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This Korean nationwide, multicenter, noninterventional, prospective cohort study aimed to analyze physician adherence to guideline-recommended therapy for heart failure (HF) with reduced ejection fraction (HFrEF) and its effect on patient-reported outcomes (PROs). Patients diagnosed with or hospitalized for HFrEF within the previous year were enrolled. Treatment adherence was considered optimal when all 3 categories of guideline-recommended medications (angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, or angiotensin receptor-neprilysin inhibitors; beta-blockers; and mineralocorticoid receptor antagonists) were prescribed and suboptimal when ≤ 2 categories were prescribed. The 36-Item Short Form Survey (SF-36) scores were compared at baseline and 6 months between the 2 groups. Overall, 854 patients from 30 hospitals were included. At baseline, the optimal adherence group comprised 527 patients (61.7%), whereas during follow-up, the optimal and suboptimal adherence groups comprised 462 (54.1%) and 281 (32.9%) patients, respectively. Patients in the suboptimal adherence group were older, with a lower body mass index, and increased comorbidities, including renal dysfunction. SF-36 scores were significantly higher in the optimal adherence group for most domains (P < 0.05). This study showed satisfactory physician adherence to contemporary treatment for HFrEF. Optimal adherence to HF medication significantly correlated with better PROs., (© 2022. The Author(s).)

Background and Objectives: Evidence for effectiveness of early change from angiotensin II receptor blockers (ARBs) or angiotensin-converting enzyme inhibitors (ACEIs) to sacubitril/valsartan is lacking. We aimed to investigate whether early changes to sacubitril/valsartan could improve outcomes in patients with nonischemic dilated cardiomyopathy (DCM) in real-world practice. Materials and Methods : A total of 296 patients with nonischemic DCM who were treated with ARB or ACEI continuously (group A, n = 150) or had their medication switched to sacubitril/valsartan (group S, n = 146) were included. The sacubitril/valsartan group was divided into early change (within 60 days, group S/E, n = 59) and late change (group S/L, n = 87) groups. Changes in echocardiographic parameters from the time of initial diagnosis to the last follow-up were analyzed. Results : Patients in group S showed greater left ventricular (LV) end-diastolic dimension (EDD) (group A vs. S, 61.7 ± 7.4 vs. 66.5 ± 8.0, p < 0.001) and lower LV ejection fraction (LVEF) (28.9 ± 8.2% vs. 23.9 ± 7.5%, p < 0.001) than those in group A at initial diagnosis. During a median follow-up of 76 months, patients in group S/E, ∆ LVEF (%) and ∆ LVESD (mm) were significantly improved compared with those in patients in group A (group A vs. S/E, ∆ LVEF, p = 0.036; ∆ LVESD, p = 0.023) or S/L (group S/E vs. S/L, ∆ LVEF, p = 0.05; ∆ LVESD, p = 0.005). Among patients whose medications were switched to sacubitril/valsartan, those with an earlier change showed a significant correlation with greater LVEF improvement (r = -0.367, p < 0.001) and LV reverse remodeling (r = 0.277, p < 0.001). Conclusions : in patients with nonischemic DCM, an early switch to sacubitril/valsartan was associated with greater improvement in LV function. Patients might benefit in terms of LV function by early switching to sacubitril/valsartan.

Although heart failure with reduced ejection fraction (HFrEF) is a common clinical syndrome and can be modified by the administration of appropriate medical therapy, there is no adequate tool available to perform reliable, economical, early-stage screening. To meet this need, we developed an interpretable artificial intelligence (AI) algorithm for HFrEF screening using electrocardiography (ECG) and validated its performance. This retrospective cohort study included two hospitals. An AI algorithm based on a convolutional neural network was developed using 39,371 ECG results from 17,127 patients. The internal validation included 3,470 ECGs from 2,908 patients. Furthermore, we conducted external validation using 4,362 ECGs from 4,176 patients from another hospital to verify the applicability of the algorithm across different centers. The end-point was to detect HFrEF, defined as an ejection fraction <40%. We also visualized the regions in 12 lead ECG that affected HFrEF detection in the AI algorithm and compared this to the previously documented literature. During the internal and external validation, the areas under the curves of the AI algorithm using a 12 lead ECG for detecting HFrEF were 0.913 (95% confidence interval, 0.902-0.925) and 0.961 (0.951-0.971), respectively, and the areas under the curves of the AI algorithm using a single-lead ECG were 0.874 (0.859-0.890) and 0.929 (0.911-0.946), respectively. The deep learning-based AI algorithm performed HFrEF detection well using not only a 12 lead but also a single-lead ECG. These results suggest that HFrEF can be screened not only using a 12 lead ECG, as is typical of a conventional ECG machine, but also with a single-lead ECG performed by a wearable device employing the AI algorithm, thereby preventing irreversible disease progression and mortality., Competing Interests: Disclosure: The authors have no conflicts of interest to report., (Copyright © ASAIO 2020.)

