Real-World Eligibility and Cost-Effectiveness Analysis of Empagliflozin for Heart Failure in Korea.

BACKGROUND: The US Food and Drug Administration (FDA) and European Medicines Agency (EMA) approved empagliflozin for reducing cardiovascular mortality and heart failure (HF) hospitalization in patients with both HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). However, limited data are available on the generalizability of empagliflozin to clinical practice. Therefore, we evaluated real-world eligibility and potential cost-effectiveness based on a nationwide prospective HF registry. METHODS: A total of 3,108 HFrEF and 2,070 HFpEF patients from the Korean Acute Heart Failure (KorAHF) registry were analyzed. Eligibility was estimated by inclusion and exclusion criteria of EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Reduced Ejection Fraction (EMPEROR-Reduced) and EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Preserved Ejection Fraction (EMPEROR-Preserved) trials and by FDA & EMA label criteria. The cost-utility analysis was done using a Markov model to project the lifetime medical cost and quality-adjusted life year (QALY). RESULTS: Among the KorAHF patients, 91.4% met FDA & EMA label criteria, while 44.7% met the clinical trial criteria. The incremental cost-effectiveness ratio of empagliflozin was calculated at US$6,764 per QALY in the overall population, which is far below a threshold of US$18,182 per QALY. The cost-effectiveness benefit was more evident in patients with HFrEF (US$5,012 per QALY) than HFpEF (US$8,971 per QALY). CONCLUSION: There is a large discrepancy in real-world eligibility for empagliflozin between FDA & EMA labels and clinical trial criteria. Empagliflozin is cost-effective in HF patients regardless of ejection fraction in South Korea health care setting. The efficacy and safety of empagliflozin in real-world HF patients should be further investigated for a broader range of clinical applications. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01389843.

In-hospital glycemic variability and all-cause mortality among patients hospitalized for acute heart failure.

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 < 0.001). Multivariate Cox proportional analysis showed that high GV was associated with an increased risk of 1-year (HR 1.56, 95% CI 1.26-1.92) mortality. High GV significantly increased the risk of 1-year mortality in non-diabetic patients (HR 1.93, 95% CI 1.47-2.54) but not in diabetic patients (HR 1.19, 95% CI 0.86-1.65, P for interaction = 0.021). CONCLUSIONS: High in-hospital GV before discharge was associated with all-cause mortality within 1 year, especially in non-diabetic patients with acute HF.

Artificial intelligence for detecting electrolyte imbalance using electrocardiography.

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.

Detection and classification of arrhythmia using an explainable deep learning model.

BACKGROUND: Early detection and intervention is the cornerstone for appropriate treatment of arrhythmia and prevention of complications and mortality. Although diverse deep learning models have been developed to detect arrhythmia, they have been criticized due to their unexplainable nature. In this study, we developed an explainable deep learning model (XDM) to classify arrhythmia, and validated its performance using diverse external validation data. METHODS: In this retrospective study, the Sejong dataset comprising 86,802 electrocardiograms (ECGs) was used to develop and internally variate the XDM. The XDM based on a neural network-backed ensemble tree was developed with six feature modules that are able to explain the reasons for its decisions. The model was externally validated using data from 36,961 ECGs from four non-restricted datasets. RESULTS: During internal and external validation of the XDM, the average area under the receiver operating characteristic curves (AUCs) using a 12­lead ECG for arrhythmia classification were 0.976 and 0.966, respectively. The XDM outperformed a previous simple multi-classification deep learning model that used the same method. During internal and external validation, the AUCs of explainability were 0.925-0.991. CONCLUSION: Our XDM successfully classified arrhythmia using diverse formats of ECGs and could effectively describe the reason for the decisions. Therefore, an explainable deep learning methodology could improve accuracy compared to conventional deep learning methods, and that the transparency of XDM can be enhanced for its application in clinical practice.

