Stroke Measures Analysis of pRognostic Testing-Mortality nomogram predicts long-term mortality after ischemic stroke.

IF 6.3 2区 医学 Q1 CLINICAL NEUROLOGY International Journal of Stroke Pub Date : 2024-09-15 DOI:10.1177/17474930241278808
Tae Jung Kim, Ji Sung Lee, Mi Sun Oh, Ji-Woo Kim, Soo-Hyun Park, Kyung-Ho Yu, Byung-Chul Lee, Byung-Woo Yoon, Sang-Bae Ko
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Abstract

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- and 5-year mortality after ischemic stroke.

Methods: 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.

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 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.

Conclusion: 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.

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卒中措施分析认知测试-死亡率(SMART-M)提名图可预测缺血性卒中后的长期死亡率。
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缺血性脑卒中后 1 年和 5 年死亡率的预测方法:我们利用了由健康保险审查与评估服务行政索赔数据库和中风临床研究中心登记数据组成的链接数据集中的数据,这些数据是急性中风患者发病 7 天内的数据。结果是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。根据 Cox 回归分析构建了一个提名图。使用 Harrell's C 指数评估了风险预测模型的性能:本研究共纳入 42207 名缺血性脑卒中患者,平均年龄为 66.6 岁,59.2% 为男性。患者被随机分为训练组(29,916 人)和验证组(12,291 人)。与缺血性卒中患者长期死亡率相关的变量包括年龄、性别、体重指数、卒中严重程度、卒中机制、发病至出院时间、卒中前依赖性、卒中史、糖尿病、高血压、冠心病、慢性肾脏病、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)、药物和出院修正兰肯量表。我们利用所确定的特征构建了一个提名图,从而开发了一个名为 "卒中措施认知测试分析-死亡率(SMART-M)"的预测系统。在开发组和验证组中,提名图的 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方法在预测缺血性卒中患者的长期死亡率方面表现良好。结论:SMART-M 方法在预测缺血性脑卒中患者的长期死亡率方面表现良好,可帮助医生和家属了解患者的长期预后并指导适当的决策过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Stroke
International Journal of Stroke 医学-外周血管病
CiteScore
13.90
自引率
6.00%
发文量
132
审稿时长
6-12 weeks
期刊介绍: The International Journal of Stroke is a welcome addition to the international stroke journal landscape in that it concentrates on the clinical aspects of stroke with basic science contributions in areas of clinical interest. Reviews of current topics are broadly based to encompass not only recent advances of global interest but also those which may be more important in certain regions and the journal regularly features items of news interest from all parts of the world. To facilitate the international nature of the journal, our Associate Editors from Europe, Asia, North America and South America coordinate segments of the journal.
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