Suppr超能文献

预测严重的无症状颈动脉狭窄及随后的卒中与心血管疾病风险。

Prediction of Severe Baseline Asymptomatic Carotid Stenosis and Subsequent Risk of Stroke and Cardiovascular Disease.

机构信息

Department of Neurology (M.H.F.P., L.J.K.), University Medical Center Utrecht, Utrecht University, the Netherlands.

Department of Vascular Medicine (S.H.J.H., F.L.J.V.), University Medical Center Utrecht, Utrecht University, the Netherlands.

出版信息

Stroke. 2024 Nov;55(11):2632-2640. doi: 10.1161/STROKEAHA.123.046894. Epub 2024 Sep 25.

Abstract

BACKGROUND

Risk models to identify patients at high risk of asymptomatic carotid artery stenosis (ACAS) can help in selecting patients for screening, but long-term outcomes in these patients are unknown. We assessed the diagnostic and prognostic value of the previously published Prevalence of ACAS (PACAS) risk model to detect ACAS at baseline and to predict subsequent risk of stroke and cardiovascular disease (CVD) during follow-up.

METHODS

We validated the discrimination and calibration of the PACAS risk model to detect severe (≥70% narrowing) ACAS with patients from the Reduction of Atherothrombosis for Continued Health registry. We subsequently calculated the incidence rates of stroke and CVD (fatal and nonfatal stroke or myocardial infarction or vascular death) during follow-up in 4 risk groups (low, medium, high, and very high, corresponding to sum scores of ≤9, 10-13, 14-17, and ≥18, respectively).

RESULTS

Among 26 384 patients, aged between 45 and 80 years, without prior carotid procedures, 1662 (6.3%) had severe baseline ACAS. During ≈70 000 patient-years of follow-up, 1124 strokes and 2484 CVD events occurred. Discrimination of the PACAS model was 0.67 (95% CI, 0.65-0.68), and calibration showed adequate concordance between predicted and observed risks of severe baseline ACAS after recalibration. Significantly higher incidence rates of stroke (<0.011) and CVD (<0.0001) during follow-up were found with increasing PACAS risk groups. Among patients with high PACAS sum score of ≥14 (corresponding to 27.7% of all patients), severe baseline ACAS prevalence was 11.4%. In addition, 56.6% of incident strokes and 64.9% of incident CVD events occurred in this group.

CONCLUSIONS

The PACAS risk model can reliably identify patients at high risk of severe baseline ACAS. Incidence rates of stroke and CVD during follow-up were significantly higher in patients with high PACAS sum scores. Selective screening of patients with high PACAS sum scores may help to prevent future stroke or CVD.

摘要

背景

识别无症状颈动脉狭窄(ACAS)高危患者的风险模型有助于筛选患者,但这些患者的长期结局尚不清楚。我们评估了先前发表的 Prevalence of ACAS(PACAS)风险模型在基线时检测 ACAS 的诊断和预后价值,并预测随访期间中风和心血管疾病(CVD)的后续风险。

方法

我们使用 Reduction of Atherothrombosis for Continued Health 登记处的患者验证了 PACAS 风险模型在检测严重(≥70%狭窄)ACAS 中的区分度和校准度。随后,我们计算了 4 个风险组(低、中、高和极高,分别对应总分≤9、10-13、14-17 和≥18)在随访期间中风和 CVD(致命和非致命中风或心肌梗死或血管死亡)的发生率。

结果

在 26384 名年龄在 45 至 80 岁之间、无颈动脉手术史的患者中,1662 名(6.3%)基线时存在严重的 ACAS。在大约 70000 患者-年的随访期间,发生了 1124 例中风和 2484 例 CVD 事件。PACAS 模型的区分度为 0.67(95%CI,0.65-0.68),经过重新校准后,预测和观察到的基线严重 ACAS 风险之间的校准显示出良好的一致性。随着 PACAS 风险组的增加,随访期间中风(<0.011)和 CVD(<0.0001)的发生率显著升高。在 PACAS 总分≥14 分的高风险组(占所有患者的 27.7%)中,基线严重 ACAS 的患病率为 11.4%。此外,该组中有 56.6%的中风事件和 64.9%的 CVD 事件发生。

结论

PACAS 风险模型可以可靠地识别出基线严重 ACAS 高危患者。随访期间中风和 CVD 的发生率在 PACAS 总分较高的患者中显著升高。对 PACAS 总分较高的患者进行选择性筛查可能有助于预防未来的中风或 CVD。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76f5/11518973/f8ab842958d3/str-55-2632-g003.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验