European Heart Journal - Digital Health最新文献_第4页

Decoding 2.3 Million ECGs: Interpretable Deep Learning for Advancing Cardiovascular Diagnosis and Mortality Risk Stratification 解码 230 万张心电图：可解释的深度学习促进心血管诊断和死亡率风险分层

European Heart Journal - Digital Health Pub Date : 2024-02-19 DOI: 10.1093/ehjdh/ztae014

Lei Lu, Tingting Zhu, A. H. Ribeiro, Lei A. Clifton, Erying Zhao, Jiandong Zhou, A. L. Ribeiro, Yuanyuan Zhang, David A. Clifton

{"title":"Decoding 2.3 Million ECGs: Interpretable Deep Learning for Advancing Cardiovascular Diagnosis and Mortality Risk Stratification","authors":"Lei Lu, Tingting Zhu, A. H. Ribeiro, Lei A. Clifton, Erying Zhao, Jiandong Zhou, A. L. Ribeiro, Yuanyuan Zhang, David A. Clifton","doi":"10.1093/ehjdh/ztae014","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae014","url":null,"abstract":"\u0000 Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a dataset of 2,322,513 ECGs collected from 1,558,772 patients with 7 years of follow-up, we developed a deep learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hyper- tension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (0.963-0.965), and 0.839 (0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis, and the advancement in mortality risk stratification; In addition, the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available.","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"256 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140451926","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}

引用次数: 0

Initial experience, safety and feasibility using remote access or onsite technical support for complex ablation procedures: Results of the REMOTE study 使用远程访问或现场技术支持进行复杂消融手术的初步经验、安全性和可行性：REMOTE 研究结果

European Heart Journal - Digital Health Pub Date : 2024-02-19 DOI: 10.1093/ehjdh/ztae013

C. Heeger, J. Vogler, Charlotte Eitel, M. Feher, S. Popescu, B. Kirstein, Sascha Hatahet, Benham Subin, K. Kuck, R. Tilz

{"title":"Initial experience, safety and feasibility using remote access or onsite technical support for complex ablation procedures: Results of the REMOTE study","authors":"C. Heeger, J. Vogler, Charlotte Eitel, M. Feher, S. Popescu, B. Kirstein, Sascha Hatahet, Benham Subin, K. Kuck, R. Tilz","doi":"10.1093/ehjdh/ztae013","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae013","url":null,"abstract":"\u0000 \u0000 \u0000 Electroanatomical mapping (EAM) systems are essential for treatment of cardiac arrhythmias. The EAM system is usually operated by qualified staff or field technical engineers (FTE) from the control room. Novel remote support technology allows for remote access of EAM via online services. Remote access increases the flexibility of the electrophysiological lab, reduces travel time and overcomes hospital access limitations especially during the COVID-19 pandemic. Here we report on the feasibility and safety of EAM remote access for cardiac ablation procedures.\u0000 \u0000 \u0000 \u0000 Mapping and ablation were achieved by combining the EnsiteXTM EAM system and the integrated EnsiteTM Connect Remote Support software, together with an integrated audiovisual solution system for remote support (Medinbox). Communication between the operator and the remote support was achieved using an incorporated internet-based common communication platform (ZoomTM), headphones and high-resolution cameras.\u0000 \u0000 \u0000 \u0000 We investigated 50 remote access assisted consecutive electrophysiological procedures from 09/2022 to 02/2023 (remote group). The data was compared to matched patients (n=50) with onsite support from the control room (control group). The median procedure time was 100min (76, 120) (remote) vs. 86min (60, 110) (control), p=0.090. The procedural success (both groups 100%, p=0.999) and complication rate (remote: 2%, control: 0%, p=0.553) were comparable between the groups. Travel burden could be reduced by 11,280 km.\u0000 \u0000 \u0000 \u0000 Remote access for EAM was feasible and safe in this single center study. Procedural data were comparable to procedures with onsite support. In the future, this new solution might have a great impact on facilitating electrophysiological procedures.\u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"4 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958609","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}

