Predicting Long-Term Mortality in Patients With Acute Heart Failure by Using Machine Learning.

BACKGROUND: High mortality rates in patients with acute heart failure (AHF) necessitate proper risk stratification. However, risk-assessment tools for long-term mortality are largely lacking. We aimed to develop a machine-learning (ML)-based risk-prediction model for long-term all-cause mortality in patients admitted for AHF. METHODS AND RESULTS: The ML model, based on boosted a Cox regression algorithm (CoxBoost), was trained with 2704 consecutive patients hospitalized for AHF (median age 73 years, 55% male, and median left ventricular ejection fraction 38%). We selected 27 input variables, including 19 clinical features and 8 echocardiographic parameters, for model development. The best-performing model, along with pre-existing risk scores (BIOSTAT-CHF and AHEAD scores), was validated in an independent test cohort of 1608 patients. During the median 32 months (interquartile range 12-54 months) of the follow-up period, 1050 (38.8%) and 690 (42.9%) deaths occurred in the training and test cohorts, respectively. The area under the receiver operating characteristic curve (AUROC) of the ML model for all-cause mortality at 3 years was 0.761 (95% CI: 0.754-0.767) in the training cohort and 0.760 (95% CI: 0.752-0.768) in the test cohort. The discrimination performance of the ML model significantly outperformed those of the pre-existing risk scores (AUROC 0.714, 95% CI 0.706-0.722 by BIOSTAT-CHF; and 0.681, 95% CI 0.672-0.689 by AHEAD). Risk stratification based on the ML model identified patients at high mortality risk regardless of heart failure phenotypes. CONCLUSIONS: The ML-based mortality-prediction model can predict long-term mortality accurately, leading to optimal risk stratification of patients with AHF.

Angiographic complete revascularization versus incomplete revascularization in patients with diabetes mellitus.

BACKGROUND: Considering the nature of diabetes mellitus (DM) in coronary artery disease, it is unclear whether complete revascularization is beneficial or not in patients with DM. We investigated the clinical impact of angiographic complete revascularization in patients with DM. METHODS: A total of 5516 consecutive patients (2003 patients with DM) who underwent coronary stenting with 2nd generation drug-eluting stent were analyzed. Angiographic complete revascularization was defined as a residual SYNTAX (SYNergy between percutaneous coronary intervention with TAXus and cardiac surgery) score of 0. The patient-oriented composite outcome (POCO, including all-cause death, any myocardial infarction, and any revascularization) and target lesion failure (TLF) at three years were analyzed. RESULTS: Complete revascularization was associated with a reduced risk of POCO in DM population [adjusted hazard ratio (HR) 0.70, 95% confidence interval (CI) 0.52-0.93, p = 0.016], but not in non-DM population (adjusted HR 0.90, 95% CI 0.69-1.17, p = 0.423). The risk of TLF was comparable between the complete and incomplete revascularization groups in both DM (adjusted HR 0.75, 95% CI 0.49-1.16, p = 0.195) and non-DM populations (adjusted HR 1.11, 95% CI 0.75-1.63, p = 0.611). The independent predictors of POCO were incomplete revascularization, multivessel disease, left main disease and low ejection fraction in the DM population, and old age, peripheral vessel disease, and low ejection fraction in the non-DM population. CONCLUSIONS: The clinical benefit of angiographic complete revascularization is more prominent in patients with DM than those without DM after three years of follow-up. Relieving residual disease might be more critical in the DM population than the non-DM population. Trial registration The Grand Drug-Eluting Stent registry NCT03507205.

Relevance of anatomical, plaque, and hemodynamic characteristics of non-obstructive coronary lesions in the prediction of risk for acute coronary syndrome.

