Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Few studies have addressed the predictive implications of right ventricular (RV) and pulmonary arterial (PA) coupling as assessed by echocardiography in patients with acute heart failure (AHF). This study aimed to ascertain the prognostic importance of RV-PA coupling in AHF cases and discern any divergence in its prognostic efficacy based on different heart failure (HF) phenotypes. Methods: We evaluated RV-PA coupling by measuring the ratio of right ventricular global longitudinal strain (RVGLS) to pulmonary arterial systolic pressure (PASP), termed the RVGLS/PASP ratio, and assessed its prognostic role using the STrain for Risk Assessment and Therapeutic Strategies in Patients with Acute Heart Failure registry. Results: From an AHF registry of 4312 patients, we analyzed the RVGLS/PASP ratio in 2,865 patients (1,449 men; age, 71.1±13.5 years). At a median follow-up of 35.0 months, 1,199 (41.8%) patients died. Remarkably, PASP (hazard ratio [HR], 1.012; p<0.001), RVGLS (HR, 1.019;p<0.001), and the RVGLS/PASP ratio (HR, 2.426; p<0.001) were statistically significant predictors of all-cause mortality in the univariate analysis. The RVGLS/PASP ratio was a significant predictor of all-cause mortality in all the HF phenotypes, including HF with reduced ejection fraction (HR, 2.124; p=0.002), HF with mildly reduced ejection fraction (HR, 2.733; p=0.021), and HF with preserved ejection fraction (HR, 2.134; p=0.006). Multivariate analysis after adjusting for clinical and echocardiographic variables revealed that the RVGLS/PASP ratio ≤0.32 was associated with a 36% increase in all-cause mortality (HR, 1.365; p<0.001). Conclusions Impaired RV-PA coupling, defined as an RVGLS/PASP ratio (≤0.32) was associated with an increased risk of mortality in patients with AHF across all HF phenotypes.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background: The association between renal dysfunction and cardiovascular outcomes has yet to be determined in patients with hypertrophic cardiomyopathy (HCM). We aimed to investigate whether mildly reduced renal function is associated with the prognosis in patients with HCM. Methods: Patients with HCM were enrolled at two tertiary HCM centers. Patients who were on dialysis, or had a previous history of heart failure (HF) or stroke were excluded. Patients were categorized into 3 groups by estimated glomerular filtration rate (eGFR): stage I (eGFR ≥ 90 mL/min/1.73 m2 , n = 538), stage II (eGFR 60–89 mL/min/1.73 m2 , n = 953), and stage III–V (eGFR < 60 mL/min/1.73 m2 , n = 265). Major adverse cardiovascular events (MACEs) were defined as a composite of cardiovascular death, hospitalization for HF (HHF), or stroke during median 4.0-year follow-up. Multivariable Cox regression model was used to adjust for covariates. Results: Among 1,756 HCM patients (mean 61.0 ± 13.4 years; 68.1% men), patients with stage III–V renal function had a significantly higher risk of MACEs (adjusted hazard ratio [aHR], 2.71; 95% confidence interval [CI], 1.39–5.27; P = 0.003), which was largely driven by increased incidence of cardiovascular death and HHF compared to those with stage I renal function. Even in patients with stage II renal function, the risk of MACE (vs. stage I: aHR, 2.21’ 95% CI, 1.23–3.96; P = 0.008) and HHF (vs. stage I: aHR, 2.62; 95% CI, 1.23–5.58; P = 0.012) was significantly increased. Conclusion This real-world observation showed that even mildly reduced renal function (i.e., eGFR 60–89 mL/min/1.73 m2 ) in patients with HCM was associated with an increased risk of MACEs, especially for HHF.