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

BACKGROUND AND OBJECTIVES: Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we developed and validated a deep learning algorithm to predict the need for critical care in pediatric EDs. METHODS: We conducted a retrospective observation cohort study using data from the Korean National Emergency Department Information System, which collected data in real time from 151 EDs. The study subjects were pediatric patients who visited EDs from 2014 to 2016. The data were divided by date into derivation and test data. The primary end point was critical care, and the secondary endpoint was hospitalization. We used age, sex, chief complaint, symptom onset to arrival time, arrival mode, trauma, and vital signs as predicted variables. RESULTS: The study subjects consisted of 2,937,078 pediatric patients of which 18,253 were critical care and 375,078 were hospitalizations. For critical care, the area under the receiver operating characteristics curve of the deep learning algorithm was 0.908 (95% confidence interval, 0.903-0.910). This result significantly outperformed that of the pediatric early warning score (0.812 [0.803-0.819]), conventional triage and acuity system (0.782 [0.773-0.790]), random forest (0.881 [0.874-0.890]), and logistic regression (0.851 [0.844-0.858]). For hospitalization, the deep-learning algorithm (0.782 [0.780-0.783]) significantly outperformed the other methods. CONCLUSIONS: The deep learning algorithm predicted the critical care and hospitalization of pediatric ED patients more accurately than the conventional early warning score, triage tool, and machine learning methods.

Beneficial Effect of Left Ventricular Remodeling after Early Change of Sacubitril/Valsartan in Patients with Nonischemic Dilated Cardiomyopathy.

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.

Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning-based early warning system. The purpose of this study was to compare the performance of an artificial intelligence-based early warning system with that of conventional methods in a real hospital situation. DESIGN: Retrospective cohort study. SETTING: This study was conducted at a hospital in which deep learning-based early warning system was implemented. PATIENTS: We reviewed the records of adult patients who were admitted to the general ward of our hospital from April 2018 to March 2019. INTERVENTIONS: The study population included 8,039 adult patients. A total 83 events of deterioration occurred during the study period. The outcome was events of deterioration, defined as cardiac arrest and unexpected ICU admission. We defined a true alarm as an alarm occurring within 0.5-24 hours before a deteriorating event. MEASUREMENTS AND MAIN RESULTS: We used the area under the receiver operating characteristic curve, area under the precision-recall curve, number needed to examine, and mean alarm count per day as comparative measures. The deep learning-based early warning system (area under the receiver operating characteristic curve, 0.865; area under the precision-recall curve, 0.066) outperformed the modified early warning score (area under the receiver operating characteristic curve, 0.682; area under the precision-recall curve, 0.010) and reduced the number needed to examine and mean alarm count per day by 69.2% and 59.6%, respectively. At the same specificity, deep learning-based early warning system had up to 257% higher sensitivity than conventional methods. CONCLUSIONS: The developed artificial intelligence based on deep-learning, deep learning-based early warning system, accurately predicted deterioration of patients in a general ward and outperformed conventional methods. This study showed the potential and effectiveness of artificial intelligence in an rapid response system, which can be applied together with electronic health records. This will be a useful method to identify patients with deterioration and help with precise decision-making in daily practice.

Impact of diabetes mellitus on mortality in patients with acute heart failure: a prospective cohort study.

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 < 7.0%) (44.0% vs. 36.8%, log-rank p = 0.016). CONCLUSIONS: DM is associated with a higher risk of overall mortality in AHF, especially HFrEF. Well-controlled diabetes (HbA1c < 7.0%) is associated with a lower risk of overall mortality compared to uncontrolled diabetes. Trial registration ClinicalTrial.gov, NCT01389843. Registered July 6, 2011. https://clinicaltrials.gov/ct2/show/NCT01389843.

Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography.

AIMS: Although left ventricular hypertrophy (LVH) has a high incidence and clinical importance, the conventional diagnosis criteria for detecting LVH using electrocardiography (ECG) has not been satisfied. We aimed to develop an artificial intelligence (AI) algorithm for detecting LVH. METHODS AND RESULTS: This retrospective cohort study involved the review of 21 286 patients who were admitted to two hospitals between October 2016 and July 2018 and underwent 12-lead ECG and echocardiography within 4 weeks. The patients in one hospital were divided into a derivation and internal validation dataset, while the patients in the other hospital were included in only an external validation dataset. An AI algorithm based on an ensemble neural network (ENN) combining convolutional and deep neural network was developed using the derivation dataset. And we visualized the ECG area that the AI algorithm used to make the decision. The area under the receiver operating characteristic curve of the AI algorithm based on ENN was 0.880 (95% confidence interval 0.877-0.883) and 0.868 (0.865-0.871) during the internal and external validations. These results significantly outperformed the cardiologist's clinical assessment with Romhilt-Estes point system and Cornell voltage criteria, Sokolov-Lyon criteria, and interpretation of ECG machine. At the same specificity, the AI algorithm based on ENN achieved 159.9%, 177.7%, and 143.8% higher sensitivities than those of the cardiologist's assessment, Sokolov-Lyon criteria, and interpretation of ECG machine. CONCLUSION: An AI algorithm based on ENN was highly able to detect LVH and outperformed cardiologists, conventional methods, and other machine learning techniques.

