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本文引用的文献

1
Development and validation of a score to predict life expectancy after carotid endarterectomy in asymptomatic patients.开发和验证一种评分系统,以预测无症状颈动脉内膜切除术患者的预期寿命。
J Vasc Surg. 2018 Jan;67(1):175-182. doi: 10.1016/j.jvs.2017.05.107. Epub 2017 Sep 21.
2
Development of a validated model to predict 30-day stroke and 1-year survival after carotid endarterectomy for asymptomatic stenosis using the Vascular Quality Initiative.利用血管质量倡议开发一个经过验证的模型,以预测无症状性狭窄患者颈动脉内膜切除术后30天内发生中风及1年生存率。
J Vasc Surg. 2017 Aug;66(2):433-444.e2. doi: 10.1016/j.jvs.2017.03.427. Epub 2017 Jun 2.
3
A New Risk Index for Predicting Outcomes among Patients Undergoing Carotid Endarterectomy in Large Administrative Data Sets.一种用于在大型管理数据集中预测接受颈动脉内膜切除术患者预后的新风险指数。
J Stroke Cerebrovasc Dis. 2016 Aug;25(8):1978-83. doi: 10.1016/j.jstrokecerebrovasdis.2016.01.023. Epub 2016 May 20.
4
Extracting a stroke phenotype risk factor from Veteran Health Administration clinical reports: an information content analysis.从退伍军人健康管理局临床报告中提取中风表型风险因素:一项信息内容分析。
J Biomed Semantics. 2016 May 10;7:26. doi: 10.1186/s13326-016-0065-1. eCollection 2016.
5
An externally validated robust risk predictive model of adverse outcomes after carotid endarterectomy.一项经外部验证的颈动脉内膜剥脱术后不良结局的稳健风险预测模型。
J Vasc Surg. 2016 Feb;63(2):345-54. doi: 10.1016/j.jvs.2015.09.003.
6
Long-term clinical outcomes and cardiovascular events after carotid endarterectomy.颈动脉内膜剥脱术后的长期临床结局和心血管事件
Ann Vasc Surg. 2015 Aug;29(6):1265-71. doi: 10.1016/j.avsg.2015.03.031. Epub 2015 May 22.
7
Revascularization of asymptomatic carotid stenosis is not appropriate in patients on dialysis.对于正在接受透析治疗的患者,对无症状性颈动脉狭窄进行血管重建术并不合适。
J Vasc Surg. 2015 Mar;61(3):670-4. doi: 10.1016/j.jvs.2014.10.002.
8
Outcomes after carotid artery stenting in Medicare beneficiaries, 2005 to 2009.2005 年至 2009 年 Medicare 受益人的颈动脉支架置入术治疗结果。
JAMA Neurol. 2015 Mar;72(3):276-86. doi: 10.1001/jamaneurol.2014.3638.
9
Preoperative frailty Risk Analysis Index to stratify patients undergoing carotid endarterectomy.用于对接受颈动脉内膜切除术的患者进行分层的术前衰弱风险分析指数。
J Vasc Surg. 2015 Mar;61(3):683-9. doi: 10.1016/j.jvs.2014.10.009. Epub 2014 Dec 9.
10
Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers.使用局部加权回归平滑法对逻辑回归模型的内部和外部校准进行图形评估。
Stat Med. 2014 Feb 10;33(3):517-35. doi: 10.1002/sim.5941. Epub 2013 Aug 23.

用于改善无症状性颈动脉狭窄患者颈动脉内膜切除术患者选择的风险预测工具。

Risk Prediction Tools to Improve Patient Selection for Carotid Endarterectomy Among Patients With Asymptomatic Carotid Stenosis.

机构信息

Division of General Internal Medicine, Department of Medicine, University of California, San Francisco.

Medical Service, San Francisco Veterans Affairs Medical Center, San Francisco, California.

出版信息

JAMA Surg. 2019 Apr 1;154(4):336-344. doi: 10.1001/jamasurg.2018.5119.

DOI:10.1001/jamasurg.2018.5119
PMID:30624562
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6484803/
Abstract

IMPORTANCE

Randomized clinical trials have demonstrated that patients with asymptomatic carotid stenosis are eligible for carotid endarterectomy (CEA) if the 30-day surgical complication rate is less than 3% and the patient's life expectancy is at least 5 years.

