Daily Cardiology Research Analysis

BACKGROUND: The analysis of cardiovascular borders (CVBs) in chest x-rays (CXRs) traditionally relied on subjective assessment and does not have established normal ranges. OBJECTIVES: The authors aimed to develop a deep learning-based method for quantifying CVBs on CXRs and to explore its clinical utility. METHODS: This study used a prevalidated deep learning to analyze CVBs. A total of 96,129 normal CXRs from 4 sites were used to establish age- and sex-specific normal ranges of CVBs. The quantified CVBs were standardized into z-scores for newly inputted CXRs. The clinical utility of the z-score analysis was tested using 44,567 diseased CXRs from 3 sites (9,964 valve disease; 32,900 coronary artery disease; 1,299 congenital heart disease; 294 aortic aneurysm; 110 mediastinal mass). RESULTS: For distinguishing valve disease from normal controls, the area under the receiver operating characteristic curve for the cardiothoracic ratio was 0.80 (95% CI: 0.80-0.80), while the combination of right atrium and left ventricle borders had an area under the receiver operating characteristic curve of 0.83 (95% CI: 0.83-0.83). Between mitral and aortic stenosis, z-scores of CVBs were significantly different in the left atrial appendage (1.54 vs 0.33, P < 0.001), carinal angle (1.10 vs 0.67, P < 0.001), and ascending aorta (0.63 vs 1.02, P < 0.001), reflecting disease pathophysiology. Cardiothoracic ratio was independently associated with a 5-year risk of death or myocardial infarction in the coronary artery disease (z-score ≥2, adjusted HR: 3.73 [95% CI: 2.09-6.64], reference z-score <-1). CONCLUSIONS: Deep learning-derived z-score analysis of CXR showed potential in classifying and stratifying the risk of cardiovascular abnormalities.

BACKGROUND: The intramural site of origin is a major cause of ablation failure of ventricular arrhythmias, and the optimal strategy is unclear. This study investigated the efficacy of a stepwise ablation approach for intramural outflow tract (OT) premature ventricular complexes (PVCs) guided by mapping of the septal coronary venous system. METHODS: Consecutive patients with OT PVCs were included, in whom an intramural origin was confirmed by demonstration of earliest activation in a septal coronary vein. Radiofrequency ablation was performed from the closest endocardial site in the left ventricular OT or right ventricular OT independent of the local activation time. If there was no suppression by endocardial ablation, then retrograde transvenous ethanol infusion with a single- or double-balloon technique was performed, targeting the earliest septal coronary vein. If venous anatomy was not suitable for ethanol ablation or if this failed, then bipolar ablation was performed. RESULTS: Sixty patients (age 61±12 years; 78% men) were included. The mean QRS duration of the PVC was 150.8±17.6 ms with a maximum deflection index of 0.51±0.11, and the most common ECG pattern was a left bundle branch block with inferior axis and V3 transition (63%), followed by a right bundle branch block with inferior axis and no transition (27%). Earliest ventricular activation (28.6±11.2 ms before QRS) was recorded in the left ventricular annular vein in 15 cases and a septal perforator vein in 45 cases. Acute PVC suppression at the end of the procedure was achieved in all cases. In 87% of cases (n=52), endocardial ablation from the endocardial left ventricular OT, right ventricular OT, or both was successful in eliminating the PVC. In the remaining 8 patients, the PVC was eliminated with ethanol infusion (n=7) and bipolar ablation (n=1). Complications included one case of pericardial effusion related to venous mapping. During follow-up (17±24 months), the PVC burden was reduced from 28±12% to 2.3±4.7%, and long-term success (≥80% burden reduction) was 88%. CONCLUSIONS: Most intramural OT PVCs can be successfully eliminated with endocardial ablation adjacent to the earliest intramural activation site. A high success rate is achieved when following a stepwise approach, with bailout ablation strategies required in a minority of cases.

BACKGROUND: Abdominal computed tomography (CT) is commonly performed in adults. Abdominal aortic calcification (AAC) can be visualized and quantified using artificial intelligence (AI) on CTs performed for other clinical purposes (opportunistic CT). We sought to investigate the value of AI-enabled AAC quantification as a predictor of coronary artery disease and its association with cardiovascular events. METHODS: A fully automated AI algorithm to quantify AAC from the diaphragm to aortic bifurcation using the Agatston score was retrospectively applied to a cohort of patient that underwent both noncontrast abdominal CT for routine clinical care and cardiac CT for coronary artery calcification (CAC) assessment. Subjects were followed for a median of 36 months for major adverse cardiovascular events (MACE, composite of death, myocardial infarction [MI], ischemic stroke, coronary revascularization) and major coronary events (MCE, MI or coronary revascularization). The 10-year Predicting Risk of cardiovascular disease EVENTs (PREVENT) cardiovascular risk score was calculated. RESULTS: Our cohort included 3599 patients (median age 61 years, 49% female, 73% white) with an evaluable abdominal and cardiac CT. There was a positive correlation between presence and severity of AAC and CAC (r = 0.56, P < .001). AAC showed excellent discriminatory power for detecting or ruling out any CAC (AUC for PREVENT risk score 0.701 [0.683-0.718]; AUC for PREVENT plus AAC 0.782 [0.767-0.797]; P < .001). There were 324 MACE, of which 246 were MCE. Following adjustment for the PREVENT score, the presence of AAC was associated with a significant risk of MACE (adjHR 2.26, 95% CI 1.67-3.07, P < .001) and MCE (adjHR 2.58, 95% CI 1.80-3.71, P < .001). A doubling of the AAC score resulted in an 11% increase in the risk of MACE and a 13% increase in the risk of MCE. CONCLUSIONS: Using opportunistic abdominal CTs, assessment of AAC using a fully automated AI algorithm, predicted CAC and was independently associated with cardiovascular events. These data support the use of opportunistic imaging for cardiovascular risk assessment. Future studies should investigate whether opportunistic imaging can help guide appropriate cardiovascular prevention strategies.