Aims: We sought to determine which echocardiographic markers of left ventricular (LV) remodeling and diastolic dysfunction can contribute as incremental and independent prognostic information in addition to current clinical risk markers of ischemic LV systolic dysfunction in the Surgical Treatment for Ischemic Heart Failure (STICH) trial., Methods and Results: The cohort consisted of 1511 of 2136 patients in STICH for whom baseline transmitral Doppler (E/A ratio) could be measured by an echocardiographic core laboratory blinded to treatment and outcomes, and prognostic value of echocardiographic variables was determined by a Cox regression model. E/A ratio was the most significant predictor of mortality amongst diastolic variables with lowest mortality for E/A closest 0.8, although mortality was consistently low for E/A 0.6 to 1.0. Mortality increased for E/A < 0.6 and > 1.0 up to approximately 2.3, beyond which there was no further increase in risk. Larger LV end-systolic volume index (LVESVI) and E/A < 0.6 and > 1.0 had incremental negative effects on mortality when added to a clinical multivariable model, where creatinine, LVESVI, age, and E/A ratio accounted for 74% of the prognostic information for predicting risk. LVESVI and E/A ratio were stronger predictors of prognosis than New York Heart Association functional class, anemia, diabetes, history of atrial fibrillation, and stroke., Conclusions: Echocardiographic markers of advanced LV remodeling and diastolic dysfunction added incremental prognostic value to current clinical risk markers. LVESVI and E/A ratio outperformed other markers and should be considered as standard in assessing risks in ischemic heart failure. E/A closest to 0.8 was the most optimal filling pattern.

Purpose of Review: Diastolic stress echocardiography may help facilitate the attribution of exertional dyspnea to cardiac and non-cardiac disease. It represents a non-invasive hemodynamic test to assess the patients with unexplained dyspnea. It can improve the diagnosis of heart failure with preserved ejection fraction (HFpEF) or diastolic heart failure., Recent Findings: A number of studies have validated exercise E/e' as a measure of left ventricular (LV) filling pressure against invasively measured LV filling pressure using simultaneous exercise echocardiography-catheterization studies. Addition of E/e' during exercise echocardiography improved sensitivity for diagnosis of HFpEF compared with resting assessment alone, and its specificity can be improved if tricuspid regurgitation velocity also increases above the normal range with exercise. The independent prognostic value of exercise E/e' has also been well delineated in a number of studies. Diastolic stress exercise echocardiography should be considered for all patients with unexplained or exertional dyspnea and normal diastolic filling pressure or grade 1 diastolic dysfunction on resting echocardiography. Addition of diastolic assessment with exercise echocardiography improves the sensitivity of the test in patients with dyspnea and there are sufficient data to integrate diastolic exercise test into our clinical practice.

Aims: This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF)., Methods and Results: 12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospitals enrolled to the Korean AHF registry were used as performance test data. The endpoints were in-hospital, 12-month, and 36-month mortality. We compared the DAHF performance with the Get with the Guidelines-Heart Failure (GWTG-HF) score, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and other machine-learning models by using the test data. Area under the receiver operating characteristic curve of the DAHF were 0.880 (95% confidence interval, 0.876-0.884) for predicting in-hospital mortality; these results significantly outperformed those of the GWTG-HF (0.728 [0.720-0.737]) and other machine-learning models. For predicting 12- and 36-month endpoints, DAHF (0.782 and 0.813) significantly outperformed MAGGIC score (0.718 and 0.729). During the 36-month follow-up, the high-risk group, defined by the DAHF, had a significantly higher mortality rate than the low-risk group(p<0.001)., Conclusion: DAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models., Competing Interests: The authors have declared that no competing interests exist.

Purpose of Review: Heart transplantation is the best option for irreversible and critically advanced heart failure. However, limited donor pool, the risk of rejection, infection, and right ventricular dysfunction in short-term post-transplant period, as well as, the development of coronary allograft vasculopathy and malignancy in the long-term post-transplant period limits the utility of heart transplantation for all comers with advanced heart failure. Therefore, selection of appropriate candidates is very important for the best short and long-term prognosis. In this article, we discuss the principles of selection of candidates and compare to the recently updated International Society for Heart and Lung Transplantation (ISHLT) listing criteria with the goal of updating current clinical practice., Recent Findings: We found that while most of the recommendations in the new listing criteria are continuous with the previous criteria, updated recommendations are made on the risk stratification models in choosing transplantation candidates. Recommendation on hepatic dysfunction is not directly included in the updated ISHLT listing criteria; however, adoption of the Model for End-stage Liver Disease (MELD) score and modified MELD scores in the evaluation of risk are suggested in recent studies. In conclusion, evaluation of patient selection for heart transplantation should be comprehensive and individualized with respect to indications and the risk of comorbidities of candidates. With the advancement of mechanical circulatory support (MCS), the selection of heart transplantation candidate is continuously evolving and widened. MCS as bridge to candidacy should be considered when the candidate has potentially reversible risk factors for transplantation.

