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Abstract Background High glycemic variability (GV) is a poor prognostic marker in cardiovascular diseases. We aimed to investigate the association of GV with all-cause mortality in patients with acute heart failure (HF). Methods The Korean Acute Heart Failure registry enrolled patients hospitalized for acute HF from 2011 to 2014. Blood glucose levels were measured at the time of admission, during hospitalization, and at discharge. We included those who had 3 or more blood glucose measurements in this study. Patients were divided into two groups based on the coefficient of variation (CoV) as an indicator of GV. Among survivors of the index hospitalization, we investigated all-cause mortality at 1 year after discharge. Results The study analyzed 2,617 patients (median age, 72 years; median left-ventricular ejection fraction, 36%; 53% male). During the median follow-up period of 11 months, 583 patients died. Kaplan–Meier curve analysis revealed that high GV (CoV > 21%) was associated with lower cumulative survival (log-rank P

Abstract Aims In 2021, vericiguat was approved by the US Food and Drug Administration (FDA) and the European Commission (EC) for reducing cardiovascular mortality and heart failure (HF) hospitalizations in patients with HF with reduced ejection fraction (HFrEF) based on the Vericiguat Global Study in Subjects with Heart Failure with Reduced Ejection Fraction (VICTORIA) trial. However, there has been no report for characterizing the generalizability of vericiguat to real‐world clinical practice. Methods and results The Korean Acute Heart Failure (KorAHF) registry is a multicentre prospective cohort study. A total of 5625 patients who were admitted for HF decompensation were consecutively enrolled. We excluded the patients without left ventricular ejection fraction (LVEF) quantification, patients with LVEF > 45%, patients with in‐hospital death or urgent heart transplantation, and patients without natriuretic peptide measurement. Among a total of 3014 enrolled patients, there were 21.9% patients with lower systolic blood pressure (SBP) (

Abstract Aims A waiting period of more than 3 months is recommended for patients before undergoing cardiac resynchronization therapy (CRT). However, due to an anticipated high mortality rate, early implementation of CRT might be beneficial for some patients. We aimed to evaluate the rate and the probability of left ventricular (LV) function improvement and their predictors in patients with heart failure (HF) with indications for CRT. Methods and results From March 2011 to February 2014, a total of 5625 hospitalized patients for acute HF were consecutively enrolled in 10 tertiary hospitals. Among them, we analysed 1792 patients (mean age 63.96 ± 15.42 years, female 63.1%) with left ventricular ejection fraction (LVEF) ≤ 35% at the baseline echocardiography and divided them into three groups: 144 with left bundle branch block (LBBB), 136 with wide QRS complexes without LBBB, and 1512 not having these findings (control). We compared and analysed these three groups for improvement of LV function at follow‐up echocardiography. In patients who met CRT indications (patients with LBBB or wide QRS complexes without LBBB), logistic regression was performed to identify risk factors for no improvement of LV. No improvement of LV was defined as LVEF ≤ 35% at follow‐up echocardiography or the composite adverse outcomes: death, heart transplantation, extracorporeal membrane oxygenation, or use of a ventricular assist device before follow‐up echocardiography. A classification tree was established using the binary recursive partitioning method to predict the outcome of patients who met CRT indications. In a median follow‐up of 11 months, LVEF improvement was observed in 24.3%, 15.4%, and 40.5% of patients with LBBB, wide QRS complexes without LBBB, and control, respectively. Patients meeting CRT indications had higher 3 month mortality rates than the control (24.6% vs. 17.7%, P = 0.002). Multivariable logistic regression analysis revealed that large LV end‐systolic dimension [odds ratio (OR) 1.10, 95% confidence interval (CI) 1.05–1.15, P