OBJECTIVES: We explored the anatomical, plaque, and hemodynamic characteristics of high-risk non-obstructive coronary lesions that caused acute coronary syndrome (ACS). METHODS: From the EMERALD study which included ACS patients with available coronary CT angiography (CCTA) before the ACS, non-obstructive lesions (percent diameter stenosis < 50%) were selected. CCTA images were analyzed for lesion characteristics by independent CCTA and computational fluid dynamics core laboratories. The relative importance of each characteristic was assessed by information gain. RESULTS: Of the 132 lesions, 24 were the culprit for ACS. The culprit lesions showed a larger change in FFRCT across the lesion (ΔFFRCT) than non-culprit lesions (0.08 ± 0.07 vs 0.05 ± 0.05, p = 0.012). ΔFFRCT showed the highest information gain (0.051, 95% confidence interval [CI] 0.050-0.052), followed by low-attenuation plaque (0.028, 95% CI 0.027-0.029) and plaque volume (0.023, 95% CI 0.022-0.024). Lesions with higher ΔFFRCT or low-attenuation plaque showed an increased risk of ACS (hazard ratio [HR] 3.25, 95% CI 1.31-8.04, p = 0.010 for ΔFFRCT; HR 2.60, 95% CI 1.36-4.95, p = 0.004 for low-attenuation plaque). The prediction model including ΔFFRCT, low-attenuation plaque and plaque volume showed the highest ability in ACS prediction (AUC 0.725, 95% CI 0.724-0.727). CONCLUSION: Non-obstructive lesions with higher ΔFFRCT or low-attenuation plaque showed a higher risk of ACS. The integration of anatomical, plaque, and hemodynamic characteristics can improve the noninvasive prediction of ACS risk in non-obstructive lesions. KEY POINTS: • Change in FFR CT across the lesion (ΔFFR CT ) was the most important predictor of ACS risk in non-obstructive lesions. • Non-obstructive lesions with higher ΔFFR CT or low-attenuation plaque were associated with a higher risk of ACS. • The integration of anatomical, plaque, and hemodynamic characteristics can improve the noninvasive prediction of ACS risk.

Clinical Factors Associated with Obstructive Coronary Artery Disease in Patients with Out-of-Hospital Cardiac Arrest: Data from the Korean Cardiac Arrest Research Consortium (KoCARC) Registry.

BACKGROUND: Although coronary artery disease (CAD) is a major cause of out-of-hospital cardiac arrest (OHCA), there has been no convinced data on the necessity of routine invasive coronary angiography (ICA) in OHCA. We investigated clinical factors associated with obstructive CAD in OHCA. METHODS: Data from 516 OHCA patients (mean age 58 years, 83% men) who underwent ICA after resuscitation was obtained from a nation-wide OHCA registry. Obstructive CAD was defined as the lesions with diameter stenosis ≥ 50% on ICA. Independent clinical predictors for obstructive CAD were evaluated using multiple logistic regression analysis, and their prediction performance was compared using area under the receiver operating characteristic curve with 10,000 repeated random permutations. RESULTS: Among study patients, 254 (49%) had obstructive CAD. Those with obstructive CAD were older (61 vs. 55 years, P < 0.001) and had higher prevalence of hypertension (54% vs. 36%, P < 0.001), diabetes mellitus (29% vs. 21%, P = 0.032), positive cardiac enzyme (84% vs. 74%, P = 0.010) and initial shockable rhythm (70% vs. 61%, P = 0.033). In multiple logistic regression analysis, old age (≥ 60 years) (odds ratio [OR], 2.01; 95% confidence interval [CI], 1.36-3.00; P = 0.001), hypertension (OR, 1.74; 95% CI, 1.18-2.57; P = 0.005), positive cardiac enzyme (OR, 1.72; 95% CI, 1.09-2.70; P = 0.019), and initial shockable rhythm (OR, 1.71; 95% CI, 1.16-2.54; P = 0.007) were associated with obstructive CAD. Prediction ability for obstructive CAD increased proportionally when these 4 factors were sequentially combined (P < 0.001). CONCLUSION: In patients with OHCA, those with old age, hypertension, positive cardiac enzyme and initial shockable rhythm were associated with obstructive CAD. Early ICA should be considered in these patients.

Performance of ECG-Derived Digital Biomarker for Screening Coronary Occlusion in Resuscitated Out-of-Hospital Cardiac Arrest Patients: A Comparative Study between Artificial Intelligence and a Group of Experts.

Acute coronary syndrome is a significant part of cardiac etiology contributing to out-of-hospital cardiac arrest (OHCA), and immediate coronary angiography has been proposed to improve survival. This study evaluated the effectiveness of an AI algorithm in diagnosing near-total or total occlusion of coronary arteries in OHCA patients who regained spontaneous circulation. Conducted from 1 July 2019 to 30 June 2022 at a tertiary university hospital emergency department, it involved 82 OHCA patients, with 58 qualifying after exclusions. The AI used was the Quantitative ECG (QCG™) system, which provides a STEMI diagnostic score ranging from 0 to 100. The QCG score's diagnostic performance was compared to assessments by two emergency physicians and three cardiologists. Among the patients, coronary occlusion was identified in 24. The QCG score showed a significant difference between occlusion and non-occlusion groups, with the former scoring higher. The QCG biomarker had an area under the curve (AUC) of 0.770, outperforming the expert group's AUC of 0.676. It demonstrated 70.8% sensitivity and 79.4% specificity. These findings suggest that the AI-based ECG biomarker could predict coronary occlusion in resuscitated OHCA patients, and it was non-inferior to the consensus of the expert group.

