背景:预测长期死亡率对于了解缺血性卒中患者的预后和指导治疗决策至关重要。因此,本研究旨在开发和验证预测缺血性卒中后1年和5年死亡率的方法.
方法:我们利用了来自关联数据集的数据,该数据集包括健康保险审查和评估服务的行政索赔数据库和临床研究中心卒中登记数据,用于急性卒中患者发病后7天内。结果是缺血性卒中后的全因死亡率。确定了与缺血性卒中后长期死亡率相关的临床变量。根据Cox回归分析构建了列线图。使用Harrell'sC指数评估风险预测模型的性能。
结果:这项研究包括42,207例缺血性卒中患者,平均年龄为66.6岁,男性占59.2%。将患者随机分为训练组(n=29,916)和验证组(n=12,291)。变量与缺血性卒中患者的长期死亡率相关,包括年龄,性别,身体质量指数,中风严重程度,中风机制,开始到门的时间,笔划前依赖性,中风史,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,以前的他汀类药物治疗,溶栓治疗,如静脉溶栓和血管内再通治疗,药物,和出院改良的Rankiin量表被确定为预测因子。我们通过使用识别的特征构建列线图,开发了一种称为卒中测量结果分析-死亡率(SMART-M)的预测系统。开发组和验证组的列线图的C统计量为0.806(95%置信区间[CI],0.802-0.812)和0.803(95%CI,0.795-0.811),分别。
结论:SMART-M方法在预测缺血性卒中患者长期死亡率方面表现良好。这种方法可以帮助医生和家庭成员了解长期结果并指导适当的决策过程。
BACKGROUND: 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-year and 5-year mortality after ischemic stroke.
METHODS: We utilized 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.
RESULTS: 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 Rankiin 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.
CONCLUSIONS: 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.