Can the Risk of Postoperative Cerebrospinal Fluid Leakage Be Predicted for Patients Undergoing Cervical Spine Surgery? Development and Evaluation of a New Predictive Nomogram
Can the Risk of Postoperative Cerebrospinal Fluid Leakage Be Predicted for Patients Undergoing Cervical Spine Surgery? Development and Evaluation of a New Predictive Nomogram
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DOI：
10.1016/j.wneu.2021.12.009
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发表时间：
2022-03-04
影响因子：
2
通讯作者：
Zhan, Xinli
中科院分区：
文献类型：
--
作者：
Huang, Shengsheng;Liang, Tuo;Zhan, Xinli
目的：既往研究回顾性分析颈椎手术中脑脊液漏（CSFL）的可能原因及发生后的处理方法。在本研究中，我们旨在建立并验证中国颈椎减压内固定（CDIF）手术患者CSFL风险的nomogram。方法：我们对接受CDIF手术的患者进行回顾性分析。在纳入的1286例患者中，CSFL组54例，正常组1232例。患者随机分为训练组和验证组。CSFL的风险评估包括21个特征。在训练测试中，采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。根据选取的特征进行多元logistic回归分析，构建模型。采用c指数、校正曲线和决策曲线分析对预测模型的临床有效性进行评估，并进行鉴定和校正。结果：风险预测图包括训练测试中的诊断、翻修手术、后纵韧带骨化、颈椎不稳定和恶性肿瘤史。该模型具有较高的预测能力，c指数为0.914(95%置信区间为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风险图具有临床应用价值。结论：我们提出的CSFL风险图包括诊断、翻修手术、后纵韧带骨化、颈椎不稳定和恶性肿瘤史。该图可用于评估接受CDIF手术的患者发生CSFL的风险。
OBJECTIVE: 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.METHODS: 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.RESULTS: 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%.CONCLUSIONS: 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.