Integrative Assessment of Cardiopulmonary Fitness Using Cardiopulmonary Exercise Test With Supine Bicycle Echocardiography in Patients Presenting Dyspnea.

BACKGROUND: Cardiopulmonary exercise test (CPET) with supine bicycle echocardiography (SBE) enables comprehensive physiologic assessment during exercise. We characterized cardiopulmonary fitness by integrating CPET-SBE parameters and evaluated its prognostic value in patients presenting with dyspnea. METHODS AND RESULTS: We retrospectively reviewed 473 consecutive patients who underwent CPET-SBE for dyspnea evaluation. A dimensionality reduction process was applied, transforming 24 clinical and CPET-SBE parameters into a 2-dimensional feature map, followed by patient clustering based on the data distribution. Clinical and exercise features were compared among the clusters in addition to the 5-year risk of clinical outcome (a composite of cardiovascular death and heart failure hospitalization). Maximum exercise effort (R >1) was achieved in 95% of cases. Through dimensionality reduction, 3 patient clusters were derived: Group 1 (n=157), 2 (n=104), and 3 (n=212). Median age and female proportion increased from Group 1 to 2, and 3, although resting echocardiography parameters showed no significant abnormalities among the groups. There was a worsening trend in the exercise response from Group 1 to 2 and 3, including left ventricular diastolic function, oxygen consumption, and ventilatory efficiency. During follow-up (median 6.0 [1.6-10.4] years), clinical outcome increased from Group 1 to 2 and 3 (5-year rate 3.7% versus 7.0% versus 13.0%, respectively; log-rank P=0.02), with higher risk in Group 2 (hazard ratio, 1.94 [95% CI, 0.52-7.22]) and Group 3 (3.92 [1.34-11.42]) compared with Group 1. CONCLUSIONS: Comprehensive evaluation using CPET-SBE can reveal distinct characteristics of cardiopulmonary fitness in patients presenting with dyspnea, potentially enhancing outcome prediction.

Impact of changes in left heart geometry on predicting new-onset atrial fibrillation in patients with hypertension.

BACKGROUND: Hypertension-induced left ventricular hypertrophy (LVH) increases end-diastolic LV pressure and contributes to left atrial enlargement (LAE), which are associated with development of atrial fibrillation. However, the impact of LVH and LAE and their regression following antihypertensive therapy on atrial fibrillation incidence remains unclear. METHODS: This retrospective analysis included consecutive patients with sinus rhythm who underwent echocardiography at hypertension diagnosis and after 6-18 months between 2006 and 2021 at tertiary care centres in Korea. LVH was defined as LV mass index greater than 115 g/m2 (men) and greater than 95 g/m2 (women), and LAE was defined as LA volume index greater than 42 ml/m2. The occurrence of new-onset atrial fibrillation (NOAF) was assessed in relation to changes in LVH and LAE status. RESULTS: Among the 1464 patients included, 163 (11.1%) developed NOAF during a median 63.8 [interquartile range (IQR) 35.9-128.5] months of surveillance period. New-onset LVH [adjusted hazard ratio (aHR) 1.88, 95% confidence interval (CI) 1.20-2.94, P = 0.006] and LAE (aHR 1.89, 95% CI 1.05-3.40, P = 0.034) were significant predictors of NOAF. Conversely, regression of LVH (aHR 0.51, 95% CI 0.28-0.91, P = 0.022) or LAE (aHR 0.30, 95% CI 0.15-0.63, P = 0.001) was associated with a reduced risk for developing NOAF. Patients with both LVH and LAE at follow-up echocardiography had a higher risk for NOAF (aHR 4.30, 95% CI 2.81-6.56, P < 0.001) than those with either LVH or LAE or those with neither. CONCLUSION: The changes in left heart geometry can serve as a predictive marker for NOAF in patients with hypertension.

Age-dependent implications of left ventricular hypertrophy regression in patients with hypertension.