Background and Objectives: Early diastolic mitral annular tissue (e’) velocity is a commonly used marker of left ventricular (LV) diastolic function. This study aimed to investigate the prognostic implications of e’ velocity in patients with mitral regurgitation (MR). Methods: This retrospective cohort study included 1,536 consecutive patients aged <65 years with moderate or severe chronic primary MR diagnosed between 2009 and 2018. The primary and secondary outcomes were all-cause and cardiovascular mortality, respectively.According to the current guidelines, the cut-off value of e’ velocity was defined as 7 cm/s. Results: A total of 404 individuals were enrolled (median age, 51.0 years; 64.1% male; 47.8% severe MR). During a median 6.0-year follow-up, there were 40 all-cause mortality and 16 cardiovascular deaths. Multivariate analysis revealed a significant association between e’ velocity and all-cause death (adjusted hazard ratio [aHR], 0.770; 95% confidence interval [CI], 0.634–0.935; p=0.008) and cardiovascular death (aHR, 0.690; 95% CI, 0.477–0.998;p=0.049). Abnormal e’ velocity (≤7 cm/s) independently predicted all-cause death (aHR, 2.467; 95% CI, 1.170–5.200; p=0.018) and cardiovascular death (aHR, 5.021; 95% CI, 1.189–21.211; p=0.028), regardless of symptoms, LV dimension and ejection fraction. Subgroup analysis according to sex, MR severity, mitral valve replacement/repair, and symptoms, showed no significant interactions. Including e’ velocity in the 10-year risk score improved reclassification for mortality (net reclassification improvement [NRI], 0.154; 95% CI, 0.308– 0.910; p<0.001) and cardiovascular death (NRI, 1.018; 95% CI, 0.680–1.356; p<0.001). Conclusions In patients aged <65 years with primary MR, e’ velocity served as an independent predictor of all-cause and cardiovascular deaths.

Background and Objectives: The prognostic or safety implication of renin-angiotensinaldosterone system inhibitors (RASi) in hypertrophic cardiomyopathy (HCM) are not well established, mainly due to concerns regarding left ventricular outflow tract (LVOT) obstruction aggravation. We investigated the implications of RASi in a sizable number of HCM patients. Methods: We enrolled 2,104 consecutive patients diagnosed with HCM in 2 tertiary university hospitals and followed up for five years. RASi use was defined as the administration of RASi after diagnostic confirmation of HCM. The primary and secondary outcomes were all-cause mortality and hospitalization for heart failure (HHF). Results: RASi were prescribed to 762 patients (36.2%). During a median follow-up of 48.1months, 112 patients (5.3%) died, and 94 patients (4.5%) experienced HHF. Patients using RASi had less favorable baseline characteristics than those not using RASi, such as older age, more frequent history of comorbidities, and lower ejection fraction. Nonetheless, there was no difference in clinical outcomes between patients with and without RASi use (log-rank p=0.368 for all-cause mortality and log-rank p=0.443 for HHF). In multivariable analysis, patients taking RASi showed a comparable risk of all-cause mortality (hazard ratio [HR], 0.70, 95% confidence interval [CI], 0.43–1.14, p=0.150) and HHF (HR, 1.03, 95% CI, 0.63–1.70, p=0.900). In the subgroup analysis, there was no significant interaction of RASi use between subgroups stratified by LVOT obstruction, left ventricular (LV) ejection fraction, or maximal LV wall thickness. Conclusions RASi use was not associated with worse clinical outcomes. It might be safely administered in patients with HCM if clinically indicated.

Background: There is an incomplete understanding of the natural course of mild to moderate aortic stenosis (AS). We aimed to evaluate the natural course of patients with mild to moderate AS and its association with coronary artery disease (CAD). Methods: We retrospectively analyzed 787 patients diagnosed with mild to moderate AS using echocardiography between 2004 and 2010. Cardiac death and aortic valve replacement (AVR) for AS were assessed. Results: A median follow-up period was 92 months. Compared to the general population, patients with mild to moderate AS had a higher risk of cardiac death (hazard ratio [HR], 17.16; 95% confidence interval [CI], 13.65–21.59; P < 0.001). Established CAD was detected in 22.4% and associated with a significantly higher risk of cardiac mortality (adjusted HR, 1.62; 95% CI, 1.04–2.53; P = 0.033). The risk of cardiac death was lower when patients were taking statin (adjusted HR, 0.64; 95% CI, 0.41–0.98; P = 0.041), which was clear only after 7 years. Both patients with CAD and on statin tended to undergo more AVR, but the difference was not statistically significant (the presence of established CAD; adjusted HR, 1.63; 95% CI, 0.51–3.51; P = 0.214 and the use of statin; adjusted HR, 1.86; 95% CI, 0.76–4.58; P = 0.177). Conclusion Mild to moderate AS does not have a benign course. The presence of CAD and statin use may affect the long-term prognosis of patients with mild to moderate AS.