Angela Koloi, Vasileios S. Loukas, Cillian Hourican, A. Sakellarios, Rick Quax, Pashupati P. Mishra, T. Lehtimäki, Olli T. Raitakari, C. Papaloukas, Jos A. Bosch, Winfried März, D. Fotiadis
Coronary artery disease (CAD) is a highly prevalent disease with modifiable risk factors. In patients with suspected obstructive CAD, evaluating the pre-test probability model is crucial for diagnosis, although its accuracy remains controversial. Machine learning (ML) predictive models can help clinicians detect CAD early and improve outcomes. This study aimed to identify early-stage CAD using ML in conjunction with a panel of clinical and laboratory tests. The study sample included 3316 patients enrolled in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. A comprehensive array of attributes was considered, and an ML pipeline was developed. Subsequently, we utilized five approaches to generating high-quality virtual patient data to improve the performance of the artificial intelligence models. An extension study was carried out using data from the Young Finns Study (YFS) to assess the results’ generalizability. Upon applying virtual augmented data, accuracy increased by approximately 5%, from 0.75 to –0.79 for random forests (RFs), and from 0.76 to –0.80 for Gradient Boosting (GB). Sensitivity showed a significant boost for RFs, rising by about 9.4% (0.81–0.89), while GB exhibited a 4.8% increase (0.83–0.87). Specificity showed a significant boost for RFs, rising by ∼24% (from 0.55 to 0.70), while GB exhibited a 37% increase (from 0.51 to 0.74). The extension analysis aligned with the initial study. Accurate predictions of angiographic CAD can be obtained using a set of routine laboratory markers, age, sex, and smoking status, holding the potential to limit the need for invasive diagnostic techniques. The extension analysis in the YFS demonstrated the potential of these findings in a younger population, and it confirmed applicability to atherosclerotic vascular disease. Using virtual population generation techniques, this study improved the accuracy of a machine learning model designed to identify early-stage CAD using standard laboratory tests.
{"title":"Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data","authors":"Angela Koloi, Vasileios S. Loukas, Cillian Hourican, A. Sakellarios, Rick Quax, Pashupati P. Mishra, T. Lehtimäki, Olli T. Raitakari, C. Papaloukas, Jos A. Bosch, Winfried März, D. Fotiadis","doi":"10.1093/ehjdh/ztae049","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae049","url":null,"abstract":"\u0000 \u0000 \u0000 Coronary artery disease (CAD) is a highly prevalent disease with modifiable risk factors. In patients with suspected obstructive CAD, evaluating the pre-test probability model is crucial for diagnosis, although its accuracy remains controversial. Machine learning (ML) predictive models can help clinicians detect CAD early and improve outcomes. This study aimed to identify early-stage CAD using ML in conjunction with a panel of clinical and laboratory tests.\u0000 \u0000 \u0000 \u0000 The study sample included 3316 patients enrolled in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. A comprehensive array of attributes was considered, and an ML pipeline was developed. Subsequently, we utilized five approaches to generating high-quality virtual patient data to improve the performance of the artificial intelligence models. An extension study was carried out using data from the Young Finns Study (YFS) to assess the results’ generalizability. Upon applying virtual augmented data, accuracy increased by approximately 5%, from 0.75 to –0.79 for random forests (RFs), and from 0.76 to –0.80 for Gradient Boosting (GB). Sensitivity showed a significant boost for RFs, rising by about 9.4% (0.81–0.89), while GB exhibited a 4.8% increase (0.83–0.87). Specificity showed a significant boost for RFs, rising by ∼24% (from 0.55 to 0.70), while GB exhibited a 37% increase (from 0.51 to 0.74). The extension analysis aligned with the initial study.