引用次数: 0

Remote Monitoring of AF Recurrence using mHealth Technology (REMOTE-AF) 利用移动医疗技术远程监测房颤复发（REMOTE-AF）

European Heart Journal - Digital Health Pub Date : 2024-02-12 DOI: 10.1093/ehjdh/ztae011

G. Adasuriya, A. Barsky, I. Kralj-Hans, S. Mohan, S. Gill, Z. Chen, J. Jarman, D. Jones, H. Valli, G. Gkoutos, V. Markides, W. Hussain, T. Wong, D. Kotecha, S. Haldar

{"title":"Remote Monitoring of AF Recurrence using mHealth Technology (REMOTE-AF)","authors":"G. Adasuriya, A. Barsky, I. Kralj-Hans, S. Mohan, S. Gill, Z. Chen, J. Jarman, D. Jones, H. Valli, G. Gkoutos, V. Markides, W. Hussain, T. Wong, D. Kotecha, S. Haldar","doi":"10.1093/ehjdh/ztae011","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae011","url":null,"abstract":"\u0000 \u0000 \u0000 This proof-of-concept study sought to evaluate changes in heart rate (HR) obtained from a consumer wearable device and compare against implanted loop recorder (ILR)-detected recurrence of atrial fibrillation (AF) and atrial tachycardia (AT) after AF ablation.\u0000 \u0000 \u0000 \u0000 REMOTE-AF (NCT05037136) was a prospectively designed sub study of the CASA-AF randomised controlled trial (NCT04280042). Participants without a permanent pacemaker had an implantable loop recorder (ILR) implanted at their index ablation procedure for longstanding persistent atrial fibrillation. HR and step count were continuously monitored using photoplethysmography (PPG) from a commercially available wrist-worn wearable. PPG recorded HR data was pre-processed with noise filtration and episodes at 1-minute intervals over 30 minutes of heart rate elevations (Z-score = 2) were compared to corresponding ILR data.\u0000 \u0000 \u0000 \u0000 Thirty-five patients were enrolled, with mean age 70.3 +/- 6.8 yrs and median follow-up 10 months (IQR 8-12 months). ILR analysis revealed seventeen out of thirty-five patients (49%) had recurrence of AF/AT. Compared with ILR recurrence, wearable-derived elevations in HR ≥ 110 beats per minute had a sensitivity of 95.3%, specificity 54.1%, positive predictive value (PPV) 15.8%, negative predictive value (NPV) 99.2% and overall accuracy 57.4%. With PPG recorded HR elevation spikes (non-exercise related), the sensitivity was 87.5%, specificity 62.2%, PPV 39.2%, NPV 92.3% and overall accuracy 64.0% in the entire patient cohort. In the AF/AT recurrence only group, sensitivity was 87.6%, specificity 68.3%, PPV 53.6%, NPV 93.0% and overall accuracy 75.0%.\u0000 \u0000 \u0000 \u0000 Consumer wearable devices have the potential to contribute to arrhythmia detection after AF ablation.\u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139784678","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}

引用次数: 0

Remote Monitoring of AF Recurrence using mHealth Technology (REMOTE-AF) 利用移动医疗技术远程监测房颤复发（REMOTE-AF）

European Heart Journal - Digital Health Pub Date : 2024-02-12 DOI: 10.1093/ehjdh/ztae011

G. Adasuriya, A. Barsky, I. Kralj-Hans, S. Mohan, S. Gill, Z. Chen, J. Jarman, D. Jones, H. Valli, G. Gkoutos, V. Markides, W. Hussain, T. Wong, D. Kotecha, S. Haldar