Left ventricular hypertrophy (LVH) is a significant risk factor for cardiovascular mortality and morbidity in patients with hypertension. However, the effect of age on LVH regression or persistence and its differential prognostic value remain unclear. Therefore, we investigated the clinical implications of LVH regression in 1847 patients with hypertension and echocardiography data (at baseline and during antihypertensive treatment at an interval of 6-18 months) according to age. LVH was defined as a left ventricular mass index (LVMI) > 115 g/m2 and >95 g/m2 in men and women, respectively. LVH prevalence at baseline was not different according to age (age < 65 years: 42.6%; age ≥65 years: 45.7%; p = 0.187), but LVH regression was more frequently observed in the younger group (36.4% vs. 27.5%; p = 0.008). Spline curves and multiple linear regression analysis showed a significant relationship between reductions in systolic blood pressure and LVMI in the younger group (ß = 0.425; p < 0.001), but not the elderly group (ß = 0.044; p = 0.308). LVH regression was associated with a lower risk of the study outcome (composite of cardiovascular death and hospitalization for heart failure) regardless of age. In conclusion, the association between the reduction in blood pressure and LVH regression was prominent in patients with age < 65 years, but not in those with age ≥65 years. However, an association between LVH regression and lower risk of cardiovascular death and hospitalization for heart failure was observed regardless of patient age, suggesting the prognostic value of the LVH regression not only in the younger patients but also in elderly patients.

Artificial intelligence-enhanced electrocardiography analysis as a promising tool for predicting obstructive coronary artery disease in patients with stable angina.

Aims: The clinical feasibility of artificial intelligence (AI)-based electrocardiography (ECG) analysis for predicting obstructive coronary artery disease (CAD) has not been sufficiently validated in patients with stable angina, especially in large sample sizes. Methods and results: A deep learning framework for the quantitative ECG (QCG) analysis was trained and internally tested to derive the risk scores (0-100) for obstructive CAD (QCGObstCAD) and extensive CAD (QCGExtCAD) using 50 756 ECG images from 21 866 patients who underwent coronary artery evaluation for chest pain (invasive coronary or computed tomography angiography). External validation was performed in 4517 patients with stable angina who underwent coronary imaging to identify obstructive CAD. The QCGObstCAD and QCGExtCAD scores were significantly increased in the presence of obstructive and extensive CAD (all P < 0.001) and with increasing degrees of stenosis and disease burden, respectively (all P trend < 0.001). In the internal and external tests, QCGObstCAD exhibited a good predictive ability for obstructive CAD [area under the curve (AUC), 0.781 and 0.731, respectively] and severe obstructive CAD (AUC, 0.780 and 0.786, respectively), and QCGExtCAD exhibited a good predictive ability for extensive CAD (AUC, 0.689 and 0.784). In the external test, the QCGObstCAD and QCGExtCAD scores demonstrated independent and incremental predictive values for obstructive and extensive CAD, respectively, over that with conventional clinical risk factors. The QCG scores demonstrated significant associations with lesion characteristics, such as the fractional flow reserve, coronary calcification score, and total plaque volume. Conclusion: The AI-based QCG analysis for predicting obstructive CAD in patients with stable angina, including those with severe stenosis and multivessel disease, is feasible.

An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI.

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.

Characteristics, Predictors, and Clinical Outcomes in Heart Failure With Reduced Ejection Fraction According to a 1-Year Left Ventricular Ejection Fraction Following Sacubitril/Valsartan Treatment.

BACKGROUND: Optimal medical treatment can lead to improvement in left ventricular ejection fraction (LVEF) in patients with heart failure with reduced EF (HFrEF). We investigated the characteristics, predictors, and outcomes of HFrEF according to the 1-year LVEF following angiotensin receptor-neprilysin inhibitors therapy (ARNI). METHODS AND RESULTS: Using the STRATS-HF-ARNI (Strain for Risk Assessment and Therapeutic Strategies in Patients With Heart Failure Treated With Angiotensin Receptor-Neprilysin Inhibitor) registry, we identified 1074 patients with HFrEF who took ARNI and underwent baseline and 1-year echocardiography. Patients were classified as HF with improved ejection fraction (HFimpEF) and persistent HFrEF (perHFrEF) (1-year LVEF >40% and ≤40%). The primary and secondary outcomes were all-cause and cardiac mortality from the 1-year follow-up. Among 1074 included patients, 498 (46.4%) had HFimpEF, and 576 (53.6%) had perHFrEF. Older age, male sex, and large LV end-diastolic volumes were positive predictors of perHFrEF, whereas atrial fibrillation and high systolic blood pressure were identified as inverse predictors. Patients with HFimpEF showed lower all-cause and cardiac mortality rates (both log-rank P<0.001). In the multivariable analysis, perHFrEF (hazard ratio, 2.402 [95% CI, 1.251-4.610]; P=0.008) was an independent predictor of poor outcomes. The risk of all-cause mortality decreased as the 1-year LVEF increased up to 40%; however, no additional risk reduction was observed beyond 40%. Compared with patients taking renin-angiotensin-aldosterone system inhibitors in the STRATS-AHF (Strain for Risk Assessment and Therapeutic Strategies in Patients With Acute Heart Failure) registry, those in the STRATS-HF-ARNI registry demonstrated better outcomes in both HFimpEF and perHFrEF. CONCLUSIONS: Patients with HFimpEF had better prognosis than those with perHFrEF, and ARNI treatment in HFrEF could be more beneficial than renin-angiotensin-aldosterone system inhibitors for both HFimpEF and perHFrEF. REGISTRATION: URL: https://www.who.int/clinical-trials-registry-platform; Unique identifier: KCT0008098.

