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Objective: We recently demonstrated that chronic treatment with interleukin-1β (IL-1β), a major inflammatory cytokine found in atherosclerotic lesions, induces coronary arteriosclerotic changes and vasospastic responses to serotonin and histamine in pigs in vivo and that those responses are partially mediated by platelet-derived growth factor (PDGF). This study was designed to examine, first, whether the effects of IL-1β are also partially mediated by fibroblast growth factor-2 (FGF-2), which is another important growth factor in atherosclerotic lesions, and, secondly, whether chronic treatment with FGF-2per se also induces histological and functional changes in porcine coronary arteries in vivo.Methods: Porcine coronary arteries were aseptically wrapped with cotton mesh absorbing IL-1β with or without neutralizing antibody to FGF-2. In a separate series of experiments porcine coronary arteries were chronically treated with FGF-2 itself in the same manner. Coronary vascular responses in vivo and histological changes were examined 2 weeks after the operation.Results: Coronary vasospastic responses to serotonin and histamine and neointimal formation were induced at the site of the coronary artery where IL-1β was chronically and locally applied. These responses were significantly suppressed by co-treatment with a neutralizing antibody to FGF-2 but not by that with non-immune IgG. Immunostaining revealed the presence of FGF-2 in the endothelial cells, the thickened intima and the media at the IL-1β-treated site. Furthermore, chronic treatment with FGF-2 also induced coronary vasospastic responses to serotonin and histamine and neointimal formation.Conclusions: These results suggest that the vascular effects of IL-1β may also be mediated by FGF-2 in our swine model and that chronic treatment with FGF-2 also causes coronary arteriosclerotic changes and vasospastic responses in vivo. [ABSTRACT FROM PUBLISHER]

Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model's performance for AF detection using 8-lead (I, II, and V1-6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856-0.888) and an odds ratio of 15.24 (95% CI: 12.42-18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856-0.886) with an odds ratio of 14.34 (95% CI: 11.64-17.67). Conclusions: We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model's performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG., Competing Interests: S.S. received lecture fees from Daiichi Sankyo and Bristol-Myers Squibb. T.Y. received research funds and/or lecture fees from Daiichi Sankyo, Bayer Yakuhin, Bristol-Myers Squibb, Pfizer, Nippon Boehringer Ingelheim, Eisai, Mitsubishi Tanabe Pharm, Ono Pharmaceutical, and Toa Eiyo. The remaining authors have nothing to disclose., (Copyright © 2024, THE JAPANESE CIRCULATION SOCIETY.)

Background: This study sought to develop an artificial intelligence-derived model to detect the dilated phase of hypertrophic cardiomyopathy (dHCM) on digital electrocardiography (ECG) and to evaluate the performance of the model applied to multiple-lead or single-lead ECG., Methods: This is a retrospective analysis using a single-center prospective cohort study (Shinken Database 2010-2017, n = 19,170). After excluding those without a normal P wave on index ECG (n = 1,831) and adding dHCM patients registered before 2009 (n = 39), 17,378 digital ECGs were used. Totally 54 dHCM patients were identified of which 11 diagnosed at baseline, 4 developed during the time course, and 39 registered before 2009. The performance of the convolutional neural network (CNN) model for detecting dHCM was evaluated using eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs with the five-fold cross validation method., Results: The area under the curve (AUC) of the CNN model to detect dHCM (n = 54) with eight-lead ECG was 0.929 (standard deviation [SD]: 0.025) and the odds ratio was 38.64 (SD 9.10). Among the single-lead and double-lead ECGs, the AUC was highest with the single lead of V5 (0.953 [SD: 0.038]), with an odds ratio of 58.89 (SD:68.56)., Conclusion: Compared with the performance of eight-lead ECG, the most similar performance was achieved with the model with a single V5 lead, suggesting that this single-lead ECG can be an alternative to eight-lead ECG for the screening of dHCM., Competing Interests: Dr. Suzuki received lecture fees from Daiichi Sankyo and Bristol-Myers Squibb. Dr. Yamashita received research funds and/or lecture fees from Daiichi Sankyo, Bayer Yakuhin, Bristol-Myers Squibb, Pfizer, Nippon Boehringer Ingelheim, Eisai, Mitsubishi Tanabe Pharm, Ono Pharmaceutical, and Toa Eiyo. J Motogi, T Umemoto, W Matsuzawa, T Takayanagi, A Hyodo, and K Satoh are employee at Nihon Kohden Corporation.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shinya Suzuki reports a relationship with Public Interest Incorporated Foundation The Cardiovascular Institute that includes: speaking and lecture fees. Takeshi Yamashita reports a relationship with Public Interest Incorporated Foundation The Cardiovascular Institute that includes: funding grants and speaking and lecture fees., (© 2023 The Author(s).)