\u0000 \u0000 \u0000 \u0000 Accurate predictions of angiographic CAD can be obtained using a set of routine laboratory markers, age, sex, and smoking status, holding the potential to limit the need for invasive diagnostic techniques. The extension analysis in the YFS demonstrated the potential of these findings in a younger population, and it confirmed applicability to atherosclerotic vascular disease.\u0000 \u0000 \u0000 \u0000 Using virtual population generation techniques, this study improved the accuracy of a machine learning model designed to identify early-stage CAD using standard laboratory tests.\u0000 \u0000 \u0000 \u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"2 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921487","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":"Why Thorough Open Data Descriptions Matters More Than Ever in the Age of AI: Opportunities for Cardiovascular Research","authors":"Sandy Engelhardt","doi":"10.1093/ehjdh/ztae061","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae061","url":null,"abstract":"","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"41 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928622","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":"Meet Key Digital Health thought leaders: Jagmeet (Jag) Singh","authors":"Nico Bruining","doi":"10.1093/ehjdh/ztae054","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae054","url":null,"abstract":"","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"34 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804400","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}
M. Tokodi, A. Kosztin, Attila Kovács, László Gellér, W. Schwertner, B. Veres, A. Behon, Christiane Lober, Nigussie Bogale, Cecilia Linde, C. Normand, Kenneth Dickstein, B. Merkely
We aimed to externally validate the SEMMELWEIS-CRT score for predicting 1-year all-cause mortality in the European Cardiac Resynchronization Therapy (CRT) Survey I dataset – a large multi-center cohort of patients undergoing CRT implantation. The SEMMELWEIS-CRT score is a machine learning-based tool trained for predicting all-cause mortality in patients undergoing CRT implantation. This tool demonstrated impressive performance during internal validation but has not yet been validated externally. To this end, we applied it to the data of 1,367 patients from the European CRT Survey I dataset. The SEMMELWEIS-CRT predicted 1-year mortality with an area under the receiver operating characteristic curve (AUC) of 0.729 [0.682–0.776], which concurred with the performance measured during internal validation (AUC: 0.768 [0.674–0.861], p=0.466). Moreover, the SEMMELWEIS-CRT score outperformed multiple conventional statistics-based risk scores, and we demonstrated that a higher predicted probability is not only associated with a higher risk of death (odds ratio [OR]: 1.081 [1.061–1.101], p<0.001) but also with an increased risk of hospitalizations for any cause (OR: 1.013 [1.002–1.025], p=0.020) or for heart failure (OR: 1.033 [1.015–1.052], p<0.001), a less than 5% improvement in left ventricular ejection fraction (OR: 1.033 [1.021–1.047], p<0.001), and lack of improvement in NYHA functional class compared to baseline (OR: 1.018 [1.006–1.029], p=0.003). In the European CRT Survey I dataset, the SEMMELWEIS-CRT score predicted 1-year all-cause mortality with good discriminatory power, which confirms the generalizability and demonstrates the potential clinical utility of this machine learning-based risk stratification tool.
{"title":"Machine learning-based prediction of 1-year all-cause mortality in patients undergoing CRT implantation: Validation of the SEMMELWEIS-CRT score in the European CRT Survey I dataset","authors":"M. Tokodi, A. Kosztin, Attila Kovács, László Gellér, W. Schwertner, B. Veres, A. Behon, Christiane Lober, Nigussie Bogale, Cecilia Linde, C. Normand, Kenneth Dickstein, B. Merkely","doi":"10.1093/ehjdh/ztae051","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae051","url":null,"abstract":"\u0000 \u0000 \u0000 We aimed to externally validate the SEMMELWEIS-CRT score for predicting 1-year all-cause mortality in the European Cardiac Resynchronization Therapy (CRT) Survey I dataset – a large multi-center cohort of patients undergoing CRT implantation.