1Keck School of Medicine, University of Southern California, Los Angeles, California.
2Department of Medical Engineering, California Institute of Technology, Pasadena, California.
Neurosurg Focus. 2023 Jun;54(6):E7. doi: 10.3171/2023.3.FOCUS22651.
Spondylolisthesis is a common operative disease in the United States, but robust predictive models for patient outcomes remain limited. The development of models that accurately predict postoperative outcomes would be useful to help identify patients at risk of complicated postoperative courses and determine appropriate healthcare and resource utilization for patients. As such, the purpose of this study was to develop k-nearest neighbors (KNN) classification algorithms to identify patients at increased risk for extended hospital length of stay (LOS) following neurosurgical intervention for spondylolisthesis.
The Quality Outcomes Database (QOD) spondylolisthesis data set was queried for patients receiving either decompression alone or decompression plus fusion for degenerative spondylolisthesis. Preoperative and perioperative variables were queried, and Mann-Whitney U-tests were performed to identify which variables would be included in the machine learning models. Two KNN models were implemented (k = 25) with a standard training set of 60%, validation set of 20%, and testing set of 20%, one with arthrodesis status (model 1) and the other without (model 2). Feature scaling was implemented during the preprocessing stage to standardize the independent features.
Of 608 enrolled patients, 544 met prespecified inclusion criteria. The mean age of all patients was 61.9 ± 12.1 years (± SD), and 309 (56.8%) patients were female. The model 1 KNN had an overall accuracy of 98.1%, sensitivity of 100%, specificity of 84.6%, positive predictive value (PPV) of 97.9%, and negative predictive value (NPV) of 100%. Additionally, a receiver operating characteristic (ROC) curve was plotted for model 1, showing an overall area under the curve (AUC) of 0.998. Model 2 had an overall accuracy of 99.1%, sensitivity of 100%, specificity of 92.3%, PPV of 99.0%, and NPV of 100%, with the same ROC AUC of 0.998.
Overall, these findings demonstrate that nonlinear KNN machine learning models have incredibly high predictive value for LOS. Important predictor variables include diabetes, osteoporosis, socioeconomic quartile, duration of surgery, estimated blood loss during surgery, patient educational status, American Society of Anesthesiologists grade, BMI, insurance status, smoking status, sex, and age. These models may be considered for external validation by spine surgeons to aid in patient selection and management, resource utilization, and preoperative surgical planning.
脊椎滑脱在美国是一种常见的手术疾病，但对于患者预后的准确预测模型仍然有限。开发能够准确预测术后结果的模型将有助于帮助识别术后过程复杂的患者，并为患者确定适当的医疗保健和资源利用。因此，本研究的目的是开发 k-最近邻（KNN）分类算法，以识别接受神经外科治疗脊椎滑脱的患者中延长住院时间（LOS）风险增加的患者。
从 QOD 脊椎滑脱数据集中查询接受单纯减压或减压加融合治疗退行性脊椎滑脱的患者。查询术前和围手术期变量，并进行 Mann-Whitney U 检验，以确定哪些变量将包含在机器学习模型中。实施了两个 KNN 模型（k = 25），其中标准训练集为 60%，验证集为 20%，测试集为 20%，一个模型包含融合状态（模型 1），另一个模型不包含（模型 2）。在预处理阶段实施了特征缩放，以标准化独立特征。
在 608 名入组患者中，544 名符合预定纳入标准。所有患者的平均年龄为 61.9 ± 12.1 岁（± SD），309 名（56.8%）患者为女性。模型 1 KNN 的总准确率为 98.1%，灵敏度为 100%，特异性为 84.6%，阳性预测值（PPV）为 97.9%，阴性预测值（NPV）为 100%。此外，为模型 1 绘制了接收者操作特征（ROC）曲线，显示总体曲线下面积（AUC）为 0.998。模型 2 的总准确率为 99.1%，灵敏度为 100%，特异性为 92.3%，PPV 为 99.0%，NPV 为 100%，ROC AUC 相同，为 0.998。
总体而言，这些发现表明非线性 KNN 机器学习模型对 LOS 具有极高的预测价值。重要的预测变量包括糖尿病、骨质疏松症、社会经济四分位数、手术持续时间、手术期间估计失血量、患者教育状况、美国麻醉医师协会分级、BMI、保险状况、吸烟状况、性别和年龄。这些模型可由脊柱外科医生进行外部验证，以帮助患者选择和管理、资源利用和术前手术规划。
