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.
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.
Farkouh
Michael E
ME
From Division of Cardiovascular Diseases and Internal Medicine (GSR, DRH, VM), Division of Emergency Medical Services and Internal Medicine (PAS), Division of Biomedical Statistics and Informatics (RJL, HJW), and Section of Health Services Evaluation (KT), Mayo Clinic College of Medicine, Mayo Clinic and Foundation, Rochester, Minnesota; and Mount Sinai School of Medicine Cardiovascular Institute (MEF, AA, SB, LR), New York, New York.
Aneja
Ashish
A
Reeder
Guy S
GS
Smars
Peter A
PA
Bansilal
Sameer
S
Lennon
Ryan J
RJ
Wiste
Heather J
HJ
Razzouk
Louai
L
Traverse
Kay
K
Holmes
David R
DR
Mathew
Verghese
V
D000208:Acute Disease; D000465:Algorithms; D000789:Angina, Unstable / Q000175:diagnosis* / Q000628:therapy; D002318:Cardiovascular Diseases / Q000145:classification / Q000175:diagnosis; D002637:Chest Pain / Q000175:diagnosis* / Q000628:therapy; D015331:Cohort Studies; D016001:Confidence Intervals; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D005500:Follow-Up Studies; D006305:Health Status Indicators; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008910:Minnesota; D017410:Practice Guidelines as Topic / Q000592:standards*; D011446:Prospective Studies; D012189:Retrospective Studies; D018570:Risk Assessment; D012720:Severity of Illness Index; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States