Qu Tian, Li Shengde, Zhou Xiang, Miao Qi, Ni Jun, Peng Bin
Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Department of Information Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Neurocrit Care. 2025 Jul 1. doi: 10.1007/s12028-025-02306-0.
Compared to in-hospital ischemic stroke, the prognosis of in-hospital intracranial hemorrhage (IH-ICH) remains poorly understood. We aimed to analyze the risk factors for early poor outcomes and propose a novel predictive nomogram for in-hospital ICH.
We retrospectively analyzed data of patients with in-hospital ICH treated in our hospital between 2014 and 2022. Baseline demographics, comorbidities, clinical characteristics, and outcomes were collected. The early poor outcome was defined as in-hospital death or discharge against medical advice. Univariate and multivariate logistic regressions were used to identify the risk factors and then construct a nomogram. The nomogram was compared with the ICH score in terms of predictive ability.
A total of 196 patients were included; the median age was 57.0 (interquartile range 40.0-67.0) years, and 84 (49.7%) patients were male. Among the cohort, 135 patients had intraparenchymal hemorrhage, 27 had subarachnoid hemorrhage, 1 had intraventricular hemorrhage, 5 had subdural hemorrhage, and 1 had epidural hemorrhage. Overall, 96 (56.8%) patients developed an early poor outcome. Multivariate logistic regression identified prior spontaneous extracranial hemorrhage (ECH), baseline modified Rankin Scale (mRS) score ≥ 4, baseline Glasgow Coma Scale (GCS) score ≤ 8, and systemic disease etiology as independent risk factors for early poor outcomes. The IH-ICH nomogram, developed based on these risk factors, had good calibration and superior predictive performance compared to the conventional ICH score (area under the receiver operating characteristic curve 0.894 vs. 0.743, p < 0.001). Besides, the decision curve analysis curves revealed greater positive net benefit of the model than the ICH score.
Patients with prior ECH, severe coma (GCS score ≤ 8), poor functional status (mRS score ≥ 4), and systemic disease etiology face a significant risk of early poor outcomes. The IH-ICH nomogram incorporating these factors offers a promising tool for identifying high-risk patients with in-hospital ICH, thereby contributing to improved patient care and resource allocation in neurology and critical care settings.
与院内缺血性卒中相比，院内颅内出血（IH-ICH）的预后仍了解不足。我们旨在分析早期不良结局的危险因素，并为院内ICH提出一种新的预测列线图。
我们回顾性分析了2014年至2022年在我院接受治疗的院内ICH患者的数据。收集了基线人口统计学、合并症、临床特征和结局。早期不良结局定义为院内死亡或违反医嘱出院。采用单因素和多因素逻辑回归来识别危险因素，然后构建列线图。将列线图与ICH评分在预测能力方面进行比较。
共纳入196例患者；中位年龄为57.0（四分位间距40.0 - 67.0）岁，84例（49.7%）患者为男性。在该队列中，135例患者为脑实质内出血，27例为蛛网膜下腔出血，1例为脑室内出血，5例为硬膜下出血，1例为硬膜外出血。总体而言，96例（56.8%）患者出现早期不良结局。多因素逻辑回归确定既往自发性颅外出血（ECH）、基线改良Rankin量表（mRS）评分≥4、基线格拉斯哥昏迷量表（GCS）评分≤8以及全身性疾病病因是早期不良结局的独立危险因素。基于这些危险因素开发的IH-ICH列线图具有良好的校准性，与传统ICH评分相比具有更好的预测性能（受试者操作特征曲线下面积为0.894对0.743，p < 0.001）。此外，决策曲线分析曲线显示该模型的净效益比ICH评分更大。
既往有ECH、严重昏迷（GCS评分≤8）、功能状态差（mRS评分≥4）以及全身性疾病病因的患者面临早期不良结局的显著风险。纳入这些因素的IH-ICH列线图为识别院内ICH高危患者提供了一个有前景的工具，从而有助于改善神经病学和重症监护环境中的患者护理和资源分配。
