Daily Cardiology Research Analysis

BACKGROUND: Atrial cardiopathy is an important cause of embolic stroke and a potential cause of cognitive impairment. Increased left atrial volume indexed to body surface area (LAVi) has been widely used as a marker for atrial cardiopathy. However, because physiological remodeling, for example, due to exercise, may also increase LAVi, it lacks specificity. Left atrial to ventricular volume (LA:LV) ratio has been suggested as an improved marker of atrial cardiopathy, allowing detection of imbalanced, pathological atrial remodeling. We investigated if LA:LV ratio is associated with different sequelae of atrial cardiopathy. METHODS: We analyzed data from 2 cohorts, the population-based UK Biobank cohort (n=38 848) and a cohort of patients with ischemic stroke from the University Hospital Zürich (n=1273). In the UK Biobank cohort, we compared the association of LAVi and LA:LV ratio with risk of incident ischemic stroke or transient ischemic attack ascertained from linked health records, using competing risks survival analysis. We also investigated the association with cognitive function using linear regression models. In the ischemic stroke patient cohort, we compared LAVi and LA:LV ratio for identifying atrial fibrillation/flutter as a cause of stroke. RESULTS: While LAVi was not significantly associated with risk of ischemic stroke/transient ischemic attack (aHR, 1.11 [95% CI, 0.97-1.26]; CONCLUSIONS: We provide evidence that LA:LV ratio is a strong, novel marker of atrial cardiopathy. Hence, LA:LV ratio has the potential to improve the diagnosis of atrial cardiopathy, facilitating the prophylaxis of ischemic stroke and maintaining brain health.

BACKGROUND: Sex differences in the diagnostic yield of invasive coronary angiography (ICA) in non-ST-elevation acute coronary syndrome (NSTE-ACS) are well established, but contemporary population-level data incorporating high-sensitivity troponin era cohorts and cross-national validation remain limited. OBJECTIVES: The objectives of the study were to assess sex differences in diagnostic yield, revascularization rates, and 1-year outcomes in patients undergoing first-time ICA for suspected NSTE-ACS. METHODS: Using data from the Swedish Coronary Angiography and Angioplasty Registry with validation in the Western Denmark Heart Registry. Adults without previously known coronary artery disease (CAD) undergoing first-time ICA for suspected NSTE-ACS were included. The primary outcome was the absence of significant CAD, defined as a normal angiography or <50% stenosis in all major epicardial vessels. Multivariable Poisson regression models were used to estimate adjusted risk ratios (aRRs). Secondary outcomes were 1-year major adverse cardiovascular events, comparing women and men with and without obstructive CAD using Kaplan-Meier estimates and adjusted Cox proportional hazards models. RESULTS: A total of 74,883 and 17,863 patients were included from Sweden and Western Denmark. Women had approximately twice the proportion without significant CAD compared to men in Swedish Coronary Angiography and Angioplasty Registry (40.9% vs 17.2%; aRR: 2.59; 95% CI: 2.51-2.67) and Western Denmark Heart Registry (50.1% vs 25.2%; aRR: 2.01; 95% CI: 1.95-2.08). In women below the age of 50 without any traditional cardiovascular risk factors, 74.8% had no significant CAD. CONCLUSIONS: In 2 population-based cohorts undergoing first-time ICA for suspected NSTE-ACS, 40 to 50% of women had no significant CAD, approximately twice the proportion to men, highlighting ongoing challenges in risk stratification of patients referred for ICA.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) affects 20 million individuals globally, with increased risk of sudden death and heart failure. Although cardiac myosin inhibitors show great promise as disease-specific treatment, current indications are for obstructive HCM. Obstruction is not always well characterized by echocardiography. Artificial intelligence might assist in the improving the underdiagnosis of left ventricular outflow tract (LVOT) obstruction. OBJECTIVES: The authors aimed to develop a deep learning model to detect LVOT obstruction from non-Doppler B-mode echocardiography. METHODS: We identified 2,396 patients with LVOT obstruction and 6,177 control patients matched by age, sex, and septal thickness. LVOT obstruction was defined as the presence of an LVOT gradient or systolic anterior motion of the mitral valve on final echocardiography report. A deep learning model was trained on non-Doppler apical 4-chamber B-mode echocardiographic videos to detect the presence of outflow obstruction identified later in the same study by spectral Doppler. We evaluated our model on held-out test sets from Cedars-Sinai Medical Center, Stanford Healthcare, and Kaiser Permanente Northern California. RESULTS: In a test set of 3,848 videos from Cedars-Sinai Medical Center, our model demonstrated strong performance, detecting LVOT obstruction with area under the receiver operating characteristic curve (AUC) of 0.858 (95% CI: 0.847-0.870). The model demonstrated generalizable performance in the Kaiser Permanente Northern California cohort with AUC of 0.817 (95% CI: 0.740-0.922) and Stanford Healthcare with AUC 0.836 (95% CI: 0.807-0.827). Performance was consistent across patient subgroups, including those with hyperdynamic left ventricular function, pre-existing valvular disease, and small left ventricular cavity size. CONCLUSIONS: In this study, we developed an artificial intelligence model to detect LVOT obstruction from standard apical 4-chamber videos, highlighting patients who may benefit from more detailed cardiac workup for obstructive HCM.