Персональные данные в автоматизированных системах финансовых организаций

Previous studies have shown that patients with Takotsubo syndrome (TS) have supranormal nitric oxide signaling, and post-mortem studies of TS heart samples revealed nitrosative stress. Therefore, we first showed in a female rat model that isoproterenol induces TS-like echocardiographic changes, evidence of nitrosative stress, and consequent activation of the energy-depleting enzyme poly(ADP-ribose) polymerase-1. We subsequently showed that pre-treatment with an inhibitor of poly(ADP-ribose) polymerase-1 ameliorated contractile abnormalities. These findings thus add to previous reports of aberrant β-adrenoceptor signaling (coupled with nitric oxide synthase activation) to elucidate mechanisms of impaired cardiac function in TS and point to potential methods of treatment.Sven Y. Surikow, Thanh H. Nguyen, Irene Stafford, Matthew Chapman, Sujith Chacko, Kuljit Singh, Giovanni Licari, Betty Raman, Darren J. Kelly, Yuan Zhang, Mark T. Waddingham, Doan T. Ngo, Alexander P. Bate, Su Jen Chua, Michael P. Frenneaux, John D. Horowit

Evidence of nitrosative stress within hearts of patients dying of Tako-tsubo cardiomyopathy

Abstract not availableS.Y. Surikow, B.Raman, J. Licari, K. Singh, T.H. Nguyen, J.D. Horowit

Takotsubo syndrome: Finally emerging from the shadows?

Online published-ahead-of-print 6 November 2020It is now 30 years since Japanese investigators first described Takotsubo Syndrome (TTS) as a disorder occurring mainly in ageing women, ascribing it to the impact of multivessel coronary artery spasm. During the intervening period, it has become clear that TTS involves relatively transient vascular injury, followed by prolonged myocardial inflammatory and eventually fibrotic changes. Hence symptomatic recovery is generally slow, currently an under-recognised issue. It appears that TTS is induced by aberrant post-β2-adrenoceptor signalling in the setting of "surge" release of catecholamines. Resultant activation of nitric oxide synthases and increased inflammatory vascular permeation lead to prolonged myocardial infiltration with macrophages and associated oedema formation. Initially, the diagnosis of TTS was made via exclusion of relevant coronary artery stenoses, plus the presence of regional left ventricular hypokinesis. However, detection of extensive myocardial oedema on cardiac MRI imaging offers a specific basis for diagnosis. No adequate methods are yet available for definitive diagnosis of TTS at hospital presentation. Other major challenges remaining in this area include understanding of the recently demonstrated association between TTS and antecedent cancer, the development of effective treatments to reduce risk of short-term (generally due to shock) and long-term mortality, and also to accelerate symptomatic recovery.Gao Jing, Thanh Ha Nguyen, Angela Kucia, Sai-Fei Liu, Sven Y. Surikow, Olivia Girolamo, Cher-Rin Chong, Yuliy Y. Chirkov, Karin Schenck-Gustafsson, Michael P. Frenneaux, John D. Horowit

Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction

IMPORTANCE Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied.Objectives To assess the utility of machine learning systems for automatic discrimination of TTS and AMI.Design, Settings, and Participants This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021.Exposure Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed.Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis.Results In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%).Conclusions and Relevance In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application.Cardiolog

Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction

IMPORTANCE Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied.Objectives To assess the utility of machine learning systems for automatic discrimination of TTS and AMI.Design, Settings, and Participants This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021.Exposure Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed.Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis.Results In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%).Conclusions and Relevance In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application

Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction

IMPORTANCE Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied. OBJECTIVES To assess the utility of machine learning systems for automatic discrimination of TTS and AMI. DESIGN, SETTINGS, AND PARTICIPANTS This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021. EXPOSURE Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed. MAIN OUTCOMES AND MEASURES Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis. RESULTS In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%). CONCLUSIONS AND RELEVANCE In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application