ISSN 1004-4140
    CN 11-3017/P

    CTPI血脑屏障完整性和血小板参数在构建老年aSAH术后迟发性脑缺血预测模型中的增益价值

    Gain value of CTPI blood-brain barrier integrity and platelet parameters in establishing a prediction model for delayed cerebral ischemia after aSAH surgery in older adults

      摘要
    • 摘要:
      目的  基于电子计算机断层扫描（CT）灌注成像（CTPI）评价血脑屏障完整性和血小板（PLT）相关指标构建老年动脉瘤性蛛网膜下腔出血（aSAH）术后迟发性脑缺血（DCI）的预测模型。
      方法  选取2022年2月至2024年6月我院收治的281例老年aSAH患者，根据术后14 d内是否发生DCI分为DCI组、无DCI组。比较两组临床资料、影像学资料及生化指标，采用LASSO-Logistic回归方程分析老年aSAH术后并发DCI的危险因素，采用R软件构建列线图模型，绘制受试者工作特征曲线（ROC）、校准曲线分析该模型预测术后并发DCI的价值。
      结果  DCI组mFS分级高分级患者占比、弥漫型低灌注患者占比、平均反映血脑屏障渗透性的体积传递常数（mKtrans）、平均反映脑灌注流出的时间（mTTD）、平均剩余函数达峰时间（mTMax）、血小板平均体积（MPV）、血小板体积指数（PVI）高于无DCI组；弥漫型低灌注、mKtrans、mTTD、mTMax、MPV、PVI是老年aSAH术后发生DCI的危险因素；基于上述危险因素所构建的术后DCI预测模型的一致性指数（C-index）为0.917，ROC的曲下面积（AUC）为0.917（95% CI：0.878~0.955），其具有较高区分度；校准曲线显示，该模型具有较高一致性。
      结论  弥漫型低灌注、mKtrans、mTTD、mTMax、MPV、PVI是老年aSAH术后发生DCI的危险因素，基于此所建立的术后DCI预测模型的区分度、准确度、预测效能较高，可精准筛查出早期DCI的潜在高危个体，为临床防治工作提供参考依据。

       

      Abstract:
      Objective  To construct a predictive model for evaluating blood-brain barrier integrity and platelet-related indicators using computed tomography perfusion imaging (CTPI) in older adult patients with delayed cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage (aSAH) surgery.
      Methods  A total of 281 older adults with aSAH admitted to our hospital between February 2022 and June 2024 were included. Patients were categorized into the DCI and non-DCI groups based on whether DCI occurred within 14 days after surgery. Clinical data, imaging data, and biochemical indicators were compared between the two groups. The LASSO-logistic regression equation was used to identify the risk factors for DCI after aSAH surgery in older adults. A nomograph model was constructed using the R software. The receiver operating characteristic (ROC) and calibration curves were used to analyze the predictive value of the model for postoperative DCI.
      Results  The proportion of patients with high-grade mFS classification, diffuse low perfusion, mean transfer constant (mKtrans; reflecting blood-brain barrier permeability), mean time to peak (mTTD; reflecting cerebral perfusion outflow), average residual function time to peak (mTMax), mean platelet volume (MPV), and platelet volume index (PVI) was significantly higher in the DCI group compared with the non-DCI group (P < 0.05). Diffuse hypoperfusion, mKtrans, mTTD, mTMax, MPV, and PVI were identified as risk factors for postoperative DCI in older adults following aSAH surgery (P < 0.05). The postoperative DCI prediction model constructed based on these risk factors exhibited a consistency index (C-index) of 0.917 and an area under the ROC curve (AUC) of 0.917 (95% CI: 0.878–0.955), indicating high discrimination. The calibration curve confirmed the model’s high consistency.
      Conclusion  Diffuse hypoperfusion, mKtrans, mTTD, mTMax, MPV, and PVI were identified as risk factors for DCI in older adults following aSAH surgery. The predictive model established based on these factors demonstrated high discrimination, accuracy, and predictive power enabling accurate identification of individuals at elevated risk for early DCI. Thus, the model provides a valuable reference for clinical prevention and treatment.

       

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