BackgroundHigh C-reactive protein (CRP) levels are associated with poor outcomes of heart failure (HF), and statins are known to reduce CRP levels. We investigated the prognostic value of CRP and statin in patients with HF with reduced and preserved ejection fraction (EF).MethodsAltogether, 3,831 patients from the Korean Acute Heart Failure registry were included and stratified according to the tertiles of CRP levels (T1: CRP < 0.30 mg/dL, T2: 0.30–1.14 mg/dL, and T3: CRP > 1.14 mg/dL). HF with reduced EF (HFrEF), HF with mildly reduced EF (HFmrEF), and HF with preserved EF (HFpEF) were defined as left ventricular ejection fraction (LVEF) ≤ 40%, 41–49%, ≥50%, respectively. The primary endpoints were all-cause, in-hospital, and post-discharge mortality.ResultsNo significant correlation was observed between CRP levels and LVEF (r = 0.02, P = 0.131). The prevalence of risk factors increased gradually from T1 to T3 in both the types of HF. Overall, 139 (3.6%) and 1,269 (34.4%) patients died during the index admission and follow-up (median: 995 days), respectively. After adjustment, each increase in the CRP tertiles was independently associated with in-hospital mortality (HFrEF: OR 1.58 and 95% CI 1.09–2.30, HFmrEF: OR 1.51 and 95% CI 0.72–3.52, and HFpEF: OR 2.98, 95% CI 1.46–6.73) and post-discharge mortality (HFrEF: HR 1.20, 95% CI 1.08–1.33, HFmrEF: HR 1.38 and 95% CI 1.12–1.70, and HFpEF: HR 1.37, 95% CI 1.02–1.85). In only patients with LVEF > 40% with highest CRP tertile, statin-users showed better survival trend than those without statins.ConclusionCRP is an excellent prognostic marker for HFrEF, HFmrEF, and HFpEF, implying that the neurohumoral and inflammatory pathways might be independent pathways. Statins may be beneficial in HF patients with increased CRP levels.Clinical trial registration[https://clinicaltrials.gov/], identifier [NCT013 89843].

Abstract Aims Tachycardia is a reversible event that may cause hemodynamic decompensation but may not necessarily cause direct damages to the myocardium. To evaluate the clinical outcomes of patients with heart failure (HF) and atrial fibrillation (AF), whose acute decompensation was tachycardia mediated. Methods and results The Korean Acute Heart Failure registry was a prospective registry that consecutively enrolled 5625 patients with acute HF. Patients were classified into three groups according to the rhythm and aggravating factor: (i) 3664 (65.1%) patients with sinus rhythm (SR), (ii) 1033 (18.4%) patients with AF whose decompensation was tachycardia‐mediated, AF‐TM (+), and (iii) N = 928 (16.5%) patients with AF whose decompensation was not tachycardia‐mediated, AF‐TM (−). The primary outcomes were in‐hospital and post‐discharge 1 year all‐cause mortality. At admission, the mean heart rate was 90.8 ± 23.4, 86.8 ± 26.8, and 106.3 ± 29.7 beats per minute for the SR, AF‐TM (−), and AF‐TM (+) groups, respectively. The AF‐TM (+) group had more favourable characteristics such as de novo onset HF, less diabetes, ischaemic heart disease, and higher blood pressure than the AF‐TM (−) group. In‐hospital mortality rates were 5.1%, 6.5%, and 1.7% for SR, AF‐TM (−), and AF‐TM (+) groups, respectively. In logistic regression analysis, the AF‐TM (+) group had lower in‐hospital mortality after adjusting the significant covariates (odds ratio, 0.49; 95% confidence interval, 0.26–0.93). The mortality rate did not differ between SR and AF‐TM (−) groups. During 1 year follow‐up, 990 (18.5%) patients died. In univariate and multivariate Cox proportional regression analyses, there was no difference in 1‐year all‐cause mortality between the three groups. Conclusions In patients with HF and AF, patients whose acute decompensation is tachycardia‐mediated have better in‐hospital, but similar post‐discharge outcomes compared with those with SR or those with AF whose decompensation is not tachycardia‐mediated. Clinical Trial Registration: ClinicalTrial.gov NCT01389843.

