BACKGROUND: Heart failure (HF) with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, and compare phenogroup characteristics and outcomes, from electronic health record data.
METHODS: 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction ≥ 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups.
RESULTS: Three phenogroups were identified: (1) Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; (2) More frail patients, with higher rates of lung disease and atrial fibrillation; (3) Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p < 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF.
CONCLUSIONS: Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles.

OBJECTIVE: The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement.
METHODS: Manually drawn ROIs (mROIs) on end-systolic and end-diastolic images were extracted from reports in a Picture Archiving and Communications System. To reduce observer variability, preprocessed ROIs (pROIs) were delineated using a 41% threshold of the maximal pixel counts of the extracted mROIs and were labeled as ground-truth. Background ROIs were automatically created using an algorithm to identify areas with minimum counts within specified probability areas around the end-systolic ROI. A 2-dimensional U-Net convolutional neural network architecture was trained to generate deep learning-based ROIs (dlROIs) from pROIs. The model\'s performance was evaluated using Lin\'s concordance correlation coefficient (CCC). Bland-Altman plots were used to assess bias and 95% limits of agreement.
RESULTS: A total of 41,462 scans (19,309 patients) were included. Strong concordance was found between LVEF measurements from dlROIs and pROIs (CCC = 85.6%; 95% confidence interval, 85.4%-85.9%), and between LVEF measurements from dlROIs and mROIs (CCC = 86.1%; 95% confidence interval, 85.8%-86.3%). In the Bland-Altman analysis, the mean differences and 95% limits of agreement of the LVEF measurements were -0.6% and -6.6% to 5.3%, respectively, for dlROIs and pROIs, and -0.4% and -6.3% to 5.4% for dlROIs and mROIs, respectively. In 37,537 scans (91%), the absolute LVEF difference between dlROIs and mROIs was <5%.
CONCLUSIONS: Our 2-dimensional U-Net convolutional neural network architecture showed excellent performance in generating LV ROIs from equilibrium radionuclide angiography scans. It may enhance the convenience and reproducibility of LVEF measurements.

We sought to validate the ability of a novel handheld ultrasound device with an artificial intelligence program (AI-POCUS) that automatically assesses left ventricular ejection fraction (LVEF). AI-POCUS was used to prospectively scan 200 patients in two Japanese hospitals. Automatic LVEF by AI-POCUS was compared to the standard biplane disk method using high-end ultrasound machines. After excluding 18 patients due to infeasible images for AI-POCUS, 182 patients (63 ± 15 years old, 21% female) were analyzed. The intraclass correlation coefficient (ICC) between the LVEF by AI-POCUS and the standard methods was good (0.81, p < 0.001) without clinically meaningful systematic bias (mean bias -1.5%, p = 0.008, limits of agreement ± 15.0%). Reduced LVEF < 50% was detected with a sensitivity of 85% (95% confidence interval 76%-91%) and specificity of 81% (71%-89%). Although the correlations between LV volumes by standard-echo and those by AI-POCUS were good (ICC > 0.80), AI-POCUS tended to underestimate LV volumes for larger LV (overall bias 42.1 mL for end-diastolic volume). These trends were mitigated with a newer version of the software tuned using increased data involving larger LVs, showing similar correlations (ICC > 0.85). In this real-world multicenter study, AI-POCUS showed accurate LVEF assessment, but careful attention might be necessary for volume assessment. The newer version, trained with larger and more heterogeneous data, demonstrated improved performance, underscoring the importance of big data accumulation in the field.

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Heart failure with preserved ejection fraction (HFpEF) is associated with high morbidity and mortality. Important risk factors for the development of HFpEF are similar to risk factors for the progression of tricuspid regurgitation (TR), and both conditions frequently coexist and thus is a distinct phenotype or a marker for advanced HF. Many patients with severe, symptomatic atrial secondary TR have been enrolled in current transcatheter device trials, and may represent patients at an advanced stage of HFpEF. Management of HFpEF thus may affect the pathophysiology of TR, and the physiologic changes that occur following transcatheter treatment of TR, may also impact symptoms and outcomes in patients with HFpEF. This review discusses these issues and suggests possible management strategies for these patients.

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UNASSIGNED: PANX1 (pannexin 1), a ubiquitously expressed ATP release membrane channel, has been shown to play a role in inflammation, blood pressure regulation, and myocardial infarction. However, the possible role of PANX1 in cardiomyocytes in the progression of heart failure has not yet been investigated.
UNASSIGNED: We generated a novel mouse line with constitutive deletion of PANX1 in cardiomyocytes (Panx1MyHC6).
UNASSIGNED: PANX1 deletion in cardiomyocytes had no effect on unstressed heart function but increased the glycolytic metabolism and resulting glycolytic ATP production, with a concurrent decrease in oxidative phosphorylation, both in vivo and in vitro. In vitro, treatment of H9c2 cardiomyocytes with isoproterenol led to PANX1-dependent release of ATP and Yo-Pro-1 uptake, as assessed by pharmacological blockade with spironolactone and siRNA-mediated knockdown of PANX1. To investigate nonischemic heart failure and the preceding cardiac hypertrophy, we administered isoproterenol, and we demonstrated that Panx1MyHC6 mice were protected from systolic and diastolic left ventricle volume increases as a result of cardiomyocyte hypertrophy. Moreover, we found that Panx1MyHC6 mice showed decreased isoproterenol-induced recruitment of immune cells (CD45+), particularly neutrophils (CD11b+, Ly6g+), to the myocardium.
UNASSIGNED: Together, these data demonstrate that PANX1 deficiency in cardiomyocytes increases glycolytic metabolism and protects against cardiac hypertrophy in nonischemic heart failure at least in part by reducing immune cell recruitment. Our study implies PANX1 channel inhibition as a therapeutic approach to ameliorate cardiac dysfunction in patients with heart failure.

OBJECTIVE: Left ventricular longitudinal function can be rapidly evaluated by measuring S\' and mitral annular plane systolic excursion (MAPSE) using tissue Doppler imaging. Even when the image quality is poor and the left ventricular endocardium is not visible, S\' and MAPSE can be measured if the mitral annulus is visible. However, the utility of S\' and MAPSE in diagnosing cancer therapy-related cardiac dysfunction (CTRCD) remains unclear. This study aimed to examine the diagnostic performance of S\' and MAPSE and determine appropriate cutoff values.
METHODS: We retrospectively enrolled 279 breast cancer patients who underwent pre- or postoperative chemotherapy with anthracyclines and trastuzumab from April 2020 to November 2022. We compared echocardiographic data before chemotherapy, 6 months after chemotherapy initiation, and 1 year later. CTRCD was defined as a decrease in left ventricular ejection fraction below 50%, with a decrease of ≥10% from baseline or a relative decrease in left ventricular global longitudinal strain (LVGLS) of ≥15%.
RESULTS: A total of 256 participants were included in this study, with a mean age of 50.2 ± 11 years. Fifty-six individuals (22%) developed CTRCD within 1 year after starting chemotherapy. The cutoff value for septal S\' was 6.85 cm/s (AUC = .81, p < .001; sensitivity 74%; specificity 73%), and for MAPSE was 11.7 mm (AUC = .65, p = .02; sensitivity 79%; specificity 45%). None of the cases with septal S\' exceeding 6.85 cm/s had an LVGLS of ≤15%.
CONCLUSIONS: Septal S\' is a useful indicator for diagnosing CTRCD.
CONCLUSIONS: Septal S\' decreased at the same time or earlier than the decrease in LVGLS. The septal S\' demonstrated higher diagnostic ability for CTRCD compared to LVGLS.