Lu Gui, Zhang Guodong, Zhang Jiaqi, Wang Lixiang, Du Baoshun
Department of Neurosurgery, Xinxiang Central Hospital, The Fourth Clinical Hospital of Xinxiang Medical University, Xinxiang, China.
Front Neurol. 2024 Dec 4;15:1502133. doi: 10.3389/fneur.2024.1502133. eCollection 2024.
To construct a nomogram model based on clinical risk factors and CT radiohistological features to predict the prognosis of hypertensive intracerebral hemorrhage (HICH).
A total of 148 patients with HICH from April 2022 to July 2024 were retrospectively selected as the research subjects. According to the modified Rankin scale at the time of discharge, they were divided into good group (Rankin scale score 0-2) and bad group (Rankin scale score 3-6). To compare the clinical data and the changes of CT radiographic characteristics in patients with different prognosis. Relevant factors affecting the prognosis were analyzed, and nomogram model was established based on the influencing factors. The fitting degree, prediction efficiency and clinical net benefit of the nomogram model were evaluated by calibration curve, ROC curve and clinical decision curve (DCA).
Compared with the good group, the hematoma volume in the poor group was significantly increased, the serum thromboxane 2(TXB2) and lysophosphatidic acid receptor 1(LPAR1) levels were significantly increased, and the energy balance related protein (Adropin) level was significantly decreased. The proportions of irregular shape, promiscuous sign, midline displacement, island sign and uneven density were all significantly increased ( < 0.05). In Logistic multivariate analysis, hematoma volume, Adropin, TXB2, LPAR1 and CT radiological features were all independent factors influencing the poor prognosis of HICH ( < 0.05). A nomogram prediction model was established based on the influencing factors. The calibration curve showed that the C-index was 0.820 (95% CI: 0.799-0.861), the goodness of fit test χ = 5.479, and = 0.391 > 0.05, indicating a high degree of fitting. The ROC curve showed that the AUC was 0.896 (95% CI: 0.817-0.923), indicating that this model had high prediction ability. The DCA curve shows that the net benefit of the nomogram model is higher when the threshold probability is 0.1-0.9.
The nomogram prediction model established based on hematoma volume, Adropin, TXB2, LPAR1 and other clinical risk factors as well as CT radiographic characteristics has high accuracy and prediction value in the diagnosis of poor prognosis in patients with HICH.
构建基于临床危险因素和CT放射组织学特征的列线图模型，以预测高血压性脑出血（HICH）的预后。
回顾性选取2022年4月至2024年7月期间的148例HICH患者作为研究对象。根据出院时改良Rankin量表将其分为预后良好组（Rankin量表评分0 - 2分）和预后不良组（Rankin量表评分3 - 6分）。比较不同预后患者的临床资料及CT影像学特征变化。分析影响预后的相关因素，并基于影响因素建立列线图模型。通过校准曲线、ROC曲线和临床决策曲线（DCA）评估列线图模型的拟合度、预测效能及临床净效益。
与预后良好组相比，预后不良组血肿体积显著增大，血清血栓素2（TXB2）和溶血磷脂酸受体1（LPAR1）水平显著升高，能量平衡相关蛋白（Adropin）水平显著降低。不规则形状、混杂征、中线移位、岛征及密度不均匀的比例均显著增加（P < 0.05）。Logistic多因素分析显示，血肿体积、Adropin、TXB2、LPAR1及CT影像学特征均为影响HICH预后不良的独立因素（P < 0.05）。基于影响因素建立列线图预测模型。校准曲线显示C指数为0.820（95%CI：0.799 - 0.861），拟合优度检验χ² = 5.479，P = 0.391 > 0.05，表明拟合度高。ROC曲线显示AUC为0.896（95%CI：0.817 - 0.923），表明该模型具有较高的预测能力。DCA曲线显示，当阈值概率为0.1 - 0.9时，列线图模型的净效益更高。
基于血肿体积、Adropin、TXB2、LPAR1等临床危险因素及CT影像学特征建立的列线图预测模型，在HICH患者预后不良诊断中具有较高的准确性和预测价值。
