Murphy Julianne, Silva Pinheiro do Nascimento Juliana, Houskamp Ethan J, Wang Hanyin, Hutch Meghan, Liu Yuzhe, Faigle Roland, Naidech Andrew M
Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University, 633 N St. Clair St. 20th floor, Chicago, IL, USA.
Department of Neurology, Northwestern Medicine, Chicago, IL, USA.
Neurocrit Care. 2025 Feb;42(1):39-47. doi: 10.1007/s12028-024-02067-2. Epub 2024 Aug 6.
The objective of this study was to define clinically meaningful phenotypes of intracerebral hemorrhage (ICH) using machine learning.
We used patient data from two US medical centers and the Antihypertensive Treatment of Acute Cerebral Hemorrhage-II clinical trial. We used k-prototypes to partition patient admission data. We then used silhouette method calculations and elbow method heuristics to optimize the clusters. Associations between phenotypes, complications (e.g., seizures), and functional outcomes were assessed using the Kruskal-Wallis H-test or χ test.
There were 916 patients; the mean age was 63.8 ± 14.1 years, and 426 patients were female (46.5%). Three distinct clinical phenotypes emerged: patients with small hematomas, elevated blood pressure, and Glasgow Coma Scale scores > 12 (n = 141, 26.6%); patients with hematoma expansion and elevated international normalized ratio (n = 204, 38.4%); and patients with median hematoma volumes of 24 (interquartile range 8.2-59.5) mL, who were more frequently Black or African American, and who were likely to have intraventricular hemorrhage (n = 186, 35.0%). There were associations between clinical phenotype and seizure (P = 0.024), length of stay (P = 0.001), discharge disposition (P < 0.001), and death or disability (modified Rankin Scale scores 4-6) at 3-months' follow-up (P < 0.001). We reproduced these three clinical phenotypes of ICH in an independent cohort (n = 385) for external validation.
Machine learning identified three phenotypes of ICH that are clinically significant, associated with patient complications, and associated with functional outcomes. Cerebellar hematomas are an additional phenotype underrepresented in our data sources.
本研究的目的是使用机器学习来定义脑出血（ICH）具有临床意义的表型。
我们使用了来自美国两个医疗中心的患者数据以及急性脑出血-II抗高血压治疗临床试验的数据。我们使用k-原型算法对患者入院数据进行划分。然后，我们使用轮廓系数法计算和肘部法启发式算法来优化聚类。使用Kruskal-Wallis H检验或χ检验评估表型、并发症（如癫痫发作）和功能结局之间的关联。
共有916例患者；平均年龄为63.8±14.1岁，426例患者为女性（46.5%）。出现了三种不同的临床表型：血肿较小、血压升高且格拉斯哥昏迷量表评分>12的患者（n = 141，26.6%）；血肿扩大且国际标准化比值升高的患者（n = 204，38.4%）；血肿体积中位数为24（四分位间距8.2 - 59.5）mL的患者，这些患者更常见为黑人或非裔美国人，且可能发生脑室内出血（n = 186，35.0%）。临床表型与癫痫发作（P = 0.024）、住院时间（P = 0.001）、出院处置（P < 0.001）以及3个月随访时的死亡或残疾（改良Rankin量表评分4 - 6）（P < 0.001）之间存在关联。我们在一个独立队列（n = 385）中重现了这三种ICH临床表型以进行外部验证。
机器学习识别出三种具有临床意义的ICH表型，它们与患者并发症以及功能结局相关。小脑血肿是我们数据源中代表性不足的另一种表型。
