Objectives: We sought to develop a simple risk score for predicting mortality after primary percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI).
Background: Accurate risk stratification after primary PCI is important. Previous risk scores after reperfusion therapy have incorporated clinical +/- angiographic variables but have not considered baseline left ventricular function. Moreover, prior studies have not been validated against independent databases or studies.
Methods: The databases from the two largest multicenter, randomized AMI trials of primary PCI were utilized for score derivation (the Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications [CADILLAC] trial, n = 2,082) and subsequent validation (the Stent-Primary Angioplasty in Myocardial Infarction [Stent-PAMI] trial, n = 900). Logistic regression and the jackknife procedure were used to select correlates of one-year mortality that were subsequently weighted and integrated into an integer scoring system.
Results: Seven variables selected from the initial multivariate model were weighted proportionally to their respective odds ratio for one-year mortality (age >65 years [2 points], Killip class 2/3 [3 points], baseline left ventricular ejection fraction <40% [4 points], anemia [2 points], renal insufficiency [3 points], triple-vessel disease [2 points], and post-procedural Thrombolysis In Myocardial Infarction flow grade [2 points]). Three strata of risk were defined (low risk, score 0 to 2; intermediate risk, score 3 to 5; and high risk, score >/=6) with excellent prognostic accuracy for survival in the derivation and validation sets (c statistics = 0.83 and 0.81 for 30-day mortality and 0.79 and 0.78 for 1-year mortality, respectively).
Conclusions: In AMI patients treated with primary PCI, seven risk factors readily available at the time of intervention accurately predict short- and long-term mortality. Of note, measurement of baseline left ventricular function is the single most powerful predictor of survival and should be incorporated into risk score models.