Background: B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-pro-BNP) are key biomarkers used for heart failure (HF) management. Although traditional auscultation lacks objective evaluation, the SSS01-series phonocardiogram enables rapid recording of heart sounds and ECG. We developed a deep-learning model to estimate plasma BNP levels from these non-invasive dynamic physiological signals, with the aim of validating the model’s performance with an external validation dataset and assessing its feasibility for clinical application.

Methods and Results: This multicenter study evaluated the estimated BNP (eBNP) model for predicting plasma BNP levels ≥100 pg/mL using 8 s of heart sound and ECG data. Validation was performed on an external validation dataset of 140 patients, achieving an area under the receiver operating characteristic curve (AUROC) of 0.895, with sensitivity and specificity of 84.3% and 82.9%, respectively. Subgroup analysis of patients with body mass index of 18.5–25 (n=127) showed more substantial predictive capability, with an AUROC of 0.959, sensitivity of 92.5%, and specificity of 84.8%.

Conclusions: The eBNP model demonstrated strong potential for non-invasive and rapid HF screening. Its simplicity and objectivity make it ideally suited for point-of-care testing, offering a promising approach for early HF diagnosis and detection monitoring of HF exacerbations. These findings, validated on datasets independent of training, highlight the model’s robustness across diverse clinical populations.

Background: The aim of this study was to retrain our existing deep learning-based fully automated aortic valve leaflets/root measurement algorithm, using computed tomography (CT) data for root dilatation (RD), and assess its clinical feasibility.

Methods and Results: 67 ECG-gated cardiac CT scans were retrospectively collected from 40 patients with RD to retrain the algorithm. An additional 100 patients’ CT data with aortic stenosis (AS, n=50) and aortic regurgitation (AR) with/without RD (n=50) were collected to evaluate the algorithm. 45 AR patients had RD. The algorithm provided patient-specific 3-dimensional aortic valve/root visualization. The measurements of 100 cases automatically obtained by the algorithm were compared with an expert’s manual measurements. Overall, there was a moderate-to-high correlation, with differences of 6.1–13.4 mm²for the virtual basal ring area, 1.1–2.6 mm for sinus diameter, 0.1–0.6 mm for coronary artery height, 0.2–0.5 mm for geometric height, and 0.9 mm for effective height, except for the sinotubular junction of the AR cases (10.3 mm) with an indefinite borderline over the dilated sinuses, compared with 2.1 mm in AS cases. The measurement time (122 s) per case by the algorithm was significantly shorter than those of the experts (618–1,126 s).

Conclusions: This fully automated algorithm can assist in evaluating aortic valve/root anatomy for planning surgical and transcatheter treatments while saving time and minimizing workload.

Background: Possible etiologies of out-of-hospital cardiac arrest (OHCA), including aortic dissection, ruptured aortic aneurysms, and pulmonary embolism, may be classified as non-cardiac causes. We investigated whether cardiac and non-cardiac OHCAs increased following the Kumamoto earthquake and whether the impact on OHCAs extended to regions far from the epicenter.

Methods and Results: We prospectively analyzed a nationwide registry of patients who experienced OHCAs between January 2013 and December 2019. Data from cases registered in 7 prefectures, including Kumamoto (Kyushu region; n=82,060), in the All-Japan Utstein Registry were analyzed for OHCAs of cardiac and non-cardiac origin. The numbers of OHCAs before and after the Kumamoto earthquake were compared using an interrupted time series analysis. The incidence of both cardiac (rate ratio [RR] 1.22) and non-cardiac (RR 1.27) OHCAs in Kumamoto Prefecture increased after the earthquake. The difference disappeared when the analysis was limited to patients with non-cardiac OHCAs with a clear cause of cardiac arrest. The number of cardiac and non-cardiac OHCAs did not increase in other prefectures within the Kyushu region.

Conclusions: The Kumamoto earthquake led to an increase in the incidence of cardiac and non-cardiac OHCAs. However, this was attenuated by increasing distance from the epicenter. Except for cardiac causes, cases complicated by earthquake-related events may include non-cardiac OHCAs due to vascular diseases that might be overlooked.

Background: This study aimed to identify risk factors associated with the implementation of veno-arterial extracorporeal membrane oxygenation (VA-ECMO) in patients with acute myocarditis and to develop a predictive model.

Methods and Results: This retrospective study included 841 patients from 7 hospitals in Korea with biopsy-proven or clinically suspected acute myocarditis. Logistic regression analysis was used to identify the clinical characteristics of patients who required VA-ECMO and to construct a scoring system to predict the implementation of VA-ECMO. Among the study population, 217 (25.8%) patients underwent VA-ECMO. The study population was divided into training (n=621) and testing (n=220) cohorts according to participating center. The final predictive model of VA-ECMO insertion derived from the training cohort included the following: initial mean blood pressure <65 mmHg, cardiac arrest, Glasgow Coma Scale score ≤12, platelet count <100×10³/mL, pulmonary congestion on chest X-ray, QRS interval ≥120 ms, left or right bundle branch block, and left ventricular ejection fraction <40%. Using this predictive model, a β coefficient-weighted Korean Acute Myocarditis (KAM) score was developed. External validation of the predictive model and KAM score using the testing cohort showed excellent discriminant ability (areas under the curve of 0.945 and 0.921, respectively).

Conclusions: A risk scoring system based on simple clinical and laboratory parameters at initial presentation could predict the implementation of VA-ECMO and clinical course in patients with acute myocarditis.

Background: Abdominal aortic aneurysm (AAA) is a vascular disease strongly associated with immune dysregulation and metabolic disturbances. Although lactate metabolism and its associated process, lactylation, have been implicated in various diseases, their specific role in AAA pathogenesis remains poorly understood.

Methods and Results: In this study, we used a multi-faceted approach, integrating single-cell and bulk RNA data analyses, with the objective of elucidating the interrelationship between lactylation and immune response in AAA patients. The result revealed significant heterogeneity in lactylation levels across different immune cell types. Cells with higher lactylation activity exhibited markedly elevated immune response scores. Differential expression and correlation analyses identified 65 lactylation-associated genes, which were further evaluated in the bulk RNA sequencing data to assess their relationship with the immune microenvironment in patients with AAA. Using 113 combinations of machine-learning algorithms, we identified 8 lactylation-related hub genes. The immune infiltration analysis demonstrated that these genes were linked to a multitude of immune cells. The animal experiments corroborated that Tnfsf8, Hist1 h2ag, Cd79b, Cd69, and Bank1 were upregulated in the AAA group, while Rpl36a and Rps29 were downregulated in the AAA group.

Conclusions: This study highlighted a potentially critical link between lactylation and immune dysregulation in AAA, thereby advancing our comprehension of the function of lactylation in AAA.