Validated risk stratification model accurately predicts low risk in patients with unstable angina

• Published in: Journal of the American College of Cardiology 2000-11, Vol.36 (6), p.1803-1808
• Main Authors: Calvin, James E, Klein, Lloyd W, VandenBerg, Elizabeth J, Meyer, Peter, Parrillo, Joseph E
• Format: Article
• Language: English
• Subjects: Aged; Angina, Unstable - complications; Angina, Unstable - epidemiology; Biological and medical sciences; Cardiology. Vascular system; Coronary heart disease; Heart; Humans; Length of Stay; Medical sciences; Middle Aged; Models, Statistical; Prospective Studies; Risk Assessment
• ISSN: 0735-1097; 1558-3597
• DOI: 10.1016/S0735-1097(00)00977-3

BACKGROUND In the mid 1990s, two unstable angina risk prediction models were proposed but neither has been validated on separate population or compared. OBJECTIVES The purpose of this study was to compare patient outcome among high, medium and low risk unstable angina patients defined by the Agency for Health Care Policy and Research (AHCPR) guideline to similar risk groups defined by a validated model from our institution (RUSH). METHODS Four hundred sixteen patients consecutively admitted to the hospital with unstable angina between January 1, 1995, and December 31, 1997, were prospectively evaluated for risk factors. The presence of major adverse events such as myocardial infarction (MI), death and heart failure was assessed for each patient by chart review. RESULTS The composite end point of heart failure, MI or death occurred in 3% and 5% of the RUSH and AHCPR low risk categories, respectively, and in 8% and 10% of AHCPR and RUSH high risk categories, respectively. Recurrent ischemic events were best predicted by the RUSH model (high: 24% vs medium: 12% and low: 10%, p = 0.029), but not by the AHCPR model (high: 14% vs medium: 13% and low: 9%, p = 0.876). The RUSH model identified five times more low risk patients than the AHCPR model. CONCLUSIONS Both models identify patients with low and high event rates of MI, death or heart failure. However, the RUSH model allowed for five times more patients to be candidates for outpatient evaluation (low risk) with a similar observed event rate to the AHCPR model; also, the RUSH model more successfully predicted ischemic complications. We conclude that the RUSH model can be used clinically to identify patients for early noninvasive evaluation, thereby improving cost effectiveness of care.