Background/Aims This study investigated the prognostic power of corrected QT (QTc) interval in patients with acute heart failure (AHF) according to sex. Methods We analyzed multicenter Korean Acute Heart Failure registry with patients with AHF admitted from 2011 to 2014. Among them, we analyzed 4,990 patients who were followed up to 5 years. Regarding QTc interval based on 12 lead electrocardiogram, patients were classified into quartiles according to sex. Results During follow-up with median 43.7 months, 2,243 (44.9%) patients died. The relationship between corrected QT interval and all-cause mortality followed a J-curve relationship. In Kaplan-Meier analysis, both sex had lowest mortality in the second QTc quartile. There were significant prognostic differences between the second and the fourth quartiles in male (log-rank p = 0.002), but not in female (log-rank p = 0.338). After adjusting covariates, the third (hazard ratio [HR], 1.185; 95% confidence interval [CI], 1.001 to 1.404; p = 0.049) and the fourth (HR, 1.404; 95% CI, 1.091 to 1.535; p = 0.003) quartiles demonstrated increased risk of mortality compared to the second quartile in male. In female, however, there was no significant difference across quartiles. QTc interval was associated with 5-year all-cause mortality in J-shape with nadir of 440 to 450 ms in male and 470 to 480 ms in female. Conclusions QTc interval was an independent predictor of overall death in male, but its significance decreased in female. The relationship between QTc interval and all-cause mortality was J-shaped in both sex.

Abstract Rapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however, conventional interpretation methods has low reliability for detecting MI and is difficulty to apply to limb 6-lead ECG based life type or wearable devices. We developed and validated a deep learning-based artificial intelligence algorithm (DLA) for detecting MI using 6-lead ECG. A total of 412,461 ECGs were used to develop a variational autoencoder (VAE) that reconstructed precordial 6-lead ECG using limb 6-lead ECG. Data from 9536, 1301, and 1768 ECGs of adult patients who underwent coronary angiography within 24 h from each ECG were used for development, internal and external validation, respectively. During internal and external validation, the area under the receiver operating characteristic curves of the DLA with VAE using a 6-lead ECG were 0.880 and 0.854, respectively, and the performances were preserved by the territory of the coronary lesion. Our DLA successfully detected MI using a 12-lead ECG or a 6-lead ECG. The results indicate that MI could be detected not only with a conventional 12 lead ECG but also with a life type 6-lead ECG device that employs our DLA.

Abstract Background In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively predict cardiac arrest using electrocardiography (ECG). We developed and validated a DLA for predicting cardiac arrest using ECG. Methods We conducted a retrospective study that included 47,505 ECGs of 25,672 adult patients admitted to two hospitals, who underwent at least one ECG from October 2016 to September 2019. The endpoint was occurrence of cardiac arrest within 24 h from ECG. Using subgroup analyses in patients who were initially classified as non-event, we confirmed the delayed occurrence of cardiac arrest and unexpected intensive care unit transfer over 14 days. Results We used 32,294 ECGs of 10,461 patients and 4483 ECGs of 4483 patients from a hospital were used as development and internal validation data, respectively. Additionally, 10,728 ECGs of 10,728 patients from another hospital were used as external validation data, which confirmed the robustness of the developed DLA. During internal and external validation, the areas under the receiver operating characteristic curves of the DLA in predicting cardiac arrest within 24 h were 0.913 and 0.948, respectively. The high risk group of the DLA showed a significantly higher hazard for delayed cardiac arrest (5.74% vs. 0.33%, P

Abstract Background Although more than one-third of the patients with acute heart failure (AHF) have diabetes mellitus (DM), it is unclear if DM has an adverse impact on clinical outcomes. This study compared the outcomes in patients hospitalized for AHF stratified by DM and left ventricular ejection fraction (LVEF). Methods The Korean Acute Heart Failure registry prospectively enrolled and followed 5625 patients from March 2011 to February 2019. The primary endpoints were in-hospital and overall all-cause mortality. We evaluated the impact of DM on these endpoints according to HF subtypes and glycemic control. Results During a median follow-up of 3.5 years, there were 235 (4.4%) in-hospital mortalities and 2500 (46.3%) overall mortalities. DM was significantly associated with increased overall mortality after adjusting for potential confounders (adjusted hazard ratio [HR] 1.11, 95% confidence interval [CI] 1.03–1.22). In the subgroup analysis, DM was associated with higher a risk of overall mortality in heart failure with reduced ejection fraction (HFrEF) only (adjusted HR 1.14, 95% CI 1.02–1.27). Inadequate glycemic control (HbA1c ≥ 7.0% within 1 year after discharge) was significantly associated with a higher risk of overall mortality compared with adequate glycemic control (HbA1c

Abstract Background In emergency medical services (EMSs), accurately predicting the severity of a patient’s medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. Methods We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. Results The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864–0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831–0.846]), Korean Triage and Acuity System (0.824 [0.815–0.832]), National Early Warning Score (0.741 [0.734–0.748]), and Modified Early Warning Score (0.696 [0.691–0.699]). Conclusions The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores.

