A low RH signal detected by EndoPAT, consistent with endothelial dysfunction, was associated with higher AE rate during follow-up. L_RHI was an independent predictor of AE. Non-invasive assessment of peripheral vascular function may be useful for the identification of patients at risk for cardiac AEs.

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Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone.

Background-Myocardial late gadolinium enhancement (LGE) on contrast-enhanced magnetic resonance imaging (CE-MRI) of patients with hypertrophic cardiomyopathy (HCM) has been suggested to represent intramyocardial fibrosis and, as such, an adverse prognostic risk factor. We evaluated the characteristics of LGE on CE-MRI and explored whether LGE among patients with HCM was associated with genetic testing, severe symptoms, ventricular arrhythmias, or sudden cardiac death (SCD LGE positive (event rate of 0.94%/y, Pϭ0.01 versus LGE negative). Two additional heart failure-related deaths were recorded among LGE-positive patients. Univariate associates of SCD or appropriate ICD discharge were positive LGE (Pϭ0.002) and presence of nonsustained ventricular tachycardia (Pϭ0.04). The association of LGE with events remained significant after controlling for other risk factors. Conclusions-In patients with HCM, presence of LGE on CE-MRI was common and more prevalent among gene-positive patients.LGE was not associated with severe symptoms. However, LGE was strongly associated with surrogates of arrhythmia and remained a significant associate of subsequent SCD and/or ICD discharge after controlling for other variables. If replicated, LGE may be considered an important risk factor for sudden death in patients with HCM. (Circ Heart Fail. 2010;3:51-58.)

Background-Multidetector computed tomography (MDCT) has high diagnostic value for detecting or excluding coronary artery stenosis. We examined performance characteristics of MDCT for diagnosing or excluding an acute coronary syndrome in patients presenting to the emergency department (ED) with possible ischemic chest pain and examined relation to clinical outcome during a 15-month follow-up period. Methods and Results-We prospectively studied 58 patients (56Ϯ10 years of age, 36% female) with chest pain possibly ischemic in origin and no new ECG changes or elevated biomarkers. The patients underwent 64-slice contrast-enhanced MDCT, which showed normal coronary vessels (no or trivial atheroma) in 15 patients, nonobstructive plaque in 20 (MDCT-negative patients), and obstructive coronary disease (Ն50% luminal narrowing) in 23 (MDCT-positive group . During a 15-month follow-up period, no deaths or myocardial infarctions occurred in the 35 patients discharged from the ED after initial triage and MDCT findings. One patient underwent late percutaneous coronary intervention (late major adverse cardiovascular events rate, 2.8%). Overall, ED MDCT sensitivity for predicting major adverse cardiovascular events (death, myocardial infarction, or revascularization) during hospitalization and follow-up was 92% (12/13), specificity was 76% (34/45), positive predictive value was 52% (12/23), and negative predictive value was 97% (34/35). Conclusions-We found that 64-slice cardiac MDCT is a potentially valuable diagnostic tool in ED patients with chest pain of uncertain origin, providing early direct noninvasive visualization of coronary anatomy. ED MDCT had high positive predictive value for diagnosing acute coronary syndrome, whereas a negative MDCT study predicted a low rate of major adverse cardiovascular events and favorable outcome during follow-up.

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Aims Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA). Methods and results The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features. Conclusion A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.

This large international radiation dose survey demonstrates considerable reduction of radiation exposure in coronary CTA during the last decade. However, the large inter-site variability in radiation exposure underlines the need for further site-specific training and adaptation of contemporary cardiac scan protocols.