Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
World Neurosurg. 2022 Oct;166:e703-e710. doi: 10.1016/j.wneu.2022.07.082. Epub 2022 Jul 22.
C5 palsy is a common postoperative complication after cervical fusion and is associated with increased health care costs and diminished quality of life. Accurate prediction of C5 palsy may allow for appropriate preoperative counseling and risk stratification. We primarily aim to develop an algorithm for the prediction of C5 palsy after instrumented cervical fusion and identify novel features for risk prediction. Additionally, we aim to build a risk calculator to provide the risk of C5 palsy.
We identified adult patients who underwent instrumented cervical fusion at a tertiary care medical center between 2013 and 2020. The primary outcome was postoperative C5 palsy. We developed ensemble machine learning, standard machine learning, and logistic regression models predicting the risk of C5 palsy-assessing discrimination and calibration. Additionally, a web-based risk calculator was built with the best-performing model.
A total of 1024 patients were included, with 52 cases of C5 palsy. The ensemble model was well-calibrated and demonstrated excellent discrimination with an area under the receiver-operating characteristic curve of 0.773. The following features were the most important for ensemble model performance: diabetes mellitus, bipolar disorder, C5 or C4 level, surgical approach, preoperative non-motor neurologic symptoms, degenerative disease, number of fused levels, and age.
We report a risk calculator that generates patient-specific C5 palsy risk after instrumented cervical fusion. Individualized risk prediction for patients may facilitate improved preoperative patient counseling and risk stratification as well as potential intraoperative mitigating measures. This tool may also aid in addressing potentially modifiable risk factors such as diabetes and obesity.
C5 臂丛神经麻痹是颈椎融合术后常见的并发症，会增加医疗保健成本，并降低生活质量。准确预测 C5 臂丛神经麻痹可能有助于进行适当的术前咨询和风险分层。我们主要旨在开发一种用于预测颈椎融合术后 C5 臂丛神经麻痹的算法，并确定用于风险预测的新特征。此外，我们旨在构建一个风险计算器，以提供 C5 臂丛神经麻痹的风险。
我们确定了 2013 年至 2020 年期间在三级护理医疗中心接受器械性颈椎融合术的成年患者。主要结局是术后 C5 臂丛神经麻痹。我们开发了用于预测 C5 臂丛神经麻痹风险的集成机器学习、标准机器学习和逻辑回归模型，评估了区分度和校准度。此外，使用表现最佳的模型构建了一个基于网络的风险计算器。
共纳入 1024 例患者，其中 52 例发生 C5 臂丛神经麻痹。集成模型校准良好，具有出色的区分度，受试者工作特征曲线下面积为 0.773。对集成模型性能最重要的特征包括：糖尿病、双相情感障碍、C5 或 C4 水平、手术入路、术前非运动性神经症状、退行性疾病、融合水平数量和年龄。
我们报告了一种风险计算器，可生成器械性颈椎融合术后患者特定的 C5 臂丛神经麻痹风险。对患者进行个体化风险预测有助于改善术前患者咨询和风险分层，以及潜在的术中缓解措施。该工具还可以帮助解决可改变的危险因素，如糖尿病和肥胖症。