AI derived ECG global longitudinal strain compared to echocardiographic measurements.

Left ventricular (LV) global longitudinal strain (LVGLS) is versatile; however, it is difficult to obtain. We evaluated the potential of an artificial intelligence (AI)-generated electrocardiography score for LVGLS estimation (ECG-GLS score) to diagnose LV systolic dysfunction and predict prognosis of patients with heart failure (HF). A convolutional neural network-based deep-learning algorithm was trained to estimate the echocardiography-derived GLS (LVGLS). ECG-GLS score performance was evaluated using data from an acute HF registry at another tertiary hospital (n = 1186). In the validation cohort, the ECG-GLS score could identify patients with impaired LVGLS (≤ 12%) (area under the receiver-operating characteristic curve [AUROC], 0.82; sensitivity, 85%; specificity, 59%). The performance of ECG-GLS in identifying patients with an LV ejection fraction (LVEF) < 40% (AUROC, 0.85) was comparable to that of LVGLS (AUROC, 0.83) (p = 0.08). Five-year outcomes (all-cause death; composite of all-cause death and hospitalization for HF) occurred significantly more frequently in patients with low ECG-GLS scores. Low ECG-GLS score was a significant risk factor for these outcomes after adjustment for other clinical risk factors and LVEF. The ECG-GLS score demonstrated a meaningful correlation with the LVGLS and is effective in risk stratification for long-term prognosis after acute HF, possibly acting as a practical alternative to the LVGLS.

Artificial intelligence-enhanced automation of left ventricular diastolic assessment: a pilot study for feasibility, diagnostic validation, and outcome prediction.

Background: Evaluating left ventricular diastolic function (LVDF) is crucial in echocardiography; however, the complexity and time demands of current guidelines challenge clinical use. This study aimed to develop an artificial intelligence (AI)-based framework for automatic LVDF assessment to reduce subjectivity and improve accuracy and outcome prediction. Methods: We developed an AI-based LVDF assessment framework using a nationwide echocardiographic dataset from five tertiary hospitals. This framework automatically identifies views, calculates diastolic parameters, including mitral inflow and annular velocities (E/A ratio, e' velocity, and E/e' ratio), maximal tricuspid regurgitation velocity, left atrial (LA) volume index, and left atrial reservoir strain (LARS). Subsequently, it grades LVDF according to guidelines. The AI-framework was validated on an external dataset composed of randomly screened 173 outpatients who underwent transthoracic echocardiography with suspicion for diastolic dysfunction and 33 individuals from medical check-ups with normal echocardiograms at Seoul National University Bundang Hospital, tertiary medical center in Korea, between May 2012 and June 2022. Additionally, we assessed the predictive value of AI-derived diastolic parameters and LVDF grades for a clinical endpoint, defined as a composite of all-cause death and hospitalization for heart failure, using Cox-regression risk modelling. Results: In an evaluation with 200 echocardiographic examinations (167 suspected diastolic dysfunction patients, 33 controls), it achieves an overall accuracy of 99.1% in identifying necessary views. Strong correlations (Pearson coefficient 0.901-0.959) were observed between AI-derived and manually-derived measurements of diastolic parameters, including LARS as well as conventional parameters. When following the guidelines, whether utilizing AI-derived or manually-derived parameters, the evaluation of LVDF consistently showed high concordance rates (94%). However, both methods exhibited lower concordance rates with the clinician's prior assessments (77.5% and 78.5%, respectively). Importantly, both AI-derived and manually-derived LVDF grades independently demonstrated significant prognostic value [adjusted hazard ratio (HR) =3.03; P=0.03 and adjusted HR =2.75; P=0.04, respectively] for predicting clinical outcome. In contrast, the clinician's prior grading lost its significance as a prognostic indicator after adjusting for clinical risk factors (adjusted HR =1.63; P=0.36). AI-derived LARS values significantly decreased with worsening LVDF (P for trend <0.001), and low LARS (<17%) was associated with increased risk for the clinical outcome (Log-rank P=0.04) relative to that for preserved LARS (≥17%). Conclusions: Our AI-based approach for automatic LVDF assessment on echocardiography is feasible, potentially enhancing clinical diagnosis and outcome prediction.