OBJECTIVE

To develop a risk prediction tool to improve patient selection for CEA among patients with asymptomatic carotid stenosis.

DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, veterans 65 years and older who received both carotid imaging and CEA in the Veterans Administration between January 1, 2005, and December 31, 2009 (n = 2325) were followed up for 5 years. Data were analyzed from January 2005 to December 2015. A risk prediction tool (the Carotid Mortality Index [CMI]) based on 23 candidate variables identified in the literature was developed using Veterans Administration and Medicare data. A simpler model based on the number of 4 key comorbidities that were prevalent and strongly associated with 5-year mortality was also developed (any cancer in the past 5 years, chronic obstructive pulmonary disease, congestive heart failure, and chronic kidney disease [the 4C model]). Model performance was assessed using measures of discrimination (eg, area under the curve [AUC]) and calibration. Internal validation was performed by correcting for optimism using 500 bootstrapped samples.

MAIN OUTCOME AND MEASURE

Five-year mortality.

RESULTS

Among 2325 veterans, the mean (SD) age was 73.74 (5.92) years. The cohort was predominantly male (98.8%) and of white race/ethnicity (94.4%). Overall, 29.5% (n = 687) of patients died within 5 years of CEA. On the basis of a backward selection algorithm, 9 patient characteristics were selected (age, chronic kidney disease, diabetes, chronic obstructive pulmonary disease, any cancer diagnosis in the past 5 years, congestive heart failure, atrial fibrillation, remote stroke or transient ischemic attack, and body mass index) for the final logistic model, which yielded an optimism-corrected AUC of 0.687 for the CMI. The 4C model had slightly worse discrimination (AUC, 0.657) compared with the CMI model; however, the calibration curve was similar to the full model in most of the range of predicted probabilities.

CONCLUSIONS AND RELEVANCE

According to results of this study, use of the CMI or the simpler 4C model may improve patient selection for CEA among patients with asymptomatic carotid stenosis.

摘要

重要性

随机临床试验表明,如果无症状颈动脉狭窄患者的 30 天手术并发症率低于 3%,且患者预期寿命至少为 5 年,则可接受颈动脉内膜切除术(CEA)。

目的

开发一种风险预测工具,以改善无症状颈动脉狭窄患者接受 CEA 的选择。

设计、地点和参与者:在这项队列研究中,2005 年 1 月 1 日至 2009 年 12 月 31 日期间,在退伍军人事务部接受颈动脉成像和 CEA 的 65 岁及以上退伍军人(n=2325)进行了为期 5 年的随访。数据分析于 2005 年 1 月至 2015 年 12 月进行。基于文献中确定的 23 个候选变量,使用退伍军人事务部和医疗保险数据开发了一种风险预测工具(颈动脉死亡率指数[CMI])。还开发了一种基于 4 个主要共病的简单模型,这些共病是普遍存在的,并且与 5 年死亡率密切相关(过去 5 年内的任何癌症、慢性阻塞性肺疾病、充血性心力衰竭和慢性肾脏病[4C 模型])。使用区分度指标(例如曲线下面积[AUC])和校准来评估模型性能。通过使用 500 个引导样本进行校正,对内部验证进行了校正。

主要结局和测量

5 年死亡率。

结果

在 2325 名退伍军人中,平均(SD)年龄为 73.74(5.92)岁。该队列主要为男性(98.8%),白人种族/民族(94.4%)。总体而言,687 例(29.5%)患者在 CEA 后 5 年内死亡。基于向后选择算法,选择了 9 项患者特征(年龄、慢性肾脏病、糖尿病、慢性阻塞性肺疾病、过去 5 年内任何癌症诊断、充血性心力衰竭、心房颤动、远处中风或短暂性脑缺血发作、体重指数)用于最终的逻辑模型,该模型的校正后 AUC 为 0.687。4C 模型的判别能力略差(AUC,0.657),但与全模型相比,校准曲线在预测概率的大部分范围内相似。

结论和相关性

根据这项研究的结果,使用 CMI 或更简单的 4C 模型可能会改善无症状颈动脉狭窄患者接受 CEA 的选择。