The CADILLAC risk score accurately identifies patients at low risk for in-hospital mortality and adverse cardiovascular events following ST elevation myocardial infarction.

BACKGROUND: The CADILLAC risk score was developed to identify patients at low risk for adverse cardiovascular events following ST elevation myocardial infarction (STEMI) treated with primary percutaneous coronary intervention (PPCI). METHODS: We performed a single center retrospective review of STEMI hospitalizations treated with PPCI from 2014 to 2018. Patients were stratified using the CADILLAC risk score into low risk, intermediate risk and high risk groups. Patients presenting with cardiac arrest or cardiogenic shock were excluded from the study. The primary outcome was adverse clinical events during initial hospitalization. Secondary outcomes were adverse clinical events at 30 days and 1 year following index hospitalization. RESULTS: The study included 341 patients. Compared to patients with a low CADILLAC score, adverse clinical events were similar in the intermediate risk group during hospitalization (OR 1.23, CI 0.37-4.05, p 0.733) and at 30 days (OR 2.27, CI 0.93-5.56, p 0.0733) while adverse clinical events were significantly elevated in the high risk group during hospitalization (OR 4.75, CI 1.91-11.84, p 0.0008) and at 30 days (OR 8.73, CI 4.02-18.96, p < 0.0001). At 1 year follow-up, compared to the low risk CADILLAC group (9.4% adverse clinical event rate), cumulative adverse clinical events were significantly higher in the intermediate risk group (22.9% event rate, OR 2.86, CI 1.39-5.89, p 0.0044) and in the elevated risk group (58.6% event rate, OR 13.67, CI 6.81-27.43, p < 0.0001). The mortality rate was 0% for patients defined at low risk by CADILLAC score during hospitalization, as well up to 1 year follow up. On receiver operating curve analysis, discrimination of in-hospital adverse clinical events was fair using CADILLAC (C = 0.66, odds ratio 1.18; 95% CI 1.04-1.33; p = 0.0064) with somewhat better discrimination at 30-day follow-up (C = 0.719) and 1-year follow-up (C = 0.715). CONCLUSION: Patients defined as low risk by the CADILLAC score following a STEMI were associated with lower mortality and adverse clinical event rates during hospitalization and up to 1 year following STEMI when compared to those with an intermediate or high CADILLAC score.

Abnormal P-wave terminal force in lead V1 is associated with low left atrial appendage ejection velocity.

BACKGROUND: Sinus P-wave abnormalities have been associated stroke in people with atrial fibrillation (AF). The majority of AF-related strokes occur from left atrial appendage (LAA) thromboembolism. Dysfunction of the left atrium (LA) and left atrial appendage (LAA) can increase rates of thromboembolic stroke. We studied whether abnormal P wave terminal force in V1 (aPTFV1) is associated with decreased LAA ejection velocity (LAAV) on transesophageal echocardiography (TEE). METHODS: We conducted a retrospective cross-sectional study reviewing patients at a tertiary care medical center who underwent TEE in sinus rhythm and had an interpretable sinus ECG within 12 months of TEE. Participants were excluded for complex congenital heart disease, age <18, cardiac transplantation, and chronic atrial pacing. Logistic regression analysis was used to estimate the odds ratios of LAAV<40 cm/s for aPTFV1. RESULTS: In our final cohort of 169 patients (28% of which had LAAV <40), the multivariate odds ratio of aPTFV1 for LAAV<40 cm/s after adjustment for CHA2DS2VASc variables, heart rate during TEE, history of atrial arrhythmias, and left atrial volume index was 2.24 (95% CI of 1.13-6.00). CONCLUSION: Abnromal P-wave terminal force in lead V1 is associated with low LAAV after adjustment for potential confounders. Future research is needed for validation of our findings and determination of clinical utility.

Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models.

Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. Here, we develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We construct personalized three-dimensional computer models of post-infarction hearts from patients' clinical magnetic resonance imaging data and assess the propensity of each model to develop arrhythmia. In a proof-of-concept retrospective study, the virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent SCD and avoid unnecessary ICD implantations.

Myocardial Infarct Segmentation From Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology.

Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology.