开颅术后颅内感染风险列线图预测模型的构建

Construction of a risk nomogram prediction model for intracranial infection after craniotomy

  • 摘要:
    目的 分析开颅术后患者颅内感染的危险因素, 建立预测风险的列线图模型并验证。
    方法 对泉州市第一医院神经外科2022年1月-2024年12月收治的1 185例开颅手术患者开展回顾性分析研究, 依据住院期间颅内感染发生情况分为感染组(n=106)与非感染组(n=1 079), 经logistic回归分析筛选术后颅内感染的危险因素, 并构建列线图预测模型。
    结果 logistic回归分析结果显示, 手术时间延长、术前糖尿病、腰大池引流置管、脑室引流>7 d、人工硬脑膜应用及术中出血量≥300 ml为术后颅内感染的危险因素(P<0.05), 并基于此6项指标建立列线图预测模型。经验证, 该列线图模型预测效能良好:一致性指数(C-index)和受试者工作特征曲线下面积(AUC)均为0.804, 区分度优异;Hosmer-Lemeshow检验(χ2=3.218, P=0.781)及校准曲线平均绝对误差(MAE)=0.009均显示其校准性能精准。经临床决策曲线(DCA)验证, 提示当列线图模型的预测概率阈值设定于0.050~0.800时, 其临床净收益达到最大化, 具有最佳临床应用价值。
    结论 本研究构建的开颅术后颅内感染风险列线图预测模型具有良好的预测效能及临床适用性, 相关预测指标科学且易获得, 能为开颅手术后颅内感染的判定提供有效参考。

     

    Abstract:
    OBJECTIVE To analyze the risk factors for intracranial infection in patients after craniotomy, establish a nomogram model for predicting risk and validate it.
    METHODS A retrospective analysis was conducted on 1 185 patients who underwent craniotomy in the Department of Neurosurgery at Quanzhou First Hospital from Jan. 2022 to Dec. 2024. These patients were divided into an infection group (n=106) and a non-infection group (n=1 079) based on the occurrence of intracranial infection during hospitalization. Logistic regression analysis was used to screen for risk factors of postoperative intracranial infection, and a nomogram prediction model was constructed.
    RESULTS Logistic regression analysis showed that prolonged operation time, preoperative diabetes, lumbar cisterna drainage catheter placement, ventricular drainage >7 days, artificial dura mater application and intraoperative blood loss ≥300 ml were risk factors for postoperative intracranial infection (P < 0.05). Based on these six indicators, a nomogram prediction model was established. The nomogram model demonstrated good predictive performance upon validation: both the concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) were 0.804, indicating excellent discrimination. The Hosmer-Lemeshow test (χ2=3.218, P=0.781) and the calibration curve mean absolute error(MAE)=0.009 both indicated precise calibration performance. The validation by the decision curve analysis (DCA) suggested that when the prediction probability threshold of the nomogram model was set between 0.050 and 0.800, the clinical net benefit reached its maximum, indicating optimal clinical application value.
    CONCLUSIONS The risk nomogram prediction model for intracranial infection after craniotomy constructed in this study demonstrates good predictive performance and clinical applicability. The relevant predictive indicators are scientific and easily obtainable, providing an effective reference for the diagnosis of intracranial infection after craniotomy.

     

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