{"title":"Remote Monitoring of AF Recurrence using mHealth Technology (REMOTE-AF)","authors":"G. Adasuriya, A. Barsky, I. Kralj-Hans, S. Mohan, S. Gill, Z. Chen, J. Jarman, D. Jones, H. Valli, G. Gkoutos, V. Markides, W. Hussain, T. Wong, D. Kotecha, S. Haldar","doi":"10.1093/ehjdh/ztae011","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae011","url":null,"abstract":"\u0000 \u0000 \u0000 This proof-of-concept study sought to evaluate changes in heart rate (HR) obtained from a consumer wearable device and compare against implanted loop recorder (ILR)-detected recurrence of atrial fibrillation (AF) and atrial tachycardia (AT) after AF ablation.\u0000 \u0000 \u0000 \u0000 REMOTE-AF (NCT05037136) was a prospectively designed sub study of the CASA-AF randomised controlled trial (NCT04280042). Participants without a permanent pacemaker had an implantable loop recorder (ILR) implanted at their index ablation procedure for longstanding persistent atrial fibrillation. HR and step count were continuously monitored using photoplethysmography (PPG) from a commercially available wrist-worn wearable. PPG recorded HR data was pre-processed with noise filtration and episodes at 1-minute intervals over 30 minutes of heart rate elevations (Z-score = 2) were compared to corresponding ILR data.\u0000 \u0000 \u0000 \u0000 Thirty-five patients were enrolled, with mean age 70.3 +/- 6.8 yrs and median follow-up 10 months (IQR 8-12 months). ILR analysis revealed seventeen out of thirty-five patients (49%) had recurrence of AF/AT. Compared with ILR recurrence, wearable-derived elevations in HR ≥ 110 beats per minute had a sensitivity of 95.3%, specificity 54.1%, positive predictive value (PPV) 15.8%, negative predictive value (NPV) 99.2% and overall accuracy 57.4%. With PPG recorded HR elevation spikes (non-exercise related), the sensitivity was 87.5%, specificity 62.2%, PPV 39.2%, NPV 92.3% and overall accuracy 64.0% in the entire patient cohort. In the AF/AT recurrence only group, sensitivity was 87.6%, specificity 68.3%, PPV 53.6%, NPV 93.0% and overall accuracy 75.0%.\u0000 \u0000 \u0000 \u0000 Consumer wearable devices have the potential to contribute to arrhythmia detection after AF ablation.\u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"57 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844584","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}

引用次数: 0

Risks and Benefits of Sharing Patient Information on Social Media: A Digital Dilemma 在社交媒体上共享患者信息的风险和益处：数字困境

European Heart Journal - Digital Health Pub Date : 2024-02-12 DOI: 10.1093/ehjdh/ztae009

Robert M A van der Boon, A. J. Camm, C. Aguiar, E. Biassin, G. Breithardt, H. Bueno, I. Drossart, N. Hoppe, E. Kamenjasevic, R. Ladeiras-Lopes, P. McrGreavy, P. Lanzer, R. Vidal-Perez, N. Bruining

{"title":"Risks and Benefits of Sharing Patient Information on Social Media: A Digital Dilemma","authors":"Robert M A van der Boon, A. J. Camm, C. Aguiar, E. Biassin, G. Breithardt, H. Bueno, I. Drossart, N. Hoppe, E. Kamenjasevic, R. Ladeiras-Lopes, P. McrGreavy, P. Lanzer, R. Vidal-Perez, N. Bruining","doi":"10.1093/ehjdh/ztae009","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae009","url":null,"abstract":"\u0000 Social media (SoMe) has witnessed remarkable growth and emerged as a dominant method of communication worldwide. Platforms such as Facebook, X (formerly Twitter), LinkedIn, Instagram, TikTok, and YouTube have become important tools of the digital native generation. In the field of medicine, particularly cardiology, attitudes towards SoMe have shifted, and professionals increasingly utilize it to share scientific findings, network with experts, and enhance teaching and learning. Notably, SoMe is being leveraged for teaching purposes, including the sharing of challenging and intriguing cases. However, sharing patient data, including photos or images, online carries significant implications and risks, potentially compromising individual privacy both online and offline. Privacy and data protection are fundamental rights within European Union (EU) treaties, and the General Data Protection Regulation (GDPR) serves as the cornerstone of data protection legislation. The GDPR outlines crucial requirements, such as obtaining “consent” and implementing “anonymization”, that must be met before sharing sensitive and patient-identifiable information. Additionally, it is vital to consider the patient perspective and prioritize ethical and social considerations when addressing challenges associated with sharing patient information on SoMe platforms. Given the absence of a peer review process and clear guidelines, we present an initial approach, a code of conduct, and recommendations for the ethical use of SoMe. In conclusion, this comprehensive review underscores the importance of a balanced approach that ensures patient privacy and upholds ethical standards while harnessing the immense potential of SoMe to advance cardiology practice and facilitate knowledge dissemination.","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"223 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139843469","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}

