵌入到用户友好的Web应用程序中，展示他们的潜在效用。
方法：我们利用来自国家外科质量改进计划数据库的数据来确定接受ACDF手术的患者。感兴趣的结果是四个短期术后不良结果：延长住院时间（LOS），非家庭排放，再入院30天,和主要并发症。我们使用了五种ML算法——TabPFN，TabNET,XGBoost,LightGBM,和随机森林-再加上Optuna优化库，用于超参数调整。为了增强我们模型的可解释性，我们采用SHapley加法扩张(SHAP)来评估预测变量的相对重要性,并使用部分依赖图来说明各个变量对我们表现最好的模型所产生的预测的影响。我们使用接收器工作特性（ROC）曲线和精确召回曲线（PRC）可视化模型性能。计算的定量指标是ROC曲线下面积(AUROC)，平衡精度，中华人民共和国下的加权面积(AUPRC)，加权精度，加权召回。选择具有最高AUROC值的模型用于包括在web应用中。
结果：分析包括57,760例LOS延长患者[11.1％的LOS延长患者]，非家庭排放57,780[3.3%非家庭排放]，57,790，30天再入院[2.9%再入院]，和57,800的主要并发症[1.4％的主要并发症]。表现最好的模型，这些是用随机森林算法构建的，预测延长LOS的平均AUROC为0.776、0.846、0.775和0.747，非家庭排放，再入院,和并发症，分别。
结论：我们的研究采用先进的ML方法来增强对ACDF术后不良结局的预测。我们设计了一个可访问的Web应用程序，将这些模型集成到临床实践中。我们的发现肯定了ML工具作为风险分层的重要补充，促进预测不同的结果，并加强对ACDF的患者咨询。
方法：我们利用来自国家外科质量改进计划数据库的数据来确定接受ACDF手术的患者。感兴趣的结果是四个短期术后不良结果：延长住院时间（LOS），非家庭排放，再入院30天,和主要并发症。我们使用了五种ML算法——TabPFN，TabNET,XGBoost,LightGBM,和随机森林-再加上Optuna优化库，用于超参数调整。为了增强我们模型的可解释性，我们采用SHapley加法扩张(SHAP)来评估预测变量的相对重要性,并使用部分依赖图来说明各个变量对我们表现最好的模型所产生的预测的影响。我们使用接收器工作特性（ROC）曲线和精确召回曲线（PRC）可视化模型性能。计算的定量指标是ROC曲线下面积(AUROC)，平衡精度，中华人民共和国下的加权面积(AUPRC)，加权精度，加权召回。选择具有最高AUROC值的模型用于包括在web应用中。
结果：分析包括57,760例LOS延长患者[11.1％的LOS延长患者]，非家庭排放57,780[3.3%非家庭排放]，57,790，30天再入院[2.9%再入院]，和57,800的主要并发症[1.4％的主要并发症]。表现最好的模型，这些是用随机森林算法构建的，预测延长LOS的平均AUROC为0.776、0.846、0.775和0.747，非家庭排放，再入院,和并发症，分别。
结论：我们的研究采用先进的ML方法来增强对ACDF术后不良结局的预测。我们设计了一个可访问的Web应用程序，将这些模型集成到临床实践中。我们的发现肯定了ML工具作为风险分层的重要补充，促进预测不同的结果，并加强对ACDF的患者咨询。
