Outcome Prediction Models for Endovascular Treatment of Ischemic Stroke: Systematic Review and External Validation

医学 改良兰金量表 冲程(发动机) 梅德林 随机对照试验 临床试验 接收机工作特性 物理疗法 急诊医学 内科学
作者
Femke Kremers,Esmee Venema,Martijne H C Duvekot,Lonneke S. F. Yo,Reinoud P H Bokkers,Geert J. Lycklama à Nijeholt,Adriaan C.G.M. van Es,Aad van der Lugt,Charles B. L. M. Majoie,James F. Burke,Bob Roozenbeek,Hester F. Lingsma,Diederik W.J. Dippel,Clean Registry Investigators
出处
期刊:Stroke [Ovid Technologies (Wolters Kluwer)]
标识
DOI:10.1161/strokeaha.120.033445
摘要

Background and Purpose: Prediction models for outcome of patients with acute ischemic stroke who will undergo endovascular treatment have been developed to improve patient management. The aim of the current study is to provide an overview of preintervention models for functional outcome after endovascular treatment and to validate these models with data from daily clinical practice. Methods: We systematically searched within Medline, Embase, Cochrane, Web of Science, to include prediction models. Models identified from the search were validated in the MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) registry, which includes all patients treated with endovascular treatment within 6.5 hours after stroke onset in the Netherlands between March 2014 and November 2017. Predictive performance was evaluated according to discrimination (area under the curve) and calibration (slope and intercept of the calibration curve). Good functional outcome was defined as a score of 0–2 or 0–3 on the modified Rankin Scale depending on the model. Results: After screening 3468 publications, 19 models were included in this validation. Variables included in the models mainly addressed clinical and imaging characteristics at baseline. In the validation cohort of 3156 patients, discriminative performance ranged from 0.61 (SPAN-100 [Stroke Prognostication Using Age and NIH Stroke Scale]) to 0.80 (MR PREDICTS). Best-calibrated models were THRIVE (The Totaled Health Risks in Vascular Events; intercept −0.06 [95% CI, −0.14 to 0.02]; slope 0.84 [95% CI, 0.75–0.95]), THRIVE-c (intercept 0.08 [95% CI, −0.02 to 0.17]; slope 0.71 [95% CI, 0.65–0.77]), Stroke Checkerboard score (intercept −0.05 [95% CI, −0.13 to 0.03]; slope 0.97 [95% CI, 0.88–1.08]), and MR PREDICTS (intercept 0.43 [95% CI, 0.33–0.52]; slope 0.93 [95% CI, 0.85–1.01]). Conclusions: The THRIVE-c score and MR PREDICTS both showed a good combination of discrimination and calibration and were, therefore, superior in predicting functional outcome for patients with ischemic stroke after endovascular treatment within 6.5 hours. Since models used different predictors and several models had relatively good predictive performance, the decision on which model to use in practice may also depend on simplicity of the model, data availability, and the comparability of the population and setting.

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
hchnb1234完成签到,获得积分10
1秒前
Bo完成签到,获得积分10
1秒前
Cgy发布了新的文献求助10
2秒前
2秒前
2秒前
2秒前
2秒前
munire发布了新的文献求助10
2秒前
达西西发布了新的文献求助10
2秒前
墨墨完成签到 ,获得积分10
3秒前
虾米发布了新的文献求助10
3秒前
完美世界应助铁豆采纳,获得20
3秒前
Yapi发布了新的文献求助10
3秒前
小石发布了新的文献求助10
4秒前
4秒前
5秒前
潮汐发布了新的文献求助10
5秒前
dengar发布了新的文献求助10
5秒前
许大脚完成签到 ,获得积分10
5秒前
kang发布了新的文献求助10
6秒前
大个应助Shaka采纳,获得10
6秒前
6秒前
6秒前
zzz发布了新的文献求助10
7秒前
肖善若发布了新的文献求助10
8秒前
8秒前
一区哥发布了新的文献求助30
8秒前
YUMI发布了新的文献求助10
8秒前
Q杰完成签到 ,获得积分10
8秒前
爱吃巧克力应助munire采纳,获得10
8秒前
量子星尘发布了新的文献求助10
9秒前
深情安青应助xjc采纳,获得10
9秒前
小二郎应助幽意采纳,获得10
9秒前
10秒前
10秒前
10秒前
10秒前
科研通AI6应助科研通管家采纳,获得10
10秒前
Hello应助科研通管家采纳,获得10
10秒前
小蘑菇应助科研通管家采纳,获得10
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
Superabsorbent Polymers 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5711035
求助须知:如何正确求助?哪些是违规求助? 5202070
关于积分的说明 15263091
捐赠科研通 4863454
什么是DOI,文献DOI怎么找? 2610771
邀请新用户注册赠送积分活动 1561017
关于科研通互助平台的介绍 1518534