Arterial walls stiffen with age. The most consistent and well-reported changes are luminal enlargement with wall thickening and a reduction of elastic properties at the level of large elastic ...arteries. Longstanding arterial pulsation in the central artery causes elastin fiber fatigue and fracture. Increased vascular calcification and endothelial dysfunction are also characteristic of arterial aging. These changes lead to increased pulse wave velocity, especially along central elastic arteries, and increases in systolic blood pressure and pulse pressure. Vascualar aging is accelerated by coexsiting cardiovascular risk factors, such as hypertension, metabolic syndrome and diabetes. Vascular aging is an independent risk factor for cardiovascular disease, from atherosclerosis to target organ damage, including coronary artery disease, stroke and heart failure. Various strategies, especially controlling hypertension, show benefit in preventing, delaying or attenuating vascular aging. (Circ J 2010; 74: 2257-2262)
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.
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.
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Severe cardiac damage following myocardial infarction (MI) causes excessive inflammation, which sustains tissue damage and often induces adverse cardiac remodeling toward cardiac function impairment ...and heart failure. Timely resolution of post‐MI inflammation may prevent cardiac remodeling and development of heart failure. Cell therapy approaches for MI are time‐consuming and costly, and have shown marginal efficacy in clinical trials. Here, nanoparticles targeting the immune system to attenuate excessive inflammation in infarcted myocardium are presented. Liposomal nanoparticles loaded with MI antigens and rapamycin (L‐Ag/R) enable effective induction of tolerogenic dendritic cells presenting the antigens and subsequent induction of antigen‐specific regulatory T cells (Tregs). Impressively, intradermal injection of L‐Ag/R into acute MI mice attenuates inflammation in the myocardium by inducing Tregs and an inflammatory‐to‐reparative macrophage polarization, inhibits adverse cardiac remodeling, and improves cardiac function. Nanoparticle‐mediated blocking of excessive inflammation in infarcted myocardium may be an effective intervention to prevent the development of post‐MI heart failure.
Liposomal nanoparticles loaded with both antigen and rapamycin (L‐Ag/Rs) are fabricated as novel therapeutics for myocardial infarction (MI). L‐Ag/Rs generate regulatory T cells and reparative M2 macrophages in infarcted heart, inducing immune tolerance with antigen‐specific manner. The reparative phase elicited by L‐Ag/R inhibits excessive inflammation and adverse cardiac remodeling in infarct region, improving cardiac function.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF).
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).
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.
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Aims
The Korean Acute Heart Failure registry (KorAHF) aims to evaluate the clinical characteristics, management, hospital course, and long‐term outcomes of patients hospitalized for acute heart ...failure syndrome (AHFS) in Korea.
Methods and results
This is a prospective observational multicentre cohort study funded by the Korea National Institute of Health. Patients hospitalized for AHFS in 10 tertiary university hospitals across the country have been consecutively enrolled since March 2011. The study is expected to complete the scheduled enrolment of 5000 patients some time in 2014, and follow‐up is planned through 2016. As of April 2012, the interim analysis of 2066 consecutive subjects was performed to understand the baseline characteristics of the population. The mean age was 69 ± 14 years; 55% were male; and 50% were de novo heart failure. The mean left ventricular ejection fraction (LVEF) was 40 ± 18%. Ischaemia was both the leading cause (38%) and the most frequent aggravating factor (26%) of AHFS. ACE inhibitors/ARBs and beta‐blockers were prescribed at discharge in 65% and 51% of the patients, respectively. In‐hospital mortality was 5.2%, and 0.9% of patients received urgent heart transplantation. Low blood pressure and azotaemia were the most important predictors of in‐hospital mortality. The post‐discharge 30‐day and 180‐day all‐cause mortality were 1.2% and 9.2%, respectively.
Conclusions
Our analysis reveals that the prognosis of AHFS in Korea is poor and that there are specific features, including lower blood pressures at admission and lower rates of heart failure related to hypertension, compared with other registries. Adherence to current guidelines should be improved.
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BFBNIB, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Abstract
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.
Background Many patients with heart failure ( HF ) with reduced ejection fraction ( HF r EF ) 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 HF r EF ( LVEF ≤40% at baseline and at 1-year follow-up), HF i EF ( LVEF ≤40% at baseline and improved up to 40% at 1-year follow-up), HF with midrange ejection fraction (LVEF between 40% and <50%), and HF with preserved ejection fraction ( LVEF ≥50%). The primary outcome was 4-year all-cause mortality from the time of HF i EF diagnosis. Among 1509 HF r EF patients who had echocardiography 1 year after index hospitalization, 720 (31.3%) were diagnosed as having HF i EF . Younger age, female sex, de novo HF , hypertension, atrial fibrillation, and β-blocker use were positive predictors and diabetes mellitus and ischemic heart disease were negative predictors of HF i EF . During 4-year follow-up, patients with HF i EF showed lower mortality than those with persistent HF r EF in univariate, multivariate, and propensity-score-matched analyses. β-Blockers, but not renin-angiotensin system inhibitors or mineralocorticoid receptor antagonists, were associated with a reduced all-cause mortality risk (hazard ratio: 0.59; 95% CI , 0.40-0.87; P=0.007). Benefits for outcome seemed similar among patients receiving low- or high-dose β-blockers (log-rank, P=0.304). Conclusions HF i EF is a distinct HF phenotype with better clinical outcomes than other phenotypes. The use of β-blockers may be beneficial for these patients. Clinical Trial Registration URL : https://www.clinicaltrials.gov . Unique identifier: NCT01389843.
Heart Transplantation in Asia Lee, Hae-Young; Oh, Byung-Hee
Circulation Journal,
2017-Apr-25, Volume:
81, Issue:
5
Journal Article
Peer reviewed
Open access
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.
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).
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.
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, 0.5930.585-0.603). 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).
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.
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