BackgroundRandomized clinical trials of spironolactone showed significant mortality reduction in patients with heart failure with reduced ejection fraction. However, its role in acute heart failure syndrome (AHFS) is largely unknown.AimTo investigate the prescription characteristics, efficacy and safety of spironolactone in real-world patients with AHFS.Methods5,136 AHFS patients who survived to hospital discharge using a nationwide prospective registry in Korea were analyzed. The primary efficacy outcome was 3-year all-cause mortality.ResultsSpironolactone was prescribed in 2,402 (46.8%) at discharge: 26%.ConclusionsAlthough spironolactone was frequently used at lower doses in real-world practice, use of spironolactone significantly reduced 3-year mortality in patients with severely reduced LVEF with acceptable safety profile. However, our findings remain prone to various biases and further prospective randomized controlled studies are needed to confirm these findings.

Background/Aims It is unknown whether different β-blockers (BBs) have variable effects on long-term survival of patients with heart failure with reduced ejection fraction (HFrEF). This study compares the effects of two BBs, carvedilol and bisoprolol, on survival in patients with HFrEF. Methods The Korean Acute Heart Failure (KorAHF) registry is a prospective multicenter cohort that includes 5,625 patients who were hospitalized for acute heart failure (AHF). We selected 3,016 patients with HFrEF and divided this study population into two groups: BB at discharge (n = 1,707) or no BB at discharge (n = 1,309). Among patients with BB at discharge, subgroups were formed based on carvedilol prescription (n = 831), or bisoprolol prescription (n = 553). Propensity score matching analysis was performed. Results Among patients who were prescribed a BB at discharge, 60.5% received carvedilol and 32.7% received bisoprolol. There was a significant reduction in allcause mortality in those patients with HFrEF prescribed a BB at discharge compared to those who were not (BB vs. no BB, 26.1% vs. 40.8%; hazard ratio [HR], 0.59; 95% confidence interval [CI], 0.52 to 0.67; p < 0.001). However, there was no significant difference in the rate of all-cause mortality between those receiving different types of BB (carvedilol vs. bisoprolol, 27.5% vs. 23.5%; HR, 1.21; 95% CI, 0.99 to 1.47; p = 0.07). Similar results were observed after propensity score matching analysis (508 pairs, 26.2% vs. 23.8%; HR, 1.10; 95% CI, 0.86 to 1.40; p = 0.47). Conclusions In the treatment of AHF with reduced EF after hospitalization, mortality benefits of carvedilol and bisoprolol were comparable.

Abstract Introduction The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. Methods and Results This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12‐lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. Conclusion The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.

Background: The prognostic impact and predictors of NOAF in HF patients are not fully elucidated. This study aims to determine whether new-onset atrial fibrillation (NOAF) affects patient outcome and investigate predictors of atrial fibrillation (AF) in acute heart failure (HF) patients using real-world data. Methods: The factors associated with NOAF in 2894 patients with sinus rhythm (SR) enrolled in the Korean Acute Heart Failure (KorAHF) registry were investigated. Survival was analyzed using AF as a time-dependent covariate. Relevant predictors of NOAF were analyzed using multivariate proportional hazards models. Results: Over 27.4 months, 187 patients developed AF. The median overall survival time was over 48 and 9.9 months for the SR and NOAF groups, respectively. Cox regression analysis with NOAF as a time-dependent covariate showed a higher risk of death among patients with NOAF. Multivariate Cox modeling showed that age, worsening HF, valvular heart disease (VHD), loop diuretics, lower heart rate, larger left atrium (LA) diameter, and elevated creatinine levels were independently associated with NOAF. Risk score indicated the number of independent predictors. The incidence of NOAF was 2.9%, 9.4%, and 21.8% in the low-risk, moderate-risk, and high-risk groups, respectively (p < 0.001). Conditional inference tree analysis identified worsening HF, heart rate, age, LA diameter, and VHD as discriminators. Conclusions: NOAF was associated with decreased survival in acute HF patients with SR. Age, worsening HF, VHD, loop diuretics, lower heart rate, larger LA diameter, and elevated creatinine could independently predict NOAF. This may be useful to risk-stratify HF patients at risk for AF.

Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The aim of this study was to develop and validate a deep learning–based algorithm, combining a multilayer perceptron and convolutional neural network, for detecting significant AS using ECGs. Methods and Results This retrospective cohort study included adult patients who had undergone both ECG and echocardiography. A deep learning–based algorithm was developed using 39 371 ECGs. Internal validation of the algorithm was performed with 6453 ECGs from one hospital, and external validation was performed with 10 865 ECGs from another hospital. The end point was significant AS (beyond moderate). We used demographic information, features, and 500‐Hz, 12‐lead ECG raw data as predictive variables. In addition, we identified which region had the most significant effect on the decision‐making of the algorithm using a sensitivity map. During internal and external validation, the areas under the receiver operating characteristic curve of the deep learning–based algorithm using 12‐lead ECG for detecting significant AS were 0.884 (95% CI, 0.880–0.887) and 0.861 (95% CI, 0.858–0.863), respectively; those using a single‐lead ECG signal were 0.845 (95% CI, 0.841–0.848) and 0.821 (95% CI, 0.816–0.825), respectively. The sensitivity map showed the algorithm focused on the T wave of the precordial lead to determine the presence of significant AS. Conclusions The deep learning–based algorithm demonstrated high accuracy for significant AS detection using both 12‐lead and single‐lead ECGs.

Background Many patients with heart failure (HF) with reduced ejection fraction (HFrEF) experience improvement or recovery of left ventricular ejection fraction (LVEF). Data on clinical characteristics, outcomes, and medical therapy in patients with HF with improved ejection fraction (HFiEF) are scarce. Methods and Results Of 5625 consecutive patients hospitalized for acute HF in the KorAHF (Registry [Prospective Cohort] for Heart Failure in Korea) study, 5103 patients had baseline echocardiography and 2302 patients had follow‐up echocardiography at 12 months. HF phenotypes were defined as persistent HFrEF (LVEF ≤40% at baseline and at 1‐year follow‐up), HFiEF (LVEF ≤40% at baseline and improved up to 40% at 1‐year follow‐up), HF with midrange ejection fraction (LVEF between 40% and

Background Many hospitalized patients with heart failure and reduced ejection fraction (HFrEF) have a slow heart rate at discharge, and the effect of β‐blockers may be reduced in those patients. We sought to examine the variable effect of β‐blockers on clinical outcomes according to the discharge heart rate of hospitalized HFrEF patients. Methods and Results The KorAHF (Korean Acute Heart Failure) registry consecutively enrolled 5625 patients hospitalized for acute heart failure. In this analysis, we included patients with HFrEF (left ventricular ejection fraction ≤40%). Slow heart rate was defined as

