Jiang Wenqi, Li Xinyang, Tan Yutao, Guo Wen, Zhang Shihong, Wu Bo, Ma Lu, Liu Ming, Xu Mangmang
Center of Cerebrovascular Diseases, Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
West China School of Medical, Sichuan University, Chengdu, Sichuan Province, China.
Ann Med. 2025 Dec;57(1):2530226. doi: 10.1080/07853890.2025.2530226. Epub 2025 Jul 13.
To study the association between cerebral small vessel diseases (CSVD) and unfavorable hematoma morphology in primary intracerebral hemorrhage (ICH).
Patients with primary ICH who were admitted to West China Hospital of Sichuan University from March 2012 to January 2021 were consecutively included. The unfavorable hematoma morphology included any hypodensity, any irregularity, black hole, blend sign, Barras shape score ≥3, Barras density score ≥3, immature hematoma and combined Barras total score (CBTS) ≥4. The combined hematoma morphology score (CHMS) was evaluated by allocating 1 point for the presence of each of the mentioned unfavorable hematoma morphology. Multivariable binary logistic and ordinal regressions, together with unsupervised machine learning, were used to determine the association between CSVD and unfavorable hematoma morphology features.
Univariable analysis showed that older age and hypertension were associated with white matter hyperintensities (WMH) presence. Regarding hematoma morphology, Barras density score ≥3, CBTS ≥4 and higher CHMS were associated with WMH absence (all < 0.05). Multivariable regression indicated that lower WMH presence were significantly associated with both CBTS ≥4 and higher CHMS after correcting for confounders. Futhermore, we employed unsupervised machine learning using K-means algorithm to cluster patients into different groups according to CSVD burden, and the results showed that cluster with higher CSVD burden was less likely to be associated with unfavorable hematoma morphology such as black hole and higher CHMS after correcting for confounders.
Lower CSVD burden might be associated with higher incidence of unfavorable hematoma morphology features, such as CTBS ≥4 and higher CHMS.
研究脑小血管病（CSVD）与原发性脑出血（ICH）中不良血肿形态之间的关联。
连续纳入2012年3月至2021年1月在四川大学华西医院住院的原发性ICH患者。不良血肿形态包括任何低密度影、任何不规则形态、黑洞征、融合征、巴拉斯形状评分≥3、巴拉斯密度评分≥3、未成熟血肿以及巴拉斯总分（CBTS）≥4。通过对每种上述不良血肿形态的存在情况给予1分来评估联合血肿形态评分（CHMS）。采用多变量二元逻辑回归和有序回归以及无监督机器学习来确定CSVD与不良血肿形态特征之间的关联。
单变量分析显示，年龄较大和高血压与白质高信号（WMH）的存在相关。关于血肿形态，巴拉斯密度评分≥3、CBTS≥4以及较高的CHMS与无WMH相关（均P<0.05）。多变量回归表明，在校正混杂因素后，较低的WMH存在与CBTS≥4和较高的CHMS均显著相关。此外，我们使用K均值算法进行无监督机器学习，根据CSVD负担将患者聚类为不同组，结果显示在校正混杂因素后，CSVD负担较高的聚类与黑洞等不良血肿形态和较高的CHMS相关的可能性较小。
较低的CSVD负担可能与不良血肿形态特征（如CTBS≥4和较高的CHMS）的较高发生率相关。