引用次数: 0

Risks and Benefits of Sharing Patient Information on Social Media: A Digital Dilemma 在社交媒体上共享患者信息的风险和益处：数字困境

European Heart Journal - Digital Health Pub Date : 2024-02-12 DOI: 10.1093/ehjdh/ztae009

Robert M A van der Boon, A. J. Camm, C. Aguiar, E. Biassin, G. Breithardt, H. Bueno, I. Drossart, N. Hoppe, E. Kamenjasevic, R. Ladeiras-Lopes, P. McrGreavy, P. Lanzer, R. Vidal-Perez, N. Bruining

{"title":"Risks and Benefits of Sharing Patient Information on Social Media: A Digital Dilemma","authors":"Robert M A van der Boon, A. J. Camm, C. Aguiar, E. Biassin, G. Breithardt, H. Bueno, I. Drossart, N. Hoppe, E. Kamenjasevic, R. Ladeiras-Lopes, P. McrGreavy, P. Lanzer, R. Vidal-Perez, N. Bruining","doi":"10.1093/ehjdh/ztae009","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae009","url":null,"abstract":"\u0000 Social media (SoMe) has witnessed remarkable growth and emerged as a dominant method of communication worldwide. Platforms such as Facebook, X (formerly Twitter), LinkedIn, Instagram, TikTok, and YouTube have become important tools of the digital native generation. In the field of medicine, particularly cardiology, attitudes towards SoMe have shifted, and professionals increasingly utilize it to share scientific findings, network with experts, and enhance teaching and learning. Notably, SoMe is being leveraged for teaching purposes, including the sharing of challenging and intriguing cases. However, sharing patient data, including photos or images, online carries significant implications and risks, potentially compromising individual privacy both online and offline. Privacy and data protection are fundamental rights within European Union (EU) treaties, and the General Data Protection Regulation (GDPR) serves as the cornerstone of data protection legislation. The GDPR outlines crucial requirements, such as obtaining “consent” and implementing “anonymization”, that must be met before sharing sensitive and patient-identifiable information. Additionally, it is vital to consider the patient perspective and prioritize ethical and social considerations when addressing challenges associated with sharing patient information on SoMe platforms. Given the absence of a peer review process and clear guidelines, we present an initial approach, a code of conduct, and recommendations for the ethical use of SoMe. In conclusion, this comprehensive review underscores the importance of a balanced approach that ensures patient privacy and upholds ethical standards while harnessing the immense potential of SoMe to advance cardiology practice and facilitate knowledge dissemination.","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"71 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139783777","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}

引用次数: 0

Using Natural Language Processing for Automated Classification of Disease and to Identify Misclassified ICD Codes in Cardiac Disease 利用自然语言处理技术自动进行疾病分类并识别心脏病中分类错误的 ICD 代码

European Heart Journal - Digital Health Pub Date : 2024-02-09 DOI: 10.1093/ehjdh/ztae008

M. Falter, D. Godderis, M. Scherrenberg, S. Kizilkilic, Linqi Xu, Marc Mertens, Jan Jansen, Pascal Legroux, H. Kindermans, Peter Sinnaeve, Frank Neven, P. Dendale