OBJECTIVE:Conventional risk stratification models for mortality of acute myocardial infarction (AMI) have potential limitations. This study aimed to develop and validate deep-learning-based risk stratification for the mortality of patients with AMI (DAMI). METHODS:The data of 22,875 AMI patients from the Korean working group of the myocardial infarction (KorMI) registry were exclusively divided into 12,152 derivation data of 36 hospitals and 10,723 validation data of 23 hospitals. The predictor variables were the initial demographic and laboratory data. The endpoints were in-hospital mortality and 12-months mortality. We compared the DAMI performance with the global registry of acute coronary event (GRACE) score, acute coronary treatment and intervention outcomes network (ACTION) score, and the thrombolysis in myocardial infarction (TIMI) score using the validation data. RESULTS:In-hospital mortality for the study subjects was 4.4% and 6-month mortality after survival upon discharge was 2.2%. The areas under the receiver operating characteristic curves (AUCs) of the DAMI were 0.905 [95% confidence interval 0.902-0.909] and 0.870 [0.865-0.876] for the ST elevation myocardial infarction (STEMI) and non ST elevation myocardial infarction (NSTEMI) patients, respectively; these results significantly outperformed those of the GRACE (0.851 [0.846-0.856], 0.810 [0.803-0.819]), ACTION (0.852 [0.847-0.857], 0.806 [0.799-0.814] and TIMI score (0.781 [0.775-0.787], 0.593[0.585-0.603]). DAMI predicted 30.9% of patients more accurately than the GRACE score. As secondary outcome, during the 6-month follow-up, the high risk group, defined by the DAMI, has a significantly higher mortality rate than the low risk group (17.1% vs. 0.5%, p < 0.001). CONCLUSIONS:The DAMI predicted in-hospital mortality and 12-month mortality of AMI patients more accurately than the existing risk scores and other machine-learning methods.

AimsThis study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF).Methods and results12,654 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(pConclusionDAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models.

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.

Background Although current guidelines now define heart failure with midrange ejection fraction (HFmrEF) as HF with a left ventricular EF of 40% to 49%, there are limited data on response to guideline‐directed medical therapy in patients with HFmrEF. The current study aimed to evaluate the association between β‐blocker, renin‐angiotensin system blocker (RASB), or aldosterone antagonist (AA) treatment with clinical outcome in patients with HFmrEF. Methods and Results We performed a patient‐level pooled analysis on 1144 patients with HFmrEF who were hospitalized for acute HF from the KorHF (Korean Heart Failure) and KorAHF (Korean Acute Heart Failure) registries. The study population was divided between use of β‐blocker, RASB, or AA to evaluate the guideline‐directed medical therapy in patients with HFmrEF. Sensitivity analyses, including propensity score matching and inverse‐probability‐weighted methods, were performed. The use of β‐blocker in the discharge group showed significantly lower rates of all‐cause mortality compared with those who did not use a β‐blocker (β‐blocker versus no β‐blocker, 30.7% versus 38.2%; hazard ratio, 0.758; 95% confidence interval, 0.615–0.934; P=0.009). Similarly, the RASB use in the discharge group was associated with the lower risk of mortality compared with no use of RASB (RASB versus no RASB, 31.9% versus 38.1%; hazard ratio, 0.76; 95% confidence interval, 0.618–0.946; P=0.013). However, there was no significant difference in all‐cause mortality between AA and no AA in the discharge group (AA versus no AA, 34.2% versus 34.0%; hazard ratio, 1.063; 95% confidence interval, 0.858–1.317; P=0.578). Multiple sensitivity analyses showed similar trends. Conclusions For treatment of acute HFmrEF after hospitalization, β‐blocker and RASB therapies on discharge were associated with reduced risk of all‐cause mortality. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT01389843.

BackgroundWorsening renal function (WRF) is associated with adverse outcomes in patients with heart failure. We investigated the predictors and prognostic value of WRF during admission, in patients with preserved ejection fraction (HFpEF) versus those with reduced ejection fraction (HFrEF). Methods and ResultsA total of 5625 patients were enrolled in the KorAHF (Korean Acute Heart Failure) registry. WRF was defined as an absolute increase in creatinine of ≥0.3 mg/dL. Transient WRF was defined as recovery of creatinine at discharge, whereas persistent WRF was indicated by a nonrecovered creatinine level. HFpEF and HFrEF were defined as a left ventricle ejection fraction ≥50% and ≤40%, respectively. Among the total population, WRF occurred in 3101 patients (55.1%). By heart failure subgroup, WRF occurred more frequently in HFrEF (57.0% versus 51.3%; P