Feasibility of Artificial Intelligence-Based Electrocardiography Analysis for the Prediction of Obstructive Coronary Artery Disease in Patients With Stable Angina: Validation Study.

BACKGROUND: Despite accumulating research on artificial intelligence-based electrocardiography (ECG) algorithms for predicting acute coronary syndrome (ACS), their application in stable angina is not well evaluated. OBJECTIVE: We evaluated the utility of an existing artificial intelligence-based quantitative electrocardiography (QCG) analyzer in stable angina and developed a new ECG biomarker more suitable for stable angina. METHODS: This single-center study comprised consecutive patients with stable angina. The independent and incremental value of QCG scores for coronary artery disease (CAD)-related conditions (ACS, myocardial injury, critical status, ST-elevation myocardial infarction, and left ventricular dysfunction) for predicting obstructive CAD confirmed by invasive angiography was examined. Additionally, ECG signals extracted by the QCG analyzer were used as input to develop a new QCG score. RESULTS: Among 723 patients with stable angina (median age 68 years; male: 470/723, 65%), 497 (69%) had obstructive CAD. QCG scores for ACS and myocardial injury were independently associated with obstructive CAD (odds ratio [OR] 1.09, 95% CI 1.03-1.17 and OR 1.08, 95% CI 1.02-1.16 per 10-point increase, respectively) but did not significantly improve prediction performance compared to clinical features. However, our new QCG score demonstrated better prediction performance for obstructive CAD (area under the receiver operating characteristic curve 0.802) than the original QCG scores, with incremental predictive value in combination with clinical features (area under the receiver operating characteristic curve 0.827 vs 0.730; P<.001). CONCLUSIONS: QCG scores developed for acute conditions show limited performance in identifying obstructive CAD in stable angina. However, improvement in the QCG analyzer, through training on comprehensive ECG signals in patients with stable angina, is feasible.

The Risk of Heart Failure Progression in Patients With Patent Foramen Ovale: Differential Risk Associated With Device Closure.

BACKGROUND: A patent foramen ovale (PFO) can unload left atrial pressure via an interatrial shunt. We investigated whether device closure of PFO is associated with a subsequent risk of heart failure (HF), particularly in patients with structural heart disease or atrial fibrillation (AF). METHODS: We enrolled 4,804 consecutive patients who underwent transesophageal echocardiography at tertiary medical centers in Korea between 2007 and 2019. The primary outcome was the 4-year risk of HF hospitalization. Underlying structural heart disease was determined by echocardiography. RESULTS: A PFO was observed in 981 (20.4%) patients, where 161 underwent device closure. During follow-up (median, 3.5 [1.4-6.4] years), the primary outcome was lower in patients with PFO than in those without (2.6% vs 4.0%; adjusted hazard ratio [aHR], 0.65; 95% CI, 0.45-0.94; P = .021). Among the patients with PFO, the primary outcome was higher in the device closure group than in the no-closure group (5.5% vs 1.2%; aHR, 5.59; 95% CI, 4.26-7.34; P < .001). A consistent result was found in patients with structural heart disease or AF (9.6% vs 3.9%; aHR, 2.55; 95% CI, 1.95-3.33; P < .001), demonstrating an increased risk of the primary outcome proportionate to the number of combined structural abnormalities. However, no significant association was observed between the primary outcome and PFO closure in those without structural heart disease or AF (1.7% vs 1.5%; aHR, 1.22; 95% CI, 0.99-1.50; P = .054). CONCLUSION: Patients with underlying structural heart disease or AF may be predisposed to symptomatic HF progression after PFO closure. Therefore, careful medical surveillance with optimal risk management is needed in these patients.

Endocardial versus whole-myocardial tracking global longitudinal strain analysis in patients with hypertrophic cardiomyopathy: A preliminary comparative study.