\u0000 \u0000 \u0000 \u0000 The SEMMELWEIS-CRT score is a machine learning-based tool trained for predicting all-cause mortality in patients undergoing CRT implantation. This tool demonstrated impressive performance during internal validation but has not yet been validated externally. To this end, we applied it to the data of 1,367 patients from the European CRT Survey I dataset. The SEMMELWEIS-CRT predicted 1-year mortality with an area under the receiver operating characteristic curve (AUC) of 0.729 [0.682–0.776], which concurred with the performance measured during internal validation (AUC: 0.768 [0.674–0.861], p=0.466). Moreover, the SEMMELWEIS-CRT score outperformed multiple conventional statistics-based risk scores, and we demonstrated that a higher predicted probability is not only associated with a higher risk of death (odds ratio [OR]: 1.081 [1.061–1.101], p<0.001) but also with an increased risk of hospitalizations for any cause (OR: 1.013 [1.002–1.025], p=0.020) or for heart failure (OR: 1.033 [1.015–1.052], p<0.001), a less than 5% improvement in left ventricular ejection fraction (OR: 1.033 [1.021–1.047], p<0.001), and lack of improvement in NYHA functional class compared to baseline (OR: 1.018 [1.006–1.029], p=0.003).\u0000 \u0000 \u0000 \u0000 In the European CRT Survey I dataset, the SEMMELWEIS-CRT score predicted 1-year all-cause mortality with good discriminatory power, which confirms the generalizability and demonstrates the potential clinical utility of this machine learning-based risk stratification tool.\u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"60 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654637","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}
V. A. A. van Es, I. D. De Lathauwer, R. G. P. Lopata, A. D. A. M. Kemperman, R. P. van Dongen, R. W. M. Brouwers, M. Funk, H. Kemps
Urbanization is related to non-communicable diseases like congestive heart failure (CHF). Understanding the influence of diverse living environments on physiological variables such as heart rate variability (HRV) in patients with chronic cardiac disease may contribute to more effective lifestyle advice and telerehabilitation strategies. This study explores how machine learning (ML) models can predict HRV metrics, which measure autonomic nervous system (ANS) responses to environmental attributes in uncontrolled real-world settings. The goal is to validate if this approach can ascertain and quantify the connection between environmental attributes and cardiac autonomic response in CHF patients. 20 participants (10 healthy, 10 CHF) wore smartwatches for 3 weeks, recording activities, locations, and HR. Environmental attributes were extracted from Google Street view images. ML models were trained and tested on the data to predict HRV metrics. The models were evaluated using Spearman’s correlation, RMSE, prediction intervals, and Bland-Altman analysis. ML models predicted HRV metrics related to vagal activity well (R > 0.8 for HR; 0.8 > R > 0.5 for RMSSD and SD1; 0.5 > R > 0.4 for HF and LF/HF) induced by environmental attributes. However, they struggled with metrics related to overall autonomic activity, due to the complex balance between sympathetic and parasympathetic modulation. This study highlights the potential of ML-based models to discern vagal dynamics influenced by living environments in healthy individuals and patients diagnosed with CHF. Ultimately, this strategy could offer rehabilitation and tailored lifestyle advice, leading to improved prognosis and enhanced overall patient well-being in CHF.
城市化与充血性心力衰竭（CHF）等非传染性疾病有关。了解不同生活环境对慢性心脏病患者心率变异性（HRV）等生理变量的影响，有助于制定更有效的生活方式建议和远程康复策略。本研究探讨了机器学习（ML）模型如何预测心率变异指标，心率变异指标测量的是自律神经系统（ANS）在不受控制的真实世界环境中对环境属性的反应。目的是验证这种方法能否确定和量化环境属性与慢性阻塞性肺病患者心脏自主神经反应之间的联系。 20 名参与者（10 名健康人，10 名慢性阻塞性肺病患者）佩戴智能手表 3 周，记录活动、地点和心率。环境属性从谷歌街景图像中提取。对数据进行了 ML 模型训练和测试，以预测心率变异指标。使用斯皮尔曼相关性、均方根误差、预测间隔和布兰-阿尔特曼分析对模型进行了评估。 ML 模型很好地预测了环境因素诱发的与迷走神经活动相关的心率变异指标（心率的 R > 0.8；RMSSD 和 SD1 的 R > 0.5；HF 和 LF/HF 的 R > 0.4）。然而，由于交感神经和副交感神经调节之间的复杂平衡，他们在处理与整体自律神经活动相关的指标时遇到了困难。 这项研究强调了基于 ML 的模型在辨别健康人和确诊为慢性阻塞性肺疾病的患者受生活环境影响的迷走神经动态方面的潜力。最终，这种策略可以提供康复和量身定制的生活方式建议，从而改善预后并提高慢性阻塞性肺疾病患者的整体健康水平。
