Clinical risk stratification in the XXXX predicts long-term cardiovascular outcomes in a population-based cohort presenting with acute @entity662 : primary results of the Olmsted county @entity662 study.

@entity662 @entity1 @entity341 @entity1459 @entity10260 @entity158 @entity583 @entity65
The long-term cardiovascular outcomes of a population-based cohort presenting to the @entity341 ( @entity341 ) with @entity662 and classified with a clinical risk stratification algorithm are not well documented. The Olmsted County Chest @entity158 Study is a community-based study that included all consecutive @entity1 presenting with @entity662 consistent with @entity1459 presenting to all EDs in Olmsted County, Minnesota. @entity1 were classified according to the Agency for Health Care Policy and Research (AHCPR) criteria. @entity1 with @entity10260 elevation @entity583 and @entity662 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 @entity1 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 @entity1 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 @entity1 at 16.6 years. At 7.3 and at 16.6 years of follow-up, biomarkers were not incrementally predictive of @entity65 .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 @entity341 , affords an excellent opportunity to identify @entity1 who might benefit from a more aggressive @entity65 factor management strategy.