{"title":"Using Natural Language Processing for Automated Classification of Disease and to Identify Misclassified ICD Codes in Cardiac Disease","authors":"M. Falter, D. Godderis, M. Scherrenberg, S. Kizilkilic, Linqi Xu, Marc Mertens, Jan Jansen, Pascal Legroux, H. Kindermans, Peter Sinnaeve, Frank Neven, P. Dendale","doi":"10.1093/ehjdh/ztae008","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae008","url":null,"abstract":"\u0000 \u0000 \u0000 ICD-codes are used for classification of hospitalisations. The codes are used for administrative, financial and research purposes. It is known however that errors occur. Natural language processing (NLP) offers promising solutions for optimising the process.\u0000 \u0000 \u0000 \u0000 To investigate methods for automatic classification of disease in unstructured medical records using NLP and to compare these to conventional ICD coding.\u0000 \u0000 \u0000 \u0000 Two datasets were used: the open-source MIMIC-III dataset (n = 55.177) and a dataset from a hospital in Belgium (n = 12.706). Automated searches using NLP algorithms were performed for the diagnoses “atrial fibrillation” and “heart failure”. Four methods were used: rule-based search, logistic regression, term frequency-inverse document frequency (TF-IDF), XGBoost and BioBERT. All algorithms were developed on the MIMIC-III dataset. The best performing algorithm was then deployed on the Belgian dataset.\u0000 \u0000 \u0000 \u0000 After pre-processing a total of 1.438 reports was retained in the Belgian dataset. XGBoost on TF-IDF matrix resulted in an accuracy of 0.94 and 0.92 for AF and HF respectively. There were 211 mismatches between algorithm and ICD codes. 103 were due to a difference in data availability or differing definitions. In the remaining 108 mismatches, 70% were due to incorrect labelling by the algorithm and 30% were due to erroneous ICD-coding (2% of total hospitalisations).\u0000 \u0000 \u0000 \u0000 A newly developed NLP algorithm attained a high accuracy for classifying disease in medical records. XGBoost outperformed the deep learning technique BioBERT. NLP algorithms could be used to identify ICD-coding errors and optimise and support the ICD-coding process.\u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"411 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139847728","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}

引用次数: 0

Using Natural Language Processing for Automated Classification of Disease and to Identify Misclassified ICD Codes in Cardiac Disease 利用自然语言处理技术自动进行疾病分类并识别心脏病中分类错误的 ICD 代码

European Heart Journal - Digital Health Pub Date : 2024-02-09 DOI: 10.1093/ehjdh/ztae008

M. Falter, D. Godderis, M. Scherrenberg, S. Kizilkilic, Linqi Xu, Marc Mertens, Jan Jansen, Pascal Legroux, H. Kindermans, Peter Sinnaeve, Frank Neven, P. Dendale

{"title":"Using Natural Language Processing for Automated Classification of Disease and to Identify Misclassified ICD Codes in Cardiac Disease","authors":"M. Falter, D. Godderis, M. Scherrenberg, S. Kizilkilic, Linqi Xu, Marc Mertens, Jan Jansen, Pascal Legroux, H. Kindermans, Peter Sinnaeve, Frank Neven, P. Dendale","doi":"10.1093/ehjdh/ztae008","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae008","url":null,"abstract":"\u0000 \u0000 \u0000 ICD-codes are used for classification of hospitalisations. The codes are used for administrative, financial and research purposes. It is known however that errors occur. Natural language processing (NLP) offers promising solutions for optimising the process.\u0000 \u0000 \u0000 \u0000 To investigate methods for automatic classification of disease in unstructured medical records using NLP and to compare these to conventional ICD coding.\u0000 \u0000 \u0000 \u0000 Two datasets were used: the open-source MIMIC-III dataset (n = 55.177) and a dataset from a hospital in Belgium (n = 12.706). Automated searches using NLP algorithms were performed for the diagnoses “atrial fibrillation” and “heart failure”. Four methods were used: rule-based search, logistic regression, term frequency-inverse document frequency (TF-IDF), XGBoost and BioBERT. All algorithms were developed on the MIMIC-III dataset. The best performing algorithm was then deployed on the Belgian dataset.\u0000 \u0000 \u0000 \u0000 After pre-processing a total of 1.438 reports was retained in the Belgian dataset. XGBoost on TF-IDF matrix resulted in an accuracy of 0.94 and 0.92 for AF and HF respectively. There were 211 mismatches between algorithm and ICD codes. 103 were due to a difference in data availability or differing definitions. In the remaining 108 mismatches, 70% were due to incorrect labelling by the algorithm and 30% were due to erroneous ICD-coding (2% of total hospitalisations).\u0000 \u0000 \u0000 \u0000 A newly developed NLP algorithm attained a high accuracy for classifying disease in medical records. XGBoost outperformed the deep learning technique BioBERT. NLP algorithms could be used to identify ICD-coding errors and optimise and support the ICD-coding process.\u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":" 87","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788123","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}