Artificial intelligence for detecting mitral regurgitation using electrocardiography.

BACKGROUND: Screening and early diagnosis of mitral regurgitation (MR) are crucial for preventing irreversible progression of MR. In this study, we developed and validated an artificial intelligence (AI) algorithm for detecting MR using electrocardiography (ECG). METHODS: This retrospective cohort study included data from two hospital. An AI algorithm was trained using 56,670 ECGs from 24,202 patients. Internal validation of the algorithm was performed with 3174 ECGs of 3174 patients from one hospital, while external validation was performed with 10,865 ECGs of 10,865 patients from another hospital. The endpoint was the diagnosis of significant MR, moderate to severe, confirmed by echocardiography. We used 500 Hz ECG raw data as predictive variables. Additionally, we showed regions of ECG that have the most significant impact on the decision-making of the AI algorithm using a sensitivity map. RESULTS: During the internal and external validation, the area under the receiver operating characteristic curve of the AI algorithm using a 12-lead ECG for detecting MR was 0.816 and 0.877, respectively, while that using a single-lead ECG was 0.758 and 0.850, respectively. In the 3157 non-MR individuals, those patients that the AI defined as high risk had a significantly higher chance of development of MR than the low risk group (13.9% vs. 2.6%, p < 0.001) during the follow-up period. The sensitivity map showed the AI algorithm focused on the P-wave and T-wave for MR patients and QRS complex for non-MR patients. CONCLUSIONS: The proposed AI algorithm demonstrated promising results for MR detecting using 12-lead and single-lead ECGs.

Implantable Cardioverter-defibrillator Utilization and Its Outcomes in Korea: Data from Korean Acute Heart Failure Registry.

BACKGROUND: There are sparse data on the utilization rate of implantable cardioverter-defibrillator (ICD) and its beneficial effects in Korean patients with heart failure with reduced left ventricular ejection fraction (LVEF). METHODS: Among 5,625 acute heart failure (AHF) patients from 10 tertiary university hospitals across Korea, 485 patients with reassessed LVEF ≤ 35% at least 3 months after the index admission were enrolled in this study. The ICD implantation during the follow-up was evaluated. Mortality was compared between patients with ICDs and age-, sex-, and follow-up duration matched control patients. RESULTS: Among 485 patients potentially indicated for an ICD for primary prevention, only 56 patients (11.5%) underwent ICD implantation during the follow-up. Patients with ICD showed a significantly lower all-cause mortality compared with their matched control population: adjusted hazard ratio (HR) (95% confidence interval [CI]) = 0.39 (0.16-0.92), P = 0.032. The mortality rate was still lower in the ICD group after excluding patients with cardiac resynchronization therapy (adjusted HR [95% CI] = 0.09 [0.01-0.63], P = 0.015). According to the subgroup analysis for ischemic heart failure, there was a significantly lower all-cause mortality in the ICD group than in the no-ICD group (HR [95% CI] = 0.20 [0.06-0.72], P = 0.013), with a borderline statistical significance (interaction P = 0.069). CONCLUSION: Follow-up data of this large, multicenter registry suggests a significant under-utilization of ICD in Korean heart failure patients with reduced LVEF. Survival analysis implies that previously proven survival benefit of ICD in clinical trials could be extrapolated to Korean patients. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01389843.

Comparison of Characteristics and 3-Year Outcomes in Patients With Acute Heart Failure With Preserved, Mid-Range, and Reduced Ejection Fraction.