{"title":"Effect of Urban Environment on Cardiovascular Health: A Feasibility Pilot Study using Machine Learning to Predict Heart Rate Variability in Heart Failure Patients","authors":"V. A. A. van Es, I. D. De Lathauwer, R. G. P. Lopata, A. D. A. M. Kemperman, R. P. van Dongen, R. W. M. Brouwers, M. Funk, H. Kemps","doi":"10.1093/ehjdh/ztae050","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae050","url":null,"abstract":"\u0000 \u0000 \u0000 Urbanization is related to non-communicable diseases like congestive heart failure (CHF). Understanding the influence of diverse living environments on physiological variables such as heart rate variability (HRV) in patients with chronic cardiac disease may contribute to more effective lifestyle advice and telerehabilitation strategies. This study explores how machine learning (ML) models can predict HRV metrics, which measure autonomic nervous system (ANS) responses to environmental attributes in uncontrolled real-world settings. The goal is to validate if this approach can ascertain and quantify the connection between environmental attributes and cardiac autonomic response in CHF patients.\u0000 \u0000 \u0000 \u0000 20 participants (10 healthy, 10 CHF) wore smartwatches for 3 weeks, recording activities, locations, and HR. Environmental attributes were extracted from Google Street view images. ML models were trained and tested on the data to predict HRV metrics. The models were evaluated using Spearman’s correlation, RMSE, prediction intervals, and Bland-Altman analysis.\u0000 \u0000 \u0000 \u0000 ML models predicted HRV metrics related to vagal activity well (R > 0.8 for HR; 0.8 > R > 0.5 for RMSSD and SD1; 0.5 > R > 0.4 for HF and LF/HF) induced by environmental attributes. However, they struggled with metrics related to overall autonomic activity, due to the complex balance between sympathetic and parasympathetic modulation.\u0000 \u0000 \u0000 \u0000 This study highlights the potential of ML-based models to discern vagal dynamics influenced by living environments in healthy individuals and patients diagnosed with CHF. Ultimately, this strategy could offer rehabilitation and tailored lifestyle advice, leading to improved prognosis and enhanced overall patient well-being in CHF.\u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"58 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654710","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}
L. Fiorina, P. Chemaly, J. Cellier, Mina Ait Said, Charlène Coquard, S. Younsi, F. Salerno, Jérôme Horvilleur, Jérôme Lacotte, Vladimir Manenti, A. Plesse, C. Henry, B. Lefebvre
Smartwatch ECGs have been identified as a noninvasive solution to assess abnormal heart rhythm, especially atrial arrhythmias which are related to stroke risk. However, the performance of these tools is limited and could be improved with the use of Deep Neural Network algorithms, particularly for specific populations encountered in clinical cardiology practice. 400 patients from the electrophysiology department of one tertiary care hospital have been included in two similar clinical trials (respectively 200 patients per study). Simultaneous ECG were recorded with the watch and a 12-lead recording system during consultation or before and after an electrophysiology procedure if any. The smartwatch ECGs were processed by the deep neural network and by the Apple watch ECG software (Apple app). Corresponding 12-lead ECGs were adjudicated by an expert electrophysiologist. The performance of the deep neural network was assessed versus the expert interpretation of the 12-lead ECG and inconclusive rates reported. Overall, the deep neural network and the Apple app presented respectively a sensitivity of 91% (95% CI: 85–95%) and 61% (95% CI: 44–75%) with a specificity of 95% (95% CI: 91–97%) and 97% (95% CI: 93–99%) when compared to physician 12-lead ECG interpretation. The deep neural network was able to provide a diagnosis on 99% of ECGs while the Apple app was only able to classify 78% of strips (22% of inconclusive diagnosis). In this study, including patients from a cardiology department, a deep neural network-based algorithm applied to a smartwatch ECG provided an accurate diagnosis regarding atrial arrhythmia detection on virtually all tracings, outperforming the Smartwatch algorithm.
