神经重症患者术后颅内感染列线图模型的构建
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南京医科大学第一附属医院

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Construction of nomogram model for postoperative intracranial infection in patients with severe neurological disease
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    摘要:

    目的:建立预测神经重症患者术后颅内感染风险的列线图模型。方法:回顾性分析2018年1月至2021年01月我院神经外科监护病房行开颅手术的200例患者的临床资料。患者按照7∶3的比例随机分为训练集( n=140)和验证集( n=60)。采用单因素分析和多因素 Logistic 回归筛选神经重症患者术后发生颅内感染的危险因素并构建列线图预测模型。绘制受试者工作特征(receiver operating characteristic,ROC)曲线及决策曲线分析(decision curve analysis, DCA)评价模型的效能及临床净获益。结果:神经重症患者的原发病、脑室外引流(external ventricular drainage,EVD)时间、腰大池引流(lumbar drainage,LD)时间是术后发生颅内感染的危险因素(P<0.05)。绘制列线图模型的ROC曲线显示,训练集和验证集的曲线下面积(area under curve,AUC)分别为0.774(95% CI: 0.695~ 0.853)、0.831(95% CI: 0.725~ 0.936),DCA曲线显示颅内感染发生的预测可提高临床获益率。结论:基于神经重症术后颅内感染的危险因素构建了列线图预测模型,有助于早期筛查神经重症术后颅内感染高危患者,以利于早期诊治,改善患者预后。

    Abstract:

    Objective: This study aims to establish a nomogram model for predicting the risk of postoperative intracranial infection in patients with severe neurological diseases. Methods: A retrospective study was conducted on clinical data from 200 patients who underwent surgical treatment in our hospital"s neurosurgical care unit between January 2018 and January 2021. The patients were randomly divided into a training set (n=140) and a validation set (n=60). Univariate analysis and multivariate logistic regression analysis were used to screen the risk factors, constructing the prediction model by nomogram. The receiver operating characteristic (ROC) curve was plotted to assess the predictive efficacy of the nomogram model for intracranial infection in patients with severe neurological diseases. Additionally, validation of the model and evaluation of its clinical net benefit were performed using decision curve analysis (DCA). Results: The protopathy, external ventricular drainage time and lumbar cisterna drainage time were the risk factors for postoperative intracranial infection (P<0.05). ROC curve of the nomogram model showed that the AUC of the training set and the validation set were 0.774(95%CI: 0.695~ 0.853) and 0.831(95%CI: 0.725~ 0.936), respectively. DCA curves showed that the prediction of intracranial infection could improve the clinical benefit rate. Conclusion: Our nomogram prediction model based on risk factors associated with postoperative intracranial infection in patients with severe neurological diseases offers an effective approach for early identification of high-risk individuals, facilitating prompt diagnosis and treatment while improving the prognosis of patients.

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  • 收稿日期:2023-07-24
  • 最后修改日期:2023-08-31
  • 录用日期:2023-10-18
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