Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA; Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
Spine Care Institute, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA; Universitätsklinikum Ulm, Klinik für Orthopädie, Oberer Eselsberg 45, 89081 Ulm, Germany.
Spine J. 2024 Feb;24(2):239-249. doi: 10.1016/j.spinee.2023.09.029. Epub 2023 Oct 20.
Degenerative lumbar spondylolisthesis (DLS) is a prevalent spinal disorder, often requiring surgical intervention. Accurately predicting surgical outcomes is crucial to guide clinical decision-making, but this is challenging due to the multifactorial nature of postoperative results. Traditional risk assessment tools have limitations, and with the advent of machine learning, there is potential to enhance the precision and comprehensiveness of preoperative evaluations.
We aimed to develop a machine-learning algorithm to predict surgical outcomes in patients with degenerative lumbar spondylolisthesis (DLS) undergoing spinal fusion surgery, only using preoperative data.
Retrospective cross-sectional study.
Patients with DLS undergoing lumbar spinal fusion surgery.
This study aimed to predict the occurrence of lower back pain (LBP) ≥4 on the numeric analogue scale (NAS) 2 years after surgery. LBP was evaluated as the average pain patients experienced at rest in the week before questioning. NAS ranges from 0 to 10, 0 representing no pain and 10 representing the worst pain imaginable.
We conducted a retrospective analysis of prospectively enrolled patients who underwent spinal fusion surgery for degenerative lumbar spondylolistheses at our institution in the United States between January 2016 and December 2018. The initial patient characteristics to be included in the training of the model were chosen by clinical expertise and through a literature review and included demographic characteristics, comorbidities, and radiologic features. The data was split into a training and validation datasets using a 60/40 split. Four different machine learning models were trained, including the modern XGBoost model, logistic regression, random-forest, and support vector machine (SVM). The models were evaluated according to the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. An AUC of 0.7 to 0.8 was considered fair, 0.8 to 0.9 good, and ≥ 0.9 excellent. Additionally, a calibration plot and the Brier score were calculated for each model.
A total of 135 patients (66% female) were included. A total of 38 (28%) patients reported LBP ≥ 4 after 2 years, representing the positive class. The XGBoost model demonstrated the best performance in the validation set with an AUC of 0.81 (95% CI 0.67-0.95). The other machine learning models performed significantly worse: with an AUC of 0.52 (95% CI 0.37-0.68) for the SVM, 0.56 (95% CI 0.37-0.76) for the logistic regression and an AUC of 0.56 (95% CI 0.37-0.78) for the random forest. In the XGBoost model age, composition of the erector spinae, and severity of lumbar spinal stenosis as were identified as the most important features.
This study represents a novel approach to predicting surgical outcomes in spinal fusion patients. The XGBoost demonstrated a better performance compared with classical models and highlighted the potential contributions of age and paraspinal musculature atrophy as significant factors. These findings have important implications for enhancing patient care through the identification of high-risk individuals and modifiable risk factors. As the incorporation of machine learning algorithms into clinical decision-making continues to gain traction in research and clinical practice, our insights reinforce this trajectory by showcasing the potential of these techniques in forecasting surgical results.
退行性腰椎滑脱症（DLS）是一种常见的脊柱疾病，通常需要手术干预。准确预测手术结果对于指导临床决策至关重要，但由于术后结果的多因素性质，这具有挑战性。传统的风险评估工具存在局限性，随着机器学习的出现，有可能提高术前评估的精度和全面性。
我们旨在开发一种机器学习算法，仅使用术前数据预测退行性腰椎滑脱症（DLS）患者接受脊柱融合手术后的手术结果。
回顾性横断面研究。
接受腰椎融合手术的 DLS 患者。
本研究旨在预测术后 2 年时数字模拟量表（NAS）上出现下腰痛（LBP）≥4 的情况。LBP 评估为患者在接受询问前一周内休息时经历的平均疼痛。NAS 范围从 0 到 10，0 代表无痛，10 代表想象中最严重的疼痛。
我们对在美国机构接受退行性腰椎滑脱融合手术的患者进行了回顾性分析，这些患者于 2016 年 1 月至 2018 年 12 月期间前瞻性入组。选择最初要包含在模型训练中的患者特征，依据是临床专业知识和文献回顾，包括人口统计学特征、合并症和影像学特征。数据使用 60/40 分割分为训练和验证数据集。训练了四种不同的机器学习模型，包括现代 XGBoost 模型、逻辑回归、随机森林和支持向量机（SVM）。根据接收器操作特征（ROC）曲线的曲线下面积（AUC）评估模型。AUC 为 0.7 至 0.8 被认为是公平的，0.8 至 0.9 是良好的，≥0.9 是优秀的。此外，为每个模型计算了校准图和 Brier 分数。
共纳入 135 名患者（66%为女性）。共有 38 名（28%）患者在 2 年后报告 LBP≥4，代表阳性组。XGBoost 模型在验证集中表现出最佳性能，AUC 为 0.81（95%CI 0.67-0.95）。其他机器学习模型的性能明显较差：SVM 的 AUC 为 0.52（95%CI 0.37-0.68），逻辑回归为 0.56（95%CI 0.37-0.76），随机森林为 0.56（95%CI 0.37-0.78）。在 XGBoost 模型中，年龄、竖脊肌成分和腰椎管狭窄的严重程度被确定为最重要的特征。
本研究代表了一种预测脊柱融合患者手术结果的新方法。XGBoost 与经典模型相比表现出更好的性能，并强调了年龄和脊柱旁肌肉萎缩的潜在贡献作为重要因素。这些发现对通过识别高危个体和可改变的风险因素来改善患者护理具有重要意义。随着机器学习算法在研究和临床实践中纳入临床决策的持续推进，我们的见解通过展示这些技术在预测手术结果方面的潜力，加强了这一趋势。