引用次数: 0

ECG-based Prediction of Conduction Disturbances after Transcatheter Aortic Valve Replacement with Convolutional Neural Network 基于心电图的卷积神经网络预测经导管主动脉瓣置换术后的传导障碍

European Heart Journal - Digital Health Pub Date : 2024-02-08 DOI: 10.1093/ehjdh/ztae007

Yuheng Jia, Yiming Li, Gaden Luosang, Jianyong Wang, Gang Peng, Xingzhou Pu, Weili Jiang, Wenjian Li, Zhengang Zhao, Yong Peng, Yuan Feng, Jiafu Wei, Yuanning Xu, Xingbin Liu, Zhang Yi, Mao Chen

{"title":"ECG-based Prediction of Conduction Disturbances after Transcatheter Aortic Valve Replacement with Convolutional Neural Network","authors":"Yuheng Jia, Yiming Li, Gaden Luosang, Jianyong Wang, Gang Peng, Xingzhou Pu, Weili Jiang, Wenjian Li, Zhengang Zhao, Yong Peng, Yuan Feng, Jiafu Wei, Yuanning Xu, Xingbin Liu, Zhang Yi, Mao Chen","doi":"10.1093/ehjdh/ztae007","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae007","url":null,"abstract":"\u0000 \u0000 \u0000 Permanent pacemaker implantation and left bundle branch block are common complications after transcatheter aortic valve replacement (TAVR) and are associated with impaired prognosis.\u0000 \u0000 \u0000 \u0000 This study aimed to develop an artificial intelligence (AI) model for predicting conduction disturbances after TAVR using preprocedural 12-lead electrocardiogram (ECG) data.\u0000 \u0000 \u0000 \u0000 We collected preprocedural 12-lead ECGs of patients who underwent TAVR at West China Hospital between March 2016 and March 2022. A hold-out testing set comprising 20% of the sample was randomly selected. We developed an AI model using a convolutional neural network, trained it using fivefold cross validation, and tested it on the hold-out testing cohort. We also developed and validated an enhanced model that included additional clinical features.\u0000 \u0000 \u0000 \u0000 After applying exclusion criteria, we included 1354 ECGs of 718 patients in the study. The AI model predicted conduction disturbances in the hold-out testing cohort with an AUC of 0.764, accuracy of 0.743, F1 score of 0.752, sensitivity of 0.876, and specificity of 0.624, based solely on pre-procedural ECG data. The performance was better than the Emory score (AUC = 0.704), as well as the Logistic (AUC = 0.574) and XGboost (AUC = 0.520) models built with previously identified high-risk ECG patterns. After adding clinical features, there was an increase in the overall performance with an AUC of 0.779, accuracy of 0.774, F1 score of 0.776, sensitivity of 0.794, and specificity of 0.752.\u0000 \u0000 \u0000 \u0000 AI-enhanced ECGs may offer better predictive value than traditionally defined high-risk ECG patterns.\u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":" 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139791241","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}

引用次数: 0

Evaluation of a novel cuffless photoplethysmography-based wristband for measuring blood pressure according to the regulatory standards 根据法规标准评估新型无袖带光敏血压计腕带的血压测量效果

European Heart Journal - Digital Health Pub Date : 2024-02-08 DOI: 10.1093/ehjdh/ztae006

M. van Vliet, S. Monnink, M. Kuiper, J. Constandse, D. Hoftijzer, E. Ronner

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