Clinical risk stratification in the emergency department predicts long-term cardiovascular outcomes in a population-based cohort presenting with acute chest pain: primary results of the Olmsted county chest pain study

急诊科的临床风险分层可预测以急性胸痛为主要症状的人群队列的长期心血管结局:奥姆斯特德县胸痛研究的主要结果

阅读:1

Abstract

The long-term cardiovascular outcomes of a population-based cohort presenting to the emergency department (ED) with chest pain and classified with a clinical risk stratification algorithm are not well documented. The Olmsted County Chest Pain Study is a community-based study that included all consecutive patients presenting with chest pain consistent with unstable angina presenting to all EDs in Olmsted County, Minnesota. Patients were classified according to the Agency for Health Care Policy and Research (AHCPR) criteria. Patients with ST elevation myocardial infarction and chest pain of noncardiac origin were excluded. Main outcome measures were major adverse cardiovascular and cerebrovascular events (MACCE) at 30 days and at a median follow-up of 7.3 years, and mortality through a median of 16.6 years.The 2271 patients were classified as follows: 436 (19.2%) as high risk, 1557 (68.6%) as intermediate risk, and 278 (12.2%) as low risk. Thirty-day MACCE occurred in 11.5% in the high-risk group, 6.2% in the intermediate-risk group, and 2.5% in the low-risk group (p < 0.001). At 7.3 years, significantly more MACCE were recorded in the intermediate-risk (hazard ratio [HR], 1.91; 95% confidence intervals [CI], 1.33-2.75) and high-risk groups (HR, 2.45; 95% CI, 1.67-3.58). Intermediate- and high-risk patients demonstrated a 1.38-fold (95% CI, 0.95-2.01; p = 0.09) and a 1.68-fold (95% CI, 1.13-2.50; p = 0.011) higher mortality, respectively, compared to low-risk patients at 16.6 years. At 7.3 and at 16.6 years of follow-up, biomarkers were not incrementally predictive of cardiovascular risk.In conclusion, a widely applicable rapid clinical algorithm using AHCPR criteria can reliably predict long-term mortality and cardiovascular outcomes. This algorithm, when applied in the ED, affords an excellent opportunity to identify patients who might benefit from a more aggressive cardiovascular risk factor management strategy.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。