Clinical predictors easily obtained at presentation predict resource utilization in unstable angina.

OBJECTIVE: To determine if a risk prediction model for patients with unstable angina would predict resource utilization. METHODS AND RESULTS: Four hundred sixty-five consecutive patients admitted for unstable angina to a tertiary care university-based medical center were prospectively evaluated from June 1, 1992, to June 30, 1995. The proportion of patients receiving coronary angiography, coronary angioplasty, and coronary artery bypass grafting were analyzed according to four risk groups on the basis of a previously published model: Group 1, <2% risk of major complication; Group 2, 2.1% to 5% risk; Group 3, 5.1 % to 15% risk; and Group 4, >15.1 % risk. Hospital length of stay and estimated cost of hospitalization based on DRG and specific payer ratio of cost-to-charge were also compared between groups. Multiple linear regression analysis was used to determine the influence of estimated risk and procedures on hospital costs. The four groups were well matched for gender, hypertension, tobacco history, and previous percutaneous transluminal coronary angioplasty and myocardial infarction. Group 4 had a higher incidence of previous coronary bypass grafting (35% vs 10%, p=0.001) and triple vessel or left main coronary artery disease compared with Group 1 (44% vs 13%, p=0.041). Group 4 patients were more likely to be admitted to the coronary care unit compared with Group 2 or Group 1 patients (80% vs Group 1: 51% [p= 0.001]; and vs Group 2: 53% [p=0.001]), more likely to receive heparin (87% vs 71%, p=0.007), and more likely to receive a beta-blocker or calcium channel blocker (89% vs 74%, p=0.008) than Group 1. Coronary angioplasty rates were similar for all groups, but Group 4 patients were more likely to receive coronary bypass grafting than Group 2 or Group 1 (27% vs Group 2: 12%, p=0.004 and vs Group 1: 8%, p=0.002). Hospital length of stay was highest in Group 4 and lowest for Group 1. Average hospital costs were significantly less in Group 3 than in Group 4, but higher than in Group 1. Multivariate analysis determined a dependency of costs on risk group with Group 2 having costs 31.4% (95% CI=9.8 to 57.2), Group 3 46.7% (24, 3 to 73.1), and Group 4 75% (46.9 to 110.7) higher than Group 1. The use of procedures also significantly increased costs, with PTCA-treated patients having a 44.9% (26.7 to 65.7) increase in costs compared with medically treated patients, and surgically treated patients having a 204.7% increase in costs. CONCLUSION: Resource utilization as assessed by the use of revascularization procedures, length of stay, and hospital costs are influenced by patient acuity estimated from a prediction model on the basis of estimated risk of cardiac complications. The model exerts independent influence on cost even after adjustment for various procedures. The use of revascularization procedures, especially coronary artery surgery, remains a large determinant of hospital cost.

Risk stratification in unstable angina. Prospective validation of the Braunwald classification.

OBJECTIVES: To validate the Braunwald classification of unstable angina as a predictor of in-hospital cardiac complications; to determine which factors of the Braunwald classification contributed significantly to this prediction; and to devise a method of combining these predictive factors into an overall odds ratio for complications. DESIGN: A validation cohort of consecutive patients followed prospectively for in-hospital cardiac complications including myocardial infarction and death. SETTING: A community-based academic medical center. PATIENTS: A total of 393 patients admitted consecutively to the coronary care and intermediate care units with unstable angina. MAIN OUTCOME MEASURES: Major cardiac complications including death, myocardial infarction, congestive heart failure, cardiogenic shock, and severe ventricular dysrhythmias. RESULTS: Multiple logistic regression analysis identified four clinical factors used in the Braunwald classification that predicted the in-hospital occurrence of major cardiac complications: (1) myocardial infarction within less than 14 days (odds ratio [OR], 5.72; 95% confidence interval [CI], 1.92 to 16.97); (2) need for intravenous nitroglycerin (OR, 2.33; 95% CI, 1.31 to 4.17); (3) lack of beta-blocker or calcium channel blocker prior to admission (OR, 3.83; 95% CI, 1.55 to 9.42); and (4) baseline ST depression (OR, 2.81; 95% CI, 1.45 to 5.47). Two other clinical factors, diabetes and age, were also significant predictors. Validation of this model using parametric and nonparametric bootstrap techniques revealed excellent agreement between the CIs for adjusted ORs derived from the multiple logistic regression and those derived from the bootstrap. CONCLUSIONS: The classification of unstable angina proposed by Braunwald includes four factors that predict risk of major in-hospital cardiac complications. Specific factors used in this classification can be combined with diabetes and age to better stratify risk of major cardiac complications in this disorder using a simpler model.