Kim Tae Jung, Lee Ji Sung, Oh Mi Sun, Kim Ji-Woo, Park Soo-Hyun, Yu Kyung-Ho, Lee Byung-Chul, Yoon Byung-Woo, Ko Sang-Bae
Department of Neurology, Seoul National University Hospital, Seoul, Korea.
Department of Critical Care Medicine, Seoul National University Hospital, Seoul, Korea.
Int J Stroke. 2025 Jan;20(1):75-84. doi: 10.1177/17474930241278808. Epub 2024 Sep 15.
Predicting long-term mortality is essential for understanding prognosis and guiding treatment decisions in patients with ischemic stroke. Therefore, this study aimed to develop and validate the method for predicting 1- and 5-year mortality after ischemic stroke.
We used data from the linked dataset comprising the administrative claims database of the Health Insurance Review and Assessment Service and the Clinical Research Center for Stroke registry data for patients with acute stroke within 7 days of onset. The outcome was all-cause mortality following ischemic stroke. Clinical variables linked to long-term mortality following ischemic stroke were determined. A nomogram was constructed based on the Cox's regression analysis. The performance of the risk prediction model was evaluated using the Harrell's C-index.
This study included 42,207 ischemic stroke patients, with a mean age of 66.6 years and 59.2% being male. The patients were randomly divided into training (n = 29,916) and validation (n = 12,291) groups. Variables correlated with long-term mortality in patients with ischemic stroke, including age, sex, body mass index, stroke severity, stroke mechanisms, onset-to-door time, pre-stroke dependency, history of stroke, diabetes mellitus, hypertension, coronary artery disease, chronic kidney disease, cancer, smoking, fasting glucose level, previous statin therapy, thrombolytic therapy, such as intravenous thrombolysis and endovascular recanalization therapy, medications, and discharge modified Rankin Scale were identified as predictors. We developed a predictive system named Stroke Measures Analysis of pRognostic Testing-Mortality (SMART-M) by constructing a nomogram using the identified features. The C-statistics of the nomogram in the developing and validation groups were 0.806 (95% confidence interval (CI), 0.802-0.812) and 0.803 (95% CI, 0.795-0.811), respectively.
The SMART-M method demonstrated good performance in predicting long-term mortality in ischemic stroke patients. This method may help physicians and family members understand the long-term outcomes and guide the appropriate decision-making process.
预测长期死亡率对于了解缺血性中风患者的预后和指导治疗决策至关重要。因此，本研究旨在开发并验证预测缺血性中风后1年和5年死亡率的方法。
我们使用了来自关联数据集的数据，该数据集包括健康保险审查与评估服务的行政索赔数据库以及中风临床研究中心对发病7天内急性中风患者的登记数据。结局为缺血性中风后的全因死亡率。确定了与缺血性中风后长期死亡率相关的临床变量。基于Cox回归分析构建了列线图。使用Harrell's C指数评估风险预测模型的性能。
本研究纳入了42207例缺血性中风患者，平均年龄66.6岁，男性占59.2%。患者被随机分为训练组（n = 29916）和验证组（n = 12291）。确定了与缺血性中风患者长期死亡率相关的变量，包括年龄、性别、体重指数、中风严重程度、中风机制、发病至入院时间、中风前依赖程度、中风病史、糖尿病、高血压、冠状动脉疾病、慢性肾病、癌症、吸烟、空腹血糖水平、既往他汀类药物治疗、溶栓治疗（如静脉溶栓和血管内再通治疗）、用药情况以及出院时改良Rankin量表评分，这些被视为预测因素。我们通过使用所确定的特征构建列线图，开发了一个名为中风预后测试-死亡率测量分析（SMART-M）的预测系统。该列线图在开发组和验证组中的C统计量分别为0.806（95%置信区间（CI），0.802 - 0.812）和0.803（95%CI，0.795 - 0.811）。
SMART-M方法在预测缺血性中风患者的长期死亡率方面表现良好。该方法可能有助于医生和家庭成员了解长期预后并指导适当的决策过程。