BACKGROUND AND OBJECTIVES: We investigated whether the feasibility of left ventricular (LV) global longitudinal strain (GLS) in hypertrophic cardiomyopathy (HCM) varies according to the methodology (e.g. endocardial vs. whole myocardial tracking techniques). METHODS: We retrospectively analyzed 111 consecutive patients with HCM (median age, 58 years; male, 68.5%) who underwent both transthoracic echocardiography (TTE) and cardiac magnetic resonance imaging (apical 29.7%, septal 33.3%, and diffuse or mixed 37.0%). TTE-whole myocardial and TTE-endocardial GLS were measured and compared in terms of association with late gadolinium enhancement (LGE) extent and discrimination performance for extensive LGE (>15% of the LV myocardium). RESULTS: Although TTE-whole myocardial and TTE-endocardial GLS were significantly correlated, absolute TTE-endocardial GLS values (19.3 [16.2-21.9] %) were higher than TTE-whole myocardial GLS values (13.3[10.9-15.6] %, p<0.001). Both TTE-derived GLS parameters were significantly correlated with the LGE extent and independently associated with extensive LGE (odds ratio [OR] 1.30, p = 0.022; and OR 1.24, p = 0.013, respectively). Discrimination performance for extensive LGE was comparable between TTE-whole myocardial and TTE-endocardial GLS (area under the curve [AUC], 0.747 and 0.754, respectively, pdifference = 0.610). However, among patients with higher LV mass index (>70 g/m2), only TTE-whole myocardial GLS correlated with LGE extent and was independently associated with extensive LGE (OR 1.35, p = 0.042), while TTE-endocardial GLS did not. Additionally, TTE-whole myocardial GLS had better discrimination performance for extensive LGE than TTE-endocardial GLS (AUC, 0.705 and 0.668, respectively, pdifference = 0.006). CONCLUSION: TTE-derived GLS using either the endocardial or whole myocardial tracking technique is feasible in patients with HCM. However, in those with severe hypertrophy, TTE-whole myocardial GLS is better than TTE-endocardial GLS.

Clinical and Vessel Characteristics Associated With Hard Outcomes After PCI and Their Combined Prognostic Implications.

Background Cardiac death or myocardial infarction still occurs in patients undergoing contemporary percutaneous coronary intervention (PCI). We aimed to identify adverse clinical and vessel characteristics related to hard outcomes after PCI and to investigate their individual and combined prognostic implications. Methods and Results From an individual patient data meta-analysis of 17 cohorts of patients who underwent post-PCI fractional flow reserve measurement after drug-eluting stent implantation, 2081 patients with available clinical and vessel characteristics were analyzed. The primary outcome was cardiac death or target-vessel myocardial infarction at 2 years. The mean age of patients was 64.2±10.2 years, and the mean angiographic percent diameter stenosis was 63.9%±14.3%. Among 11 clinical and 8 vessel features, 4 adverse clinical characteristics (age ≥65 years, diabetes, chronic kidney disease, and left ventricular ejection fraction <50%) and 2 adverse vessel characteristics (post-PCI fractional flow reserve ≤0.80 and total stent length ≥54 mm) were identified to independently predict the primary outcome (all P<0.05). The number of adverse vessel characteristics had additive predictability for the primary end point to that of adverse clinical characteristics (area under the curve 0.72 versus 0.78; P=0.03) and vice versa (area under the curve 0.68 versus 0.78; P=0.03). The cumulative event rate increased in the order of none, either, and both of adverse clinical characteristics ≥2 and adverse vessel characteristics ≥1 (0.3%, 2.4%, and 5.3%; P for trend <0.01). Conclusions In patients undergoing drug-eluting stent implantation, adverse clinical and vessel characteristics were associated with the risk of cardiac death or target-vessel myocardial infarction. Because these characteristics showed independent and additive prognostic value, their integrative assessment can optimize post-PCI risk stratification. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT04684043. www.crd.york.ac.uk/prospero/. Unique Identifier: CRD42021234748.

Risk of earlier atherosclerotic cardiovascular disease in women with low bone mineral density.