BACKGROUND:We investigated the role of nutritional risk index (NRI) in predicting 1-year mortality in patients with acute decompensated heart failure (ADHF). METHODS:Among 5,625 cohort patients enrolled in Korean Acute Heart Failure (KorAHF) Registry, a total of 5,265 patients who were possible to calculate NRI [NRI = (1.519 x serum albumin [g/dl]) + (41.7 x weight [kg]/ideal body weight [kg])] were enrolled. The patients were divided into 4 groups according to the NRI quartile; Q1 100 (n = 1711, 65.6 ± 14.5 years, 779 males). Primary end-point was all-cause mortality at 1-year clinical follow-up. RESULTS:The 1-year mortality was significantly increased as the NRI quartile decreased, and the lowest NRI quartile was associated with the highest 1-year mortality (Q1: 27.5% vs. Q2: 20.9% vs. Q3: 12.9% vs. Q4: 8.7%, linear p

BackgroundIn clinical practice, a risk prediction model is an effective solitary program to predict prognosis in particular patient groups. B-type natriuretic peptide (BNP)and N-terminal pro-b-type natriuretic peptide (NT-proBNP) are widely recognized outcome-predicting factors for patients with heart failure (HF).This study derived external validation of a risk score to predict 1-year mortality after discharge in hospitalized patients with HF using the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC)program data. We also assessed the effect of adding BNP or NT-proBNP to this risk score model in a Korean HF registry population.Method and resultsWe included 5625 patients from the Korean acute heart failure registry (KorAHF) and excluded those who died in hospital. The MAGGIC constructed a risk score to predict mortality in patients with HF by using 13 routinely available patient characteristics (age, gender, diabetes, chronic obstructive pulmonary disorder (COPD), HF diagnosed within the last 18 months, current smoker, NYHA class, use of beta blocker, ACEI or ARB, body mass index, systolic blood pressure, creatinine, and EF). We added BNP or NT-proBNP, which are the most important biomarkers, to the MAGGIC risk scoring system in patients with HF. The outcome measure was 1-year mortality. In multivariable analysis, BNP or NT-proBNP independently predicted death. The risk score was significantly varied between alive and dead groups (30.61 ± 6.32 vs. 24.80 ± 6.81, p < 0.001). After the conjoint use of BNP or NT-proBNP and MAGGIC risk score in patients with HF, a significant difference in risk score was noted (31.23 ± 6.46 vs. 25.25 ± 6.96, p < 0.001).The discrimination abilities of the risk score model with and without biomarker showed minimal improvement (C index of 0.734 for MAGGIC risk score and 0.736 for MAGGIC risk score plus BNP or NT-proBNP, p = 0.0502) and the calibration was found good. However, we achieved a significant improvement in net reclassification and integrated discrimination for mortality (NRI of 33.4%,p < 0.0001 and IDI of 0.002, p < 0.0001).ConclusionIn the KorAHF, the MAGGIC project HF risk score performed well in a large nationwide contemporary external validation cohort. Furthermore, the addition of BNP or NT-proBNPto the MAGGIC risk score was beneficial in predicting more death in hospitalized patients with HF.

Vascular calcification is an advanced feature of atherosclerosis for which no effective therapy is available. To investigate the modulation or reversal of calcification, we identified calcifying progenitor cells and investigated their calcifying/decalcifying potentials. Cells from the aortas of mice were sorted into four groups using Sca-1 and PDGFRα markers. Sca-1(+) (Sca-1(+)/PDGFRα(+) and Sca-1(+)/PDGFRα(-)) progenitor cells exhibited greater osteoblastic differentiation potentials than Sca-1(-) (Sca-1(-)/PDGFRα(+) and Sca-1(-)/PDGFRα(-)) progenitor cells. Among Sca-1(+) progenitor populations, Sca-1(+)/PDGFRα(-) cells possessed bidirectional differentiation potentials towards both osteoblastic and osteoclastic lineages, whereas Sca-1(+)/PDGFRα(+) cells differentiated into an osteoblastic lineage unidirectionally. When treated with a peroxisome proliferator activated receptor γ (PPARγ) agonist, Sca-1(+)/PDGFRα(-) cells preferentially differentiated into osteoclast-like cells. Sca-1(+) progenitor cells in the artery originated from the bone marrow (BM) and could be clonally expanded. Vessel-resident BM-derived Sca-1(+) calcifying progenitor cells displayed nonhematopoietic, mesenchymal characteristics. To evaluate the modulation of in vivo calcification, we established models of ectopic and atherosclerotic calcification. Computed tomography indicated that Sca-1(+) progenitor cells increased the volume and calcium scores of ectopic calcification. However, Sca-1(+)/PDGFRα(-) cells treated with a PPARγ agonist decreased bone formation 2-fold compared with untreated cells. Systemic infusion of Sca-1(+)/PDGFRα(-) cells into Apoe(-/-) mice increased the severity of calcified atherosclerotic plaques. However, Sca-1(+)/PDGFRα(-) cells in which PPARγ was activated displayed markedly decreased plaque severity. Immunofluorescent staining indicated that Sca-1(+)/PDGFRα(-) cells mainly expressed osteocalcin; however, activation of PPARγ triggered receptor activator for nuclear factor-κB (RANK) expression, indicating their bidirectional fate in vivo. These findings suggest that a subtype of BM-derived and vessel-resident progenitor cells offer a therapeutic target for the prevention of vascular calcification and that PPARγ activation may be an option to reverse calcification.

Paraoxonase (PON) is a high-density-lipoprotein (HDL) associated enzyme with antioxidative and anti-atherogenic property. Its function is associated with coronary artery disease and its activity genetically controlled. We evaluated whether genetic variation of PON-1 is associated with clinical outcome in a large cohort of Korean patients with drug-eluting stents implantation.A total of 1676 patients with drug-eluting stent implantation were enrolled in the prospective CROSS-VERIFY cohort from June 2006 to June 2010. We genotyped the PON1-Q192R gene, measured clopidogrel on-treatment platelet reactivity (OPR), and analyzed lipid profiles. The primary endpoint was the composite of cardiac death, myocardial infarction, and stent thrombosis at 12 months.PON-1 genotyping data were available in 1336 patients. Since the Q-allele is associated with decreased PON-activity, we analyzed the outcome between patients with QQ/QR (815 patients, 61%) and those with RR-genotype (521 patients, 39%). After adjustment for common cardiac risk factors, the QQ/QR-genotype was an independent predictor of the primary thrombotic endpoint with an 11-fold increased risk (HR 11.6, 95% CI: 1.55-87.0), but not repeat revascularization (HR 1.12, 95% CI: 0.78-1.61). The QQ/QR-genotype was not associated with OPR (QQ/QR: 231±86 PRU vs. RR 236±82 PRU, p = 0.342) but higher small-dense LDL levels (1.20±0.12 mg/dL vs. 0.76±0.15 mg/dL, p = 0.027). The increased risk of thrombotic outcomes was more profound in acute coronary syndrome (ACS) patients compared with non-ACS patients.PON1 Q-allele is an independent predictor of worse cardiovascular outcome independent of platelet function and is associated with significantly higher levels of small dense LDL-C.

Transplantation of autologous skeletal myoblasts (SMBs) is a potential therapeutic approach for myocardial infarction. However, their clinical efficacy and safety is still controversial. Electrical coupling through gap junction between SMBs and host myocardium is essential for synchronized contraction and electrical stability. Here, we investigated the effect of heart beat-simulating environment, oscillating pressure, on the expression of connexin43 in two types of SMBs from rat and mouse. We found that connexin43 is markedly decreased under ischemia-mimicking conditions such as serum starvation and hypoxia (1% O 2 ) in rat primary cultured SMBs and mouse C2C12 SMB cell line. Interestingly, the decrease of connexin43 expression under serum starvation was attenuated by oscillating pressure. Oscillating pressure treatment increased the expression of connexin43 twofold through AP-1 stimulation, which was blocked by PD98059, ERK inhibitor. In coculture of cardiomyocytes and C2C12, pressure-treated C2C12 and cardiomyocytes were able to form functional gap junction, which was demonstrated by both calcein-AM dye transfer assay and measurement of simultaneous contraction. In rat myocardial infarction model, transplantation of SMBs pretreated with oscillating pressure resulted in lesser ventricular dilatation and better systolic function than transplantation of untreated SMBs and control group. These results suggested that application of oscillating pressure on SMBs before transplantation may be useful to promote therapeutic efficacy for myocardial infarction by enhancing gap junction formation between transplanted and host cells.