European Heart Journal - Digital Health最新文献

{"title":"International evaluation of an artificial intelligence-powered ecg model detecting acute coronary occlusion myocardial infarction","authors":"R. Herman, H. P. Meyers, Stephen W Smith, D. Bertolone, A. Leone, Konstantinos Bermpeis, M. M. Viscusi, M. Belmonte, A. Demolder, V. Boza, B. Vavrik, V. Kresnakova, Andrej Iring, M. Martonak, Jakub Bahyl, Timea Kisova, D. Schelfaut, M. Vanderheyden, L. Perl, Emre Aslanger, R. Hatala, Wojtek Wojakowski, J. Bartunek, Emanuele Barbato","doi":"10.1093/ehjdh/ztad074","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad074","url":null,"abstract":"Majority of acute coronary syndromes (ACS) present without typical ST-elevation. One third of Non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery (occlusion myocardial infarction [OMI]), leading to poor outcomes due to delayed identification and invasive management. We sought to develop a versatile artificial intelligence (AI)-model detecting acute OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art diagnostic criteria. An AI model was developed using 18,616 ECGs from 10,543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. Primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3,254 ECGs from 2,222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve (AUC) of 0.938 (95% CI: 0.924-0.951) in identifying the primary OMI outcome, with superior performance (accuracy 90.9% [95% CI: 89.7-92.0], sensitivity 80.6% [95% CI: 76.8-84.0], specificity 93.7 [95% CI: 92.6-94.8]) compared to STEMI criteria (accuracy 83.6% [95% CI: 82.1-85.1], sensitivity 32.5% [95% CI: 28.4-36.6], specificity 97.7% [95% CI: 97.0-98.3]) and similar performance compared to ECG experts (accuracy 90.8% [95% CI: 89.5-91.9], sensitivity 73.0% [95% CI: 68.7-77.0], specificity 95.7% [95% CI: 94.7-96.6]). The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared to the STEMI criteria. This suggests its potential to improve ACS triage ensuring appropriate and timely referral for immediate revascularization.","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139219332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and internal validation of machine learning-based models and external validation of existing risk scores for outcome prediction in patients with ischemic stroke","authors":"Daniel Axford, Ferdous Sohel, V. Abedi, Ye Zhu, R. Zand, Ebrahim Barkoudah, Troy Krupica, Kingsley Iheasirim, U. M. Sharma, S. Dugani, Paul Y Takahashi, S. Bhagra, Mohammad H Murad, Gustavo Saposnik, M. Yousufuddin","doi":"10.1093/ehjdh/ztad073","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad073","url":null,"abstract":"We developed new ML models and externally validated existing statistical models (ischemic stroke predictive risk score [iScore] and totaled health risks in vascular events [THRIVE] scores) for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first AIS. In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models (random forest [RF], support vector machine [SVM], and extreme gradient boosting [XGBOOST]) and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11% and 34% of patients, respectively, reached the composite outcome. For the 90-day prediction, the area under the receiver operating characteristic curves (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method-based risk prediction tools for outcome prediction after AIS in new datasets.","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139247684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Data to Wisdom: Harnessing the Power of Multimodal Approach for Personalized Atherosclerotic Cardiovascular Risk Assessment","authors":"Sadeer Al-Kindi, Khurram Nasir","doi":"10.1093/ehjdh/ztad068","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad068","url":null,"abstract":"","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"53 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139261015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population data-based federated machine-learning improves automated echocardiographic quantification of cardiac structure and function – the AVE project","authors":"Caroline Morbach, Götz Gelbrich, M. Schreckenberg, Maike Hedemann, Dora Pelin, N. Scholz, O. Miljukov, Achim Wagner, Fabian Theisen, N. Hitschrich, Hendrik Wiebel, Daniel Stapf, Oliver Karch, Stefan Frantz, Peter U Heuschmann, Stefan Störk","doi":"10.1093/ehjdh/ztad069","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad069","url":null,"abstract":"Machine-learning (ML)-based automated measurement of echocardiography images emerged as an option to reduce observer variability. To improve the accuracy of a pre-existing automated reading tool (“original detector”) by federated ML-based re-training. AVE (Automatisierte Vermessung der Echokardiographie) was based on the echocardiography images of n = 4,965 participants of the population-based Characteristics and Course of Heart Failure Stages A-B and Determinants of Progression Cohort Study. We implemented federated ML: echocardiography images were read by the Academic CoreLab Ultrasound-based Cardiovascular Imaging at the University Hospital Würzburg (UKW). A random algorithm selected 3,226 participants for re-training of the original detector. According to data protection rules, generation of ground truth and ML training cycles took place within the UKW network. Only non-personal training weights were exchanged with the external cooperation partner for refinement of ML algorithms. Both the original detector as the re-trained detector were then applied to the echocardiograms of n = 563 participants not used for training. With regards to the human referent the re-trained detector revealed 1) superior accuracy when contrasted with the original detector´s performance as it arrived at significantly smaller mean differences in all but one parameter, and 2) smaller absolute difference between measurements when compared to a group of different human observers. Population data based ML in a federated ML set-up was feasible. The re-trained detector exhibited a much lower measurement variability than human readers. This gain in accuracy and precision strengthens the confidence into automated echocardiographic readings, which carries large potential for applications in various settings.","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"37 9-10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139275005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}