Stroke Measures Analysis of pRognostic Testing – Mortality (SMART-M) nomogram predicts long-term mortality after ischaemic stroke

医学 冲程(发动机) 列线图 溶栓 内科学 糖尿病 比例危险模型 冠状动脉疾病 物理疗法 心脏病学 急诊医学 心肌梗塞 机械工程 工程类 内分泌学
作者
Tae Jung Kim,Ji Sung Lee,Mi Sun Oh,Ji‐Woo Kim,Soo-Hyun Park,Kyungho Yu,Byung Chul Lee,Byung‐Woo Yoon,Sang‐Bae Ko
出处
期刊:International Journal of Stroke [SAGE]
标识
DOI:10.1177/17474930241278808
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI

祝大家在新的一年里科研腾飞
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
8秒前
xxyqddx发布了新的文献求助10
13秒前
辛勤尔冬完成签到 ,获得积分10
13秒前
苏格完成签到 ,获得积分10
14秒前
20秒前
20秒前
平常的冷之完成签到,获得积分10
23秒前
8R60d8应助科研通管家采纳,获得20
44秒前
FashionBoy应助科研通管家采纳,获得10
44秒前
彭于晏应助科研通管家采纳,获得10
44秒前
星辰大海应助科研通管家采纳,获得10
44秒前
44秒前
shi0331完成签到,获得积分10
46秒前
吡咯爱成环应助庞可焓采纳,获得10
48秒前
siu完成签到 ,获得积分10
49秒前
冯大夫发布了新的文献求助10
50秒前
SmileLin完成签到,获得积分10
51秒前
54秒前
清脆松完成签到 ,获得积分10
1分钟前
wsl发布了新的文献求助10
1分钟前
马李啸发布了新的文献求助10
1分钟前
Aprial完成签到,获得积分10
1分钟前
1分钟前
文静的电灯胆完成签到,获得积分10
1分钟前
大翟完成签到,获得积分10
1分钟前
简单喀秋莎完成签到,获得积分10
1分钟前
马李啸完成签到,获得积分10
1分钟前
zh完成签到 ,获得积分10
1分钟前
wsl完成签到,获得积分10
1分钟前
SmileLin发布了新的文献求助10
1分钟前
1分钟前
Derek完成签到,获得积分0
2分钟前
2分钟前
领导范儿应助咕咕咕咕采纳,获得10
2分钟前
2分钟前
PATTOM完成签到,获得积分10
2分钟前
PATTOM发布了新的文献求助10
2分钟前
平头哥哥完成签到 ,获得积分10
2分钟前
心灵美的幼荷完成签到 ,获得积分10
2分钟前
无花果应助科研通管家采纳,获得10
2分钟前
高分求助中
Востребованный временем 2500
Production Logging: Theoretical and Interpretive Elements 2000
Agaricales of New Zealand 1: Pluteaceae - Entolomataceae 1500
Early Devonian echinoderms from Victoria (Rhombifera, Blastoidea and Ophiocistioidea) 1000
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 1000
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 1000
The moderating role of collaborative capacity in the relationship between ecological niche-fitness and innovation investment: an ecosystem perspective 800
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3369394
求助须知:如何正确求助?哪些是违规求助? 2988093
关于积分的说明 8730399
捐赠科研通 2670877
什么是DOI,文献DOI怎么找? 1463149
科研通“疑难数据库(出版商)”最低求助积分说明 677118
邀请新用户注册赠送积分活动 668299