Background: Impaired nitric oxide-mediated pulmonary vascular tone is commonly found in heart failure with reduced ejection fraction (HFrEF), and is associated with derangement of left ventricular (LV) hemodynamics and decreased exercise capacity, which may be reversed by PDE5 inhibitor. This study investigated the effects of a new, long-acting PDE5 inhibitor on LV hemodynamics and exercise capacity in HFrEF., Methods: Patients with chronic HFrEF on optimal medical therapy for >30 days before enrollment were randomly assigned to placebo or udenafil at a dose of 50mg 2x/day for the first 4 weeks followed by 100mg 2x/day for the next 8 weeks. All patients underwent cardiopulmonary exercise echocardiography before and after the 12-week treatment., Results: Improvement of subjective functional capacity was more frequently reported in the udenafil group (P = 0.002). Also, a higher increase in peak VO2 (Δpeak VO2, 21.6% (6.9 ~ 106.4%) vs 1.9% (-15.7 ~ 21.0%) in the placebo group, P = 0.04) and a larger decrease in ventilatory efficiency were observed in the udenafil group (Δ-6.4 ± 9.7 vs Δ1.9 ± 12.1 in the placebo group, P = 0.03). Regarding LV systolic function, the extent of increment in LV ejection fraction was significantly greater in the udenafil group (6.6 ± 6.4% vs 2.3 ± 4.8% in the placebo group, P = 0.02). In the udenafil group, an echocardiographic surrogate of LV filling pressure was more prominently decreased (P = 0.006) along with a significant reverse remodeling of left atrial volume index (57 ± 25mL at baseline to 44 ± 23 at 12th week, P = 0.04) and a progressive fall in B-type natriuretic peptide level (589 ± 679pg/mL at baseline to 220 ± 225pg/mL at 12th week, P < 0.001), indicating LV diastolic function improvement. Udenafil was well tolerated without excess of adverse events compared to placebo., Conclusions: Udenafil improves LV systolic/diastolic functions and exercise capacity in conjunction with established conventional pharmacotherapy, without significant adverse events in HFrEF., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: We developed and validated an artificial intelligence (AI)-enabled smartwatch ECG to detect heart failure-reduced ejection fraction (HFrEF). Methods: This was a cohort study involving two hospitals (A and B). We developed the AI in two steps. First, we developed an AI model (ECGT2T) to synthesize ten-lead ECG from the asynchronized 2-lead ECG (Lead I and II). ECGT2T is a deep learning model based on a generative adversarial network, which translates source ECGs to reference ECGs by learning styles of the reference ECGs. For this, we included adult patients aged ≥18 years from hospital A with at least one digitally stored 12-lead ECG. Second, we developed an AI model to detect HFrEF using a 10 s 12-lead ECG. The AI model was based on convolutional neural network. For this, we included adult patients who underwent ECG and echocardiography within 14 days. To validate the AI, we included adult patients from hospital B who underwent two-lead smartwatch ECG and echocardiography on the same day. The AI model generates a 10 s 12-lead ECG from a two-lead smartwatch ECG using ECGT2T and detects HFrEF using the generated 12-lead ECG. Results: We included 137,673 patients with 458,745 ECGs and 38,643 patients with 88,900 ECGs from hospital A for developing the ECGT2T and HFrEF detection models, respectively. The area under the receiver operating characteristic curve of AI for detecting HFrEF using smartwatch ECG was 0.934 (95% confidence interval 0.913–0.955) with 755 patients from hospital B. The sensitivity, specificity, positive predictive value, and negative predictive value of AI were 0.897, 0.860, 0.258, and 0.994, respectively. Conclusions: An AI-enabled smartwatch 2-lead ECG could detect HFrEF with reasonable performance. [ABSTRACT FROM AUTHOR]

Background: Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically‐ill patients and for guiding decision making. The existing models, however, cannot be used during initial treatment or screening. This study aimed to derive and validate an echocardiography‐based mortality prediction model for HD using deep learning (DL). Methods: In this multicenter retrospective cohort study, the subjects were admitted adult (age ≥ 18 years) HD patients who underwent echocardiography. The outcome was in‐hospital mortality. We extracted predictor variables from echocardiography reports using text mining. We developed deep learning‐based prediction model using derivation data of a hospital A. And we conducted external validation using echocardiography report of hospital B. We conducted subgroup analysis of coronary heart disease (CHD) and heart failure (HF) patients of hospital B and compared DL with the currently used predictive models (eg, Global Registry of Acute Coronary Events (GRACE) score, Thrombolysis in Myocardial Infarction score (TIMI), Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and Get With The Guidelines‐Heart Failure (GWTG‐HF) score). Results: The study subjects comprised 25 776 patients with 1026 mortalities. The areas under the receiver operating characteristic curve (AUROC) of the DL model were 0.912, 0.898, 0.958, and 0.913 for internal validation, external validation, CHD, and HF, respectively, and these results significantly outperformed other comparison models. Conclusions: This echocardiography‐based deep learning model predicted in‐hospital mortality among HD patients more accurately than existing prediction models and other machine learning models. [ABSTRACT FROM AUTHOR]