{"title":"Artificial Intelligence-based ECG Analysis Improves Atrial Arrhythmia Detection from a smartwatch ECG","authors":"L. Fiorina, P. Chemaly, J. Cellier, Mina Ait Said, Charlène Coquard, S. Younsi, F. Salerno, Jérôme Horvilleur, Jérôme Lacotte, Vladimir Manenti, A. Plesse, C. Henry, B. Lefebvre","doi":"10.1093/ehjdh/ztae047","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae047","url":null,"abstract":"\u0000 \u0000 \u0000 Smartwatch ECGs have been identified as a noninvasive solution to assess abnormal heart rhythm, especially atrial arrhythmias which are related to stroke risk. However, the performance of these tools is limited and could be improved with the use of Deep Neural Network algorithms, particularly for specific populations encountered in clinical cardiology practice.\u0000 \u0000 \u0000 \u0000 400 patients from the electrophysiology department of one tertiary care hospital have been included in two similar clinical trials (respectively 200 patients per study). Simultaneous ECG were recorded with the watch and a 12-lead recording system during consultation or before and after an electrophysiology procedure if any. The smartwatch ECGs were processed by the deep neural network and by the Apple watch ECG software (Apple app). Corresponding 12-lead ECGs were adjudicated by an expert electrophysiologist. The performance of the deep neural network was assessed versus the expert interpretation of the 12-lead ECG and inconclusive rates reported.\u0000 \u0000 \u0000 \u0000 Overall, the deep neural network and the Apple app presented respectively a sensitivity of 91% (95% CI: 85–95%) and 61% (95% CI: 44–75%) with a specificity of 95% (95% CI: 91–97%) and 97% (95% CI: 93–99%) when compared to physician 12-lead ECG interpretation. The deep neural network was able to provide a diagnosis on 99% of ECGs while the Apple app was only able to classify 78% of strips (22% of inconclusive diagnosis).\u0000 \u0000 \u0000 \u0000 In this study, including patients from a cardiology department, a deep neural network-based algorithm applied to a smartwatch ECG provided an accurate diagnosis regarding atrial arrhythmia detection on virtually all tracings, outperforming the Smartwatch algorithm.\u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141672236","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":"Implantable cardiac monitors: the digital future of risk prediction?","authors":"A. Bauer, Clemens Dlaska","doi":"10.1093/ehjdh/ztae036","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae036","url":null,"abstract":"","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"75 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141357992","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}
E. Caiani, H. Kemps, P. Hoogendoorn, R. Asteggiano, A. Böhm, B. Borregaard, G. Boriani, H. Brunner la Rocca, R. Casado-Arroyo, S. Castelletti, R. Christodorescu, M. R. Cowie, P. Dendale, F. Dunn, A. G. Fraser, D A Lane, E. T. Locati, K. Malaczynska-Rajpold, C. Merșa, L. Neubeck, G. Parati, C. Plummer, G. Rosano, M. Scherrenberg, A. Smirthwaite, P. Szymański
Mobile health (mHealth) solutions have the potential to improve self-management and clinical care. For successful integration into routine clinical practice, healthcare professionals (HCPs) need accepted criteria helping the mHealth solutions’ selection, while patients require transparency to trust their use. Information about their evidence, safety and security may be hard to obtain and consensus is lacking on the level of required evidence. The new Medical Device Regulation is more stringent than its predecessor, yet its scope does not span all intended uses and several difficulties remain. The European Society of Cardiology Regulatory Affairs Committee set up a Task Force to explore existing assessment frameworks and clinical and cost-effectiveness evidence. This knowledge was used to propose criteria with which HCPs could evaluate mHealth solutions spanning diagnostic support, therapeutics, remote follow-up and education, specifically for cardiac rhythm management, heart failure and preventive cardiology. While curated national libraries of health apps may be helpful, their requirements and rigour in initial and follow-up assessments may vary significantly. The recently developed CEN-ISO/TS 82304-2 health app quality assessment framework has the potential to address this issue and to become a widely used and efficient tool to help drive decision-making internationally. The Task Force would like to stress the importance of co-development of solutions with relevant stakeholders, and maintenance of health information in apps to ensure these remain evidence-based and consistent with best practice. Several general and domain-specific criteria are advised to assist HCPs in their assessment of clinical evidence to provide informed advice to patients about mHealth utilisation.