Low bone mineral density (BMD) is associated with higher risk of atherosclerotic cardiovascular disease (ASCVD) in women. We investigated whether the association between low BMD and ASCVD differs according to the age at ASCVD occurrence. We retrospectively analyzed 7932 women aged 50-65 years who underwent dual-energy X-ray absorptiometry. ASCVD was defined as a composite of ASCVD death, myocardial infarction, and ischemic stroke. When we classified participants into no event (n = 7803), early ASCVD (< 70 years) (n = 97), and late ASCVD (≥ 70 years) (n = 32) groups, age gradually increased across groups (median, 58, 60, and 63 years, respectively). However, the estimated BMD T-score at the age of 65 years was lowest in the early ASCVD group (median - 0.9, - 1.1, and - 0.5, respectively). Lower BMD was an independent predictor for early ASCVD (adjusted hazard ratio [95% confidence interval]: 1.34 [1.08-1.67] per 1-SD decrease in T-score), but not for late ASCVD (0.88 [0.60-1.30]). The inverse trend between early ASCVD risk and BMD T-score was consistent regardless of the number of accompanied clinical risk factors. Thus, low BMD is an independent predictor for early ASCVD in women. BMD evaluation can provide prognostic benefit for risk stratification for early ASCVD.

Feasibility of the contraction-relaxation coupling index in outcome prediction for patients with acute heart failure.

AIMS: Contemporary heart failure (HF) classification based on left ventricular (LV) ejection fraction is limited for comprehensive assessment of LV function. We aimed to validate the feasibility of the contraction-relaxation coupling index (CRC) as a novel predictor for clinical outcomes in patients with acute HF. METHODS AND RESULTS: A total of 3266 consecutive patients (median age: 74 years, 53% male) with acute HF were included. CRC was defined as the ratio of end-diastolic elastance (LV end-diastolic pressure/stroke volume) to end-systolic elastance (LV end-systolic pressure/end-systolic volume). The risk for 1 year composite endpoint of all-cause mortality or hospitalization for HF (primary outcome) was compared after group categorization using CRC tertiles (Tertile 1: CRC ≤ 0.17, Tertile 2: 0.17 < CRC ≤ 0.40, and Tertile 3: 0.40 < CRC). The median CRC was 0.3 and the median LVEF was 42%. After adjustment for clinical and echocardiographic covariates, CRC was an independent predictor for the primary outcome (hazard ratio [HR]: 1.74, 95% confidence interval [CI]: 1.47-2.07 in Tertile 3 and HR: 1.21, 95% CI: 1.02-1.44 in Tertile 2 when compared with Tertile 1; HR: 1.23, 95% CI: 1.14-1.33 per one-standard deviation increment in CRC). The risk model with CRC showed better performance in outcome discrimination than the model with LVEF (c-statistic 0.701 vs. 0.699, P for difference <0.001). Patients with higher CRC demonstrated better effectiveness of neurohormonal blockade for the primary outcome compared with those with lower CRC (HR: 0.38, 95% CI: 0.29-0.50 in Tertile 3 and HR: 0.67, 95% CI: 0.52-0.89 in Tertile 1). CONCLUSIONS: CRC provides an independent value for outcome prediction in patients with acute HF. CRC would be a sensitive indicator for prognostic risk stratification and for predicting treatment response to the neurohormonal blockade.

Prognostic Implications of Mechanical Phenotypes in Heart Failure Characterized by 3-Chamber Strain Echocardiography.

Background Heart failure (HF) involves dysfunction of the left ventricle (LV) as well as left atrium and right ventricle. We characterized mechanical phenotypes of HF using 3-chamber strain echocardiography and compared their clinical outcomes. Methods and Results We retrospectively analyzed 3574 patients (median age, 74 years; male 52.8%) with acute HF who underwent 3-chamber strain echocardiography. Patients were classified as with LV, left atrium, or right ventricle myopathy if their corresponding strain values (LV global longitudinal strain, left atrium reservoir strain, and right ventricle global longitudinal strain) were lower than median cutoffs, respectively. The mechanical phenotypes of individual patients were characterized according to the combined myopathy. The primary outcome was a composite end point of 5-year all-cause mortality and HF hospitalization. During follow-up (median, 25.8 months), the primary outcome occurred in 1877 (52.5%) patients. Three-chamber strain values were independent predictors for the primary outcome. An incremental trend was observed for the primary outcome, along with the increasing numbers of combined myopathy. Each mechanical phenotype exhibited an increased risk of the primary outcome, with the highest risk observed in patients with 3-chamber myopathy (hazard ratio, 1.67 [95% CI, 1.42-1.96]). The prognostic significance of the mechanical phenotypes was feasible across the conventional HF subtypes stratified by LV ejection fraction. In HF with preserved ejection fraction, the presence of left atrium and right ventricle myopathy significantly increased the primary outcome, regardless of combined left ventricle myopathy. Conclusions Assessment of 3-chamber strain in HF enables characterization of distinctive mechanical phenotypes, which provides an independent prognostic value that may support long-term risk stratification.