Department of Radiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China; Department of Medical Imaging, Chongqing University Central Hospital, The Fourth People's Hospital of Chongqing, Chongqing, China.
Department of Radiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Ann Palliat Med. 2021 Dec;10(12):12789-12800. doi: 10.21037/apm-21-3569.
Hematoma expansion (HE) is an important risk factor for poor prognosis in patients with hypertensive intracerebral hemorrhage. This study aimed to establish a nomogram model for predicting HE, and evaluate the model.
The clinical data and plain computed tomography (CT) scan signs of 341 patients with hypertensive intracerebral hemorrhage were retrospectively analyzed. According to the development of HE, the patients were divided into an HE group (100 cases) and a non-HE group (241 cases). The clinical data and CT scan signs of the patients in these two groups were compared. Variables that had statistically significant differences were included in the multivariate logistic regression analysis to screen for independent predictors of HE and establish a nomogram model. The discrimination, calibration, and clinical practicability of this model were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and a decision curve analysis (DCA), respectively. Finally, the internal validation of this model was performed using the bootstrap method.
The time interval from disease onset to the first CT [odds ratio (OR) =0.807, 95% confidence interval (CI): 0.665-0.979], volume of the hematoma at the first CT (OR =1.017, 95% CI: 1.001-1.033), irregular shape of the hematoma (OR =2.458, 95% CI: 1.355-4.456), swirl sign (OR =2.308, 95% CI: 1.239-4.298), and blend sign (OR =2.509, 95% CI: 1.304-4.830) were independent predictors of HE (all P<0.05). These factors were used to establish a nomogram model. The area under the ROC curve of the model was 0.762 (95% CI: 0.703-0.821). The results of the Hosmer-Lemeshow test and calibration curves showed that the predictive probabilities of the model fit the actual probabilities well. The DCA results showed that the domain probability range of the model was wide. The internal validation results showed that the C-index was 0.751, and the model's discrimination was good.
The nomogram model established in this study had good discrimination, calibration, and clinical practicability. The model could serve as an intuitive and reliable guiding tool for the clinical identification of HE risk of hypertensive intracerebral hemorrhage.
血肿扩大（HE）是高血压性脑出血患者预后不良的重要危险因素。本研究旨在建立预测 HE 的列线图模型，并对该模型进行评估。
回顾性分析 341 例高血压性脑出血患者的临床资料和颅脑 CT 平扫征象。根据 HE 的发生情况，将患者分为 HE 组（100 例）和非 HE 组（241 例）。比较两组患者的临床资料和 CT 扫描征象。将具有统计学差异的变量纳入多因素 logistic 回归分析，筛选出 HE 的独立预测因素，并建立列线图模型。采用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）分别评估该模型的区分度、校准度和临床实用性。最后，采用 bootstrap 方法对该模型进行内部验证。
发病至首次 CT 的时间间隔[比值比（OR）=0.807，95%置信区间（CI）：0.6650.979]、首次 CT 血肿量（OR=1.017，95%CI：1.0011.033）、血肿形态不规则（OR=2.458，95%CI：1.3554.456）、漩涡征（OR=2.308，95%CI：1.2394.298）和混杂征（OR=2.509，95%CI：1.3044.830）是 HE 的独立预测因素（均 P<0.05）。将这些因素纳入建立列线图模型。模型的 ROC 曲线下面积为 0.762（95%CI：0.7030.821）。Hosmer-Lemeshow 检验和校准曲线的结果表明，模型的预测概率与实际概率拟合良好。DCA 结果表明，该模型的适用域概率范围较宽。内部验证结果显示，C 指数为 0.751，模型的区分度较好。
本研究建立的列线图模型具有良好的区分度、校准度和临床实用性。该模型可为临床识别高血压性脑出血 HE 风险提供直观、可靠的指导工具。
