This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases.This position paper focuses on the clinical management of valvular heart diseases with reference to the guidelines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee tried to reflect the recently published results on the topic of valvular heart diseases and Korean data by a systematic literature search based on validity and relevance. In part I of this article, we will review and discuss the current position of aortic valve disease in Korea.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases.This position paper focuses on the diagnosis and management of valvular heart diseases with referring to the guide‑ lines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee sought to reflect national data on the topic of valvular heart diseases published to date through a systematic literature search based on validity and relevance. In the part II of this article, we intend to pre‑ sent recommendations for diagnosis and treatment of mitral valve disease and tricuspid valve disease.

Background/Aims: Sex differences in the prognosis of heart failure (HF) have yielded inconsistent results, and data from Asian populations are even rare. This study aimed to investigate sex differences in clinical characteristics and long-term prognosis among Korean patients with HF. Methods: A total of 5,625 Korean patients hospitalized for acute HF were analyzed using a prospective multi-center registry database. Baseline clinical characteristics and long-term outcomes including HF readmission and death were compared between sexes. Results: Women were older than men and had worse symptoms with higher N-terminal pro B-type natriuretic peptide levels. Women had a significantly higher proportion of HF with preserved ejection fraction (HFpEF). There were no significant differences in in-hospital mortality and rate of guideline-directed medical therapies in men and women. During median follow- up of 3.4 years, cardiovascular death (adjusted hazard ratio [HR], 1.38; 95% confidence interval [CI], 1.07–1.78; p = 0.014), and composite outcomes of death and HF readmission (adjusted HR, 1.13; 95% CI, 1.01–1.27; p = 0.030) were significantly higher in men than women. When evaluating heart failure with reduced ejection fraction (HFrEF) and HFpEF separately, men were an independent risk factor of cardiovascular death in patients with HFrEF. Clinical outcome was not different between sexes in HFpEF. Conclusions In the Korean multi-center registry, despite having better clinical characteristics, men exhibited a higher risk of all-cause mortality and readmission for HF. The main cause of these disparities was the higher cardiovascular mortality rate observed in men compared to women with HFrEF.

Background and Objectives: This study aimed to investigate the association between cardiovascular events and 2 different levels of elevated on-treatment diastolic blood pressures (DBP) in the presence of achieved systolic blood pressure targets (SBP). Methods: A nation-wide population-based cohort study comprised 237,592 patients with hypertension treated. The primary endpoint was a composite of cardiovascular death, myocardial infarction, and stroke. Elevated DBP was defined according to the Seventh Report of Joint National Committee (JNC7; SBP <140 mmHg, DBP ≥90 mmHg) or to the 2017 American College of Cardiology/American Heart Association (ACC/AHA) definitions (SBP <130 mmHg, DBP ≥80 mmHg). Results: During a median follow-up of 9 years, elevated on-treatment DBP by the JNC7 definition was associated with an increased risk of the occurrence of primary endpoint compared with achieved both SBP and DBP (adjusted hazard ratio [aHR], 1.14; 95% confidence interval [CI], 1.05–1.24) but not in those by the 2017 ACC/AHA definition. Elevated ontreatment DBP by the JNC7 definition was associated with a higher risk of cardiovascular mortality (aHR, 1.42; 95% CI, 1.18–1.70) and stroke (aHR, 1.19; 95% CI, 1.08–1.30). Elevated on-treatment DBP by the 2017 ACC/AHA definition was only associated with stroke (aHR, 1.10;95% CI, 1.04–1.16). Similar results were seen in the propensity-score-matched cohort. Conclusion Elevated on-treatment DBP by the JNC7 definition was associated a high risk of major cardiovascular events, while elevated DBP by the 2017 ACC/AHA definition was only associated with a higher risk of stroke. The result of study can provide evidence of DBP targets in subjects who achieved SBP targets.

BACKGROUND AND OBJECTIVES: This study aimed to confirm the effects of traditional holidays on the incidence and outcomes of out-of-hospital cardiac arrest (OHCA) in South Korea. METHODS: We studied 95,066 OHCAs of cardiac cause from a nationwide, prospective study from the Korea OHCA Registry from January 2012 to December 2016. We compared the incidence of OHCA, in-hospital mortality, and neurologic outcomes between traditional holidays, Seollal (Lunar New Year's Day) and Chuseok (Korean Thanksgiving Day), and other day types (weekday, weekend, and public holiday). RESULTS: OHCA occurred more frequently on traditional holidays than on the other days. The median OHCA incidence were 51.0 (interquartile range [IQR], 44.0–58.0), 53.0 (IQR, 46.0–60.5), 52.5 (IQR, 45.3–59.8), and 60.0 (IQR, 52.0–69.0) cases/day on weekday, weekend, public holiday, and traditional holiday, respectively (p<0.001). The OHCA occurred more often at home rather than in public place, lesser bystander cardiopulmonary resuscitation (CPR) was performed, and the rate of cessation of CPR within 20 minutes without recovery of spontaneous circulation was higher on traditional holiday. After multivariable adjustment, traditional holiday was associated with higher in-hospital mortality (adjusted hazard ratio [HR], 1.339; 95% confidence interval [CI], 1.058–1.704; p=0.016) but better neurologic outcomes (adjusted HR, 0.503; 95% CI, 0.281–0.894; p=0.020) than weekdays. CONCLUSIONS: The incidence of OHCAs was associated with day types in a year. It occurred more frequently on traditional holidays than on other day types. It was associated with higher in-hospital mortality and favorable neurologic outcomes than weekday.
Cardiopulmonary Resuscitation ; Epidemiology ; Heart Arrest ; Holidays ; Hospital Mortality ; Incidence ; Korea ; Mortality ; Out-of-Hospital Cardiac Arrest ; Prospective Studies

BACKGROUND AND OBJECTIVES: Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). METHODS: The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. RESULTS: The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. CONCLUSIONS: The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.
Artificial Intelligence ; Early Diagnosis ; Echocardiography ; Electrocardiography ; Forests ; Heart Failure ; Heart ; Humans ; Learning ; Logistic Models ; Machine Learning ; Mass Screening ; ROC Curve

BACKGROUND AND OBJECTIVES: Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). METHODS: The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. RESULTS: The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. CONCLUSIONS The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.