Background: There is increasing evidence that 12-lead electrocardiograms (ECG) can be used to predict biological age, which is associated with cardiovascular events. However, the utility of artificial intelligence (AI)-predicted age using ECGs remains unclear., Methods: Using a single-center database, we developed an AI-enabled ECG using 17 042 sinus rhythm ECGs (SR-ECG) to predict chronological age (CA) with a convolutional neural network that yields AI-predicted age. Using the 5-fold cross validation method, AI-predicted age deriving from the test dataset was yielded for all ECGs. The incidence by AgeDiff and the areas under the curve by receiver operating characteristic curve with AI-predicted age for cardiovascular events were analyzed., Results: During the mean follow-up period of 460.1 days, there were 543 cardiovascular events. The annualized incidence of cardiovascular events was 2.24 %, 2.44 %, and 3.01 %/year for patients with AgeDiff < -6, -6 to ≤6, and >6 years, respectively. The areas under the curve for cardiovascular events with CA and AI-predicted age, respectively, were 0.673 and 0.679 (Delong's test, P = 0.388) for all patients; 0.642 and 0.700 (P = 0.003) for younger patients (CA < 60 years); and 0.584 and 0.570 (P = 0.268) for older patients (CA ≥ 60 years)., Conclusions: AI-predicted age using 12-lead ECGs showed superiority in predicting cardiovascular events compared with CA in younger patients, but not in older patients., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr Suzuki received research funding from Mitsubishi Tanabe Pharm and Daiichi Sankyo. Dr Yamashita received research funds and/or lecture fees from Daiichi Sankyo, Bayer Yakuhin, Bristol Myers Squibb, Pfizer, Nippon Boehringer Ingelheim, Eisai, Mitsubishi Tanabe Pharm, Ono Pharmaceutical, and Toa Eiyo. The remaining authors have no conflicts of interest to declare., (© 2023 The Author(s).)

Background: This study aimed to increase the knowledge on how to enhance the performance of artificial intelligence (AI)-enabled electrocardiography (ECG) to detect atrial fibrillation (AF) on sinus rhythm ECG (SR-ECG)., Methods: It is a retrospective analysis of a single-center, prospective cohort study (Shinken Database). We developed AI-enabled ECG using SR-ECG to predict AF with a convolutional neural network (CNN). Among new patients in our hospital (n = 19,170), 276 AF label (having ECG on AF [AF-ECG] in the ECG database) and 1896 SR label with following three conditions were identified in the derivation dataset: (1) without structural heart disease, (2) in AF label, SR-ECG was taken within 31 days from AF-ECG, and (3) in SR label, follow-up ≥ 1,095 days. Three patterns of AF label were analyzed by timing of SR-ECG to AF-ECG (before/after/before-or-after, CNN algorithm 1 to 3). The outcome measurement was area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. As an extra-testing dataset, the performance of AI-enabled ECG was tested in patients with structural heart disease., Results: The AUC of AI-enabled ECG with CNN algorithm 1, 2, and 3 in the derivation dataset was 0.83, 0.88, and 0.86, respectively; when tested in patients with structural heart disease, 0.75, 0.81, and 0.78, respectively., Conclusion: We confirmed high performance of AI-enabled ECG to detect AF on SR-ECG in patients without structural heart disease. The performance enhanced especially when SR-ECG after index AF-ECG was included in the algorithm, which was consistent in patients with structural heart disease., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper: [Dr. Suzuki received research funding from Mitsubishi Tanabe Pharm and Daiichi Sankyo. Dr. Yamashita received research funds and/or lecture fees from Daiichi Sankyo, Bayer Yakuhin, Bristol-Myers Squibb, Pfizer, Nippon Boehringer Ingelheim, Eisai, Mitsubishi Tanabe Pharm, Ono Pharmaceutical, and Toa Eiyo.]., (© 2022 The Authors.)