Circulation Cardiovascular Interventions · 2010 · 76 citations · 22 references
MedicineBiostatisticsCardiologyClinical CardiologyRisk AssessmentSurvival AnalysisAcute Coronary SyndromesCardiac Risk AssessmentIschemic Heart DiseaseMortality ModelingRisk StratificationSurvival Outcomes90-Day MortalityBaseline Cardiac Risk StratificationCardiac Outcome AssessmentCardiovascular Risk StratificationHazard Ratio ModelingHeart RateIschemic Risk StratificationMyocardial Infarction Risk Stratification
Background— Accurate models to predict mortality are needed for risk stratification in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI). Methods and Results— We examined 5745 patients with STEMI undergoing primary PCI in the Assessment of Pexelizumab in Acute Myocardial Infarction Trial within 6 hours of symptom onset. A Cox proportional hazards model incorporating regression splines to accommodate nonlinearity in the log hazard ratio (HR) scale was used to determine baseline independent predictors of 90-day mortality. At 90 days, 271 (4.7%) of 5745 patients died. Independent correlates of 90-day mortality were (in descending order of statistical significance) age (HR, 2.03/10-y increments; 95% CI, 1.80 to 2.29), systolic blood pressure (HR, 0.86/10-mm Hg increments; 95% CI, 0.82 to 0.90), Killip class (class 3 or 4 versus 1 or 2) (HR, 4.24; 95% CI, 2.97 to 6.08), heart rate (>70 beats per minute) (HR, 1.45/10-beat increments; 95% CI, 1.31 to 1.59), creatinine (HR, 1.23/10-μmol/L increments >90 μmol/L; 95% CI, 1.13 to 1.34), sum of ST-segment deviations (HR, 1.25/10-mm increments; 95% CI, 1.11 to 1.40), and anterior STEMI location (HR, 1.47; 95% CI, 1.12 to 1.93) (c-index, 0.82). Internal validation with bootstrapping confirmed minimal overoptimism (c-index, 0.81). Conclusions— Our study provides a practical method to assess intermediate-term prognosis of patients with STEMI undergoing primary PCI, using baseline clinical and ECG variables. This model identifies key factors affecting prognosis and enables quantitative risk stratification that may be helpful in guiding clinical care and for risk adjustment for observational analyses.
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Heart Disease and Stroke Statistics—2009 Update
Donald M. Lloyd‐Jones, Robert J. Adams, Mercedes R. Carnethon et al. · Circulation · 2009 · 3.9K citations
Evaluating the Yield of Medical Tests
Frank E. Harrell · JAMA · 1982 · 3.4K citations
Heart Disease and Stroke Statistics—2009 Update
Donald M. Lloyd‐Jones, Robert J. Adams, Mercedes R. Carnethon et al. · Circulation · 2008 · 3.1K citations · Full text