BACKGROUND: The clinical characteristics and outcomes of acute heart failure (AHF) according to left ventricular ejection fraction (LVEF) have not been fully elucidated, especially for patients with mid-range LVEF. We performed a comprehensive comparison of the epidemiology, patterns of in-hospital management, and clinical outcomes in AHF patients with different LVEF categories. Methods and Results: The Korean Acute Heart Failure (KorAHF) registry is a prospective multicenter cohort of hospitalized AHF patients in Korea. A total of 5,374 patients enrolled in the KorAHF registry were classified according to LVEF based on the 2016 ESC guidelines. More than half of the HF patients (58%) had reduced EF (HFrEF), 16% had mid-range EF (HFmrEF), and 25% had preserved EF (HFpEF). The HFmrEF patients showed intermediate epidemiological profiles between HFrEF and HFpEF and had a propensity to present as de-novo HF with ischemic etiology. Patients with lower LVEF had worse short-term outcomes, and the all-cause in-hospital mortality, including urgent heart transplantation, of HFrEF, HFmrEF, and HFpEF was 7.1%, 3.6%, and 3.0%, respectively. Overall, discharged AHF patients showed poor 3-year all-cause death up to 38%, which was comparable between LVEF subgroups (P=0.623). CONCLUSIONS: Each LVEF subgroup of AHF patients was a heterogeneous population with diverse characteristics, which have a significant effect on the clinical outcomes. This finding suggested that focused phenotyping of AHF patients could help identify the optimal management strategy and develop novel effective therapies.

Fate of Acute Heart Failure Patients With Mid-Range Ejection Fraction.

BACKGROUND: The outcomes of heart failure (HF) with mid-range ejection fraction (HFmrEF) have been rarely studied, and follow-up data on left ventricular ejection fraction (LVEF) are scarse.Methods and Results:Patients were selected from a prospective multicenter registry of patients hospitalized for acute HF and then classified in the improved group if they exhibited %LVEF change ≥5 with follow-up LVEF ≥50%. Follow-up LVEF reported at least 90 days after discharge was used for classification. Of the 3,085 patients with acute HF, 454 were classified in the HFmrEF, and 276 had follow-up data. Of these 276 patients, 34.1% were classified in the improved group. Multivariate analysis revealed that hypertension, higher heart rate, lower serum sodium level, and maintenance therapy with ß-blocker were associated with improved LVEF. The survival rate was significantly higher in the improved group than in the other groups. Young age and maintenance therapy with renin-angiotensin system blockers or aldosterone antagonists were significantly associated with better survival in HFmrEF. CONCLUSIONS: One-third of HFmrEF patients showed improved LVEF; moreover, the survival rate in the improved group was higher than the other groups. Renin-angiotensin system blockers and aldosterone antagonists could improve the survival of HFmrEF patients.

Evaluating the Efficacy, Safety, and Tolerability of Serelaxin When Added to Standard Therapy in Asian Patients With Acute Heart Failure: Design and Rationale of RELAX-AHF-ASIA Trial.

BACKGROUND: Acute heart failure (AHF), a common and growing health concern worldwide, is associated with high risk of post-discharge rehospitalization and mortality. Existing evidence indicates potential therapeutic benefits of serelaxin in Caucasian AHF patients, but corresponding data in Asians remain scarce. RELAX-AHF-ASIA, a multinational, randomized, double-blind, placebo-controlled, phase III trial, will evaluate the effects of serelaxin on symptom relief and clinical outcomes in Asian AHF patients, with the use of novel assessments. METHODS AND RESULTS: Patients with AHF, systolic blood pressure ≥125 mm Hg, and mild to moderate renal dysfunction will be randomized within 16 hours of presentation to receive 48-hour intravenous infusion of 30 µg ⋅ kg-1 ⋅ d-1 serelaxin or placebo in addition to standard therapy. The composite primary end point includes: (1) treatment success (moderate/marked improvement in patient-reported dyspnea and physician-assessed signs of congestion on day 2); (2) treatment failure (in-hospital worsening of signs and/or symptoms of heart failure [HF] requiring intensification of intravenous HF therapy or mechanical ventilation, renal/circulatory support, rehospitalization due to HF/renal-failure, or death through day 5); and (3) unchanged status. Secondary end points include time to in-hospital worsening HF through day 5 and all-cause and cardiovascular deaths through day 180. CONCLUSIONS: RELAX-AHF-ASIA, the largest randomized clinical trial in Asian AHF patients to date, has a novel composite primary end point and the potential to become a hallmark of AHF trials.

Heart Transplantation in Asia.

Heart transplantation (HTx) is the effective way to improve quality of life as well as survival in terminal heart failure (HF) patients. Since the first heart transplant in 1968 in Japan and in earnest in 1987 at Taiwan, HTx has been continuously increasing in Asia. Although the current percentage of heart transplants from Asia comprises only 5.7% of cases in the International Society of Heart and Lung Transplantation (ISHLT) registry, the values were under-reported and soon will be greatly increased. HTx in Asia shows comparable with or even better results compared with ISHLT registry data. Several endemic infections, including type B hepatitis, tuberculosis, and cytomegalovirus, are unique aspects of HTx in Asia, and need special attention in transplant care. Although cardiac allograft vasculopathy (CAV) is considered as a leading cause of death after HTx globally, multiple observations suggest less prevalence and benign nature of CAV among Asian populations. Although there are many obstacles such as religion, social taboo or legal process, Asian countries will keep overcoming obstacles and broaden the field of HTx.

Gender Difference in the Prognostic Value of N-Terminal Pro-B Type Natriuretic Peptide in Patients With Heart Failure　- A Report From the Korean Heart Failure Registry (KorHF).

BACKGROUND: Very little data is available to evaluate the gender-specific role of N-terminal pro-B type natriuretic peptide (NT-proBNP). This study was performed to investigate whether there is a gender difference in the prognostic value of NT-proBNP in patients hospitalized for heart failure (HF).Methods and Results:A total of 2,280 patients hospitalized with HF (67.9±14.3 years, 50.9% women) from the nationwide registry database were analyzed. Composite events including all-cause mortality and HF readmission were assessed. During the mean follow-up period of 1,245±824 days, there were 1,067 cases of composite events (49.7%). NT-proBNP levels were significantly higher in patients with events than those without in both genders (P<0.001 for each). A higher NT-proBNP level was an independent predictor of events (highest vs. lowest tertile: hazard ratio [HR], 1.74; 95% confidence interval [CI], 1.25-2.43; P=0.001) in men, even after controlling for potential confounders. However, NT-proBNP was not associated with the occurrence of composite events in women in the same multivariable analysis (P>0.05). CONCLUSIONS: In patients with HF, the NT-proBNP level seems to be a more valuable marker in the prediction of long-term mortality and HF readmission in men than in women.

Interaction between platelets and endothelial progenitor cells via LPA-Edg-2 axis is augmented by PPAR-δ activation.

BACKGROUND: Peroxisome proliferator-activated receptor (PPAR)-δ is a nuclear receptor regulating cell metabolism. The role of PPAR-δ in late endothelial progenitor cells (EPCs) has not been fully elucidated. We aim to understand the effects of PPAR-δ activation on late EPC and to reveal the underlying mechanism. METHODS AND RESULTS: Treatment with a highly selective PPAR-δ agonist (GW501516) induced proliferation of late EPCs and enhanced their vasculogenic potential. Search for the target molecule of PPAR-δ activation revealed endothelial differentiation gene (Edg)-2. Chromatin immunoprecipitation and promoter assays demonstrated that Edg-2 gene was specifically induced by PPAR-δ through direct transcriptional activation. Lysophosphatidic acid (LPA), an Edg ligand, mimicked the pro-vasculogenic effects of GW501516 in late EPCs whereas Edg antagonist (Ki16425) blocked these effects. Edg-2 is a membrane receptor for LPA which is a major growth factor from activated platelets. Thus, the interaction between platelets and late EPCs via the LPA-Edg-2 axis was assessed. Platelet supernatant boosted the pro-vasculogenic effects of GW501516, which was reversed by antagonist to PPAR-δ (GSK0660) or Edg (Ki16425). Both of in vivo Matrigel plug model and mouse skin punch-wound model demonstrated that the combination of platelets and PPAR-δ-activated late EPCs synergistically enhanced vascular regeneration. CONCLUSIONS: There exists a synergistic interaction between human platelets and late EPCs leading to vascular regeneration. This interaction consists of LPA from platelets and its receptor Edg-2 on the surface of EPCs and can be potentiated by PPAR-δ activation in EPCs. A PPAR-δ agonist is a good candidate to achieve vasculogenesis for ischemic vascular disease.

Vascular calcifying progenitor cells possess bidirectional differentiation potentials.

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.

Direct conversion of adult skin fibroblasts to endothelial cells by defined factors.

BACKGROUND: Cell-based therapies to augment endothelial cells (ECs) hold great therapeutic promise. Here, we report a novel approach to generate functional ECs directly from adult fibroblasts. METHODS AND RESULTS: Eleven candidate genes that are key regulators of endothelial development were selected. Green fluorescent protein (GFP)-negative skin fibroblasts were prepared from Tie2-GFP mice and infected with lentiviruses allowing simultaneous overexpression of all 11 factors. Tie2-GFP(+) cells (0.9%), representing Tie2 gene activation, were detected by flow cytometry. Serial stepwise screening revealed 5 key factors (Foxo1, Er71, Klf2, Tal1, and Lmo2) that were required for efficient reprogramming of skin fibroblasts into Tie2-GFP(+) cells (4%). This reprogramming strategy did not involve pluripotency induction because neither Oct4 nor Nanog was expressed after 5 key factor transduction. Tie2-GFP(+) cells were isolated using fluorescence-activated cell sorting and designated as induced ECs (iECs). iECs exhibited endothelium-like cobblestone morphology and expressed EC molecular markers. iECs possessed endothelial functions such as Bandeiraea simplicifolia-1 lectin binding, acetylated low-density lipoprotein uptake, capillary formation on Matrigel, and nitric oxide production. The epigenetic profile of iECs was similar to that of authentic ECs because the promoters of VE-cadherin and Tie2 genes were demethylated. mRNA profiling showed clustering of iECs with authentic ECs and highly enriched endothelial genes in iECs. In a murine model of hind-limb ischemia, iEC implantation increased capillary density and enhanced limb perfusion, demonstrating the in vivo viability and functionality of iECs. CONCLUSIONS: We demonstrated the first direct conversion of adult fibroblasts to functional ECs. These results suggest a novel therapeutic modality for cell therapy in ischemic vascular disease.

Clinical benefit of spironolactone in patients with acute decompensated heart failure and severe renal dysfunction: Data from the Korean Heart Failure Registry.

BACKGROUNDS: We investigated the relationship between spironolactone use and all-cause mortality in acute decompensated heart failure (ADHF) patients with severe renal dysfunction. The clinical benefit of spironolactone in the treatment of heart failure (HF) has been described in several large randomized clinical trials. However, its clinical benefits have not been studied in hospitalized ADHF patients with severe renal dysfunction (estimated glomerular filtration rate [eGFR] <45 mL/min per 1.73 m(2)). METHODS AND RESULTS: We retrospectively analyzed data from the Korean Heart Failure Registry. We included 1,035 ADHF patients with severe renal dysfunction. In Kaplan-Meier survival analysis, all-cause mortality in the spironolactone-treated group was significantly lower than that in the nonspironolactone group (18.1% vs 24.9%, respectively, log rank P = .028). However, spironolactone use was not an independent predictor after adjusting other HF risk factors (hazard ratio 0.974, 95% CI 0.681-1.392, P = .884) and after propensity score matching (P = .115). In subgroup analysis, the clinical benefit of spironolactone use was preserved in women, prehospital spironolactone use, the chronic kidney disease stage 3b (eGFR 30-44 mL/min per 1.73 m(2)), and the appropriate spironolactone use (eGFR ≥30 mL/min per 1.73 m(2) and K ≤5.0 mmol/L). CONCLUSION: The spironolactone therapy was not beneficial in ADHF patients with severe renal dysfunction after multivariable adjusting and propensity score matching. However, we reassured the current HF guidelines for spironolactone use and the clinical benefit in chronic kidney disease stage 3b should be assessed in future clinical trial.