Introduction:
Guidelines to prevent sudden cardiac death (SCD) following acute coronary syndrome (ACS) are widely based on cutoffs defined on left ventricular ejection fraction (LVEF) with limited use of other available data.
Methods:
We investigated the improvement in predicting post-ACS SCD using a multi-factorial model that integrates an assessment of left ventricular dysfunction through echocardiography with a broader set of other parameters collected routinely during hospitalization for ACS. Patients in the MERLIN-TIMI36 trial were admitted within 48 hours of ischemic symptoms for non-ST-elevation ACS and followed for a median of 348 days. SCD was adjudicated by a blinded clinical events committee. Data from 4,404 patients with LVEF and other clinical parameters routinely collected during index hospitalization (demographic, comorbidity, history, laboratory, electrocardiographic, and medication variables) were used to train and validate a logistic regression model to predict SCD using stepwise backward elimination and leave-one-out cross-validation.
Results:
The stepwise elimination process retained age, history of congestive heart failure, ST depression, beta blocker use, BNP, LVEF, and ischemia and ventricular tachycardia on continuous ECG as variables in the model. The model achieved significant improvements in discrimination, calibration and reclassification relative to LVEF, and demonstrated further utility in stratifying patients with mild/moderate left ventricular dysfunction or normal systolic function (Table 1). The model also resulted in higher sensitivity without increasing false positives relative to the LVEF<=30% rule (38% increase in correct predictions of SCD).
Conclusions:
Risk stratification for post-ACS SCD is significantly improved using multi-factorial models to integrate information in LVEF with other clinical parameters routinely collected during hospitalization.
SENTINEL EVENTS PRIOR TO SUDDEN CARDIAC DEATH AMONG CLINICALLY STABLE ACUTE CORONARY SYNDROME PATIENTS: INSIGHTS FROM THE IMPROVE-IT TRIAL
