Huang Shengsheng, Liang Tuo, Sun Xuhua, Chen Liyi, Jiang Jie, Chen Jiarui, Liu Chong, Zhan Xinli
Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China.
Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China.
World Neurosurg. 2022 Mar;159:e70-e78. doi: 10.1016/j.wneu.2021.12.009. Epub 2021 Dec 8.
Previous studies have retrospectively analyzed the likely causes of cerebrospinal fluid leakage (CSFL) during cervical spine surgery and the management of CSFL after its occurrence. In the present study, we aimed to develop and validate a nomogram for the risk of CSFL in Chinese patients who had undergone cervical decompression and internal fixation (CDIF) surgery.
We performed a retrospective analysis of patients who had undergone CDIF surgery. Of the 1286 included patients, 54 were in the CSFL group and 1232 were in the normal group. The patients were randomly divided into training and validation tests. The risk assessment for CSFL included 21 characteristics. The feature selection for the CSFL model was optimized using the least absolute shrinkage and selection operator regression model in the training test. Multivariate logistic regression analysis was performed to construct the model according to the selected characteristics. The clinical usefulness of the predictive model was assessed using the C-index, calibration curve, and decision curve analysis with identification and calibration.
The risk prediction nomogram included the diagnosis, revision surgery, ossification of the posterior longitudinal ligament, cervical instability, and a history of malignancy in the training test. The model demonstrated high predictive power, with a C-index of 0.914 (95% confidence interval, 0.876-0.951) and an area under the curve of 0.914. The results of the decision curve analysis demonstrated the clinical usefulness of the CSFL risk nomogram when the probability threshold for CSFL was 1%-62%.
Our proposed nomogram for CSFL risk includes the diagnosis, revision surgery, ossification of the posterior longitudinal ligament, cervical instability, and a history of malignancy. The nomogram can be used to evaluate the risk of CSFL for patients undergoing CDIF surgery.
既往研究回顾性分析了颈椎手术中脑脊液漏（CSFL）的可能原因及其发生后的处理方法。在本研究中，我们旨在开发并验证一种用于接受颈椎减压内固定（CDIF）手术的中国患者发生CSFL风险的列线图。
我们对接受CDIF手术的患者进行了回顾性分析。在纳入的1286例患者中，54例属于CSFL组，1232例属于正常组。将患者随机分为训练组和验证组。CSFL的风险评估包括21项特征。在训练组中使用最小绝对收缩和选择算子回归模型对CSFL模型的特征选择进行优化。根据所选特征进行多因素逻辑回归分析以构建模型。使用C指数、校准曲线以及带有识别和校准的决策曲线分析来评估预测模型的临床实用性。
在训练组中，风险预测列线图包括诊断、翻修手术、后纵韧带骨化、颈椎不稳以及恶性肿瘤病史。该模型显示出较高的预测能力，C指数为0.914（95%置信区间，0.876 - 0.951），曲线下面积为0.914。决策曲线分析结果表明，当CSFL的概率阈值为1% - 62%时，CSFL风险列线图具有临床实用性。
我们提出的CSFL风险列线图包括诊断、翻修手术、后纵韧带骨化、颈椎不稳以及恶性肿瘤病史。该列线图可用于评估接受CDIF手术患者发生CSFL的风险。