{"title":"Standardised assessment of evidence supporting the adoption of mobile health solutions: A Clinical Consensus Statement of the ESC Regulatory Affairs Committee","authors":"E. Caiani, H. Kemps, P. Hoogendoorn, R. Asteggiano, A. Böhm, B. Borregaard, G. Boriani, H. Brunner la Rocca, R. Casado-Arroyo, S. Castelletti, R. Christodorescu, M. R. Cowie, P. Dendale, F. Dunn, A. G. Fraser, D A Lane, E. T. Locati, K. Malaczynska-Rajpold, C. Merșa, L. Neubeck, G. Parati, C. Plummer, G. Rosano, M. Scherrenberg, A. Smirthwaite, P. Szymański","doi":"10.1093/ehjdh/ztae042","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae042","url":null,"abstract":"\u0000 Mobile health (mHealth) solutions have the potential to improve self-management and clinical care. For successful integration into routine clinical practice, healthcare professionals (HCPs) need accepted criteria helping the mHealth solutions’ selection, while patients require transparency to trust their use. Information about their evidence, safety and security may be hard to obtain and consensus is lacking on the level of required evidence. The new Medical Device Regulation is more stringent than its predecessor, yet its scope does not span all intended uses and several difficulties remain. The European Society of Cardiology Regulatory Affairs Committee set up a Task Force to explore existing assessment frameworks and clinical and cost-effectiveness evidence. This knowledge was used to propose criteria with which HCPs could evaluate mHealth solutions spanning diagnostic support, therapeutics, remote follow-up and education, specifically for cardiac rhythm management, heart failure and preventive cardiology. While curated national libraries of health apps may be helpful, their requirements and rigour in initial and follow-up assessments may vary significantly. The recently developed CEN-ISO/TS 82304-2 health app quality assessment framework has the potential to address this issue and to become a widely used and efficient tool to help drive decision-making internationally. The Task Force would like to stress the importance of co-development of solutions with relevant stakeholders, and maintenance of health information in apps to ensure these remain evidence-based and consistent with best practice. Several general and domain-specific criteria are advised to assist HCPs in their assessment of clinical evidence to provide informed advice to patients about mHealth utilisation.","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"2 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141267321","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":"Artificial intelligence and transcatheter aortic valve implantation-induced conduction disturbances—adding insight beyond the human ‘I’","authors":"P. Houthuizen, Peter P T de Jaegere","doi":"10.1093/ehjdh/ztae040","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae040","url":null,"abstract":"","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141271921","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}
Jim O’Brien, Sergio Valsecchi, Fionnuala Seaver, L. Rosalejos, Diana Arellano, Kristine Laurilla, G. Jauvert, Noel Fitzpatrick, T. Tahin, Ted Keelan, Joe Galvin, Gabor Szeplaki
Catheter ablation is a widely accepted intervention for atrial fibrillation (AF) management. Prior to undertaking this procedure, thorough patient education on its efficacy and potential complications is crucial. Additionally, educating patients about stroke risk management and anticoagulant therapy is imperative. At Mater Private Hospital in Dublin, we implemented a solution, integrating a customized treatment pathway and a mobile application. This patient-centered approach aims to optimize the clinical management of AF catheter ablation candidates, focusing on knowledge gaps and adherence to guideline-based care to enhance overall outcomes. The application automates pre-operative assessments and post-operative support, facilitating seamless patient-clinician communication. During the observation period (September 2022 to April 2023), 63 patients installed the app. Patient adherence to the pathway was strong, with 98% of patients actively engaging in the treatment pathway and with 81% completing all pre-operative tasks. The average enrollment-to-admission duration was 14 days, and post-ablation tasks were fulfilled by 62% of patients within an average of 36 days. Operators perceived the solution as user-friendly and effective in enhancing patient connectivity. Patient satisfaction was high, and knowledge about AF improved notably through the solution, particularly concerning the recognition of symptoms and anticoagulation therapy-related complications. Our findings demonstrates the successful implementation of the app-based Ablation Solution, showcasing widespread patient use, improved adherence, and enhanced understanding of AF and its treatments. The system effectively connects healthcare providers with patients, offering a promising approach to streamline AF catheter ablation management and improve patient outcomes.
{"title":"Streamlining Atrial Fibrillation Ablation Management Using a Digitization Solution","authors":"Jim O’Brien, Sergio Valsecchi, Fionnuala Seaver, L. Rosalejos, Diana Arellano, Kristine Laurilla, G. Jauvert, Noel Fitzpatrick, T. Tahin, Ted Keelan, Joe Galvin, Gabor Szeplaki","doi":"10.1093/ehjdh/ztae041","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae041","url":null,"abstract":"\u0000 \u0000 \u0000 Catheter ablation is a widely accepted intervention for atrial fibrillation (AF) management. Prior to undertaking this procedure, thorough patient education on its efficacy and potential complications is crucial. Additionally, educating patients about stroke risk management and anticoagulant therapy is imperative.\u0000 \u0000 \u0000 \u0000 At Mater Private Hospital in Dublin, we implemented a solution, integrating a customized treatment pathway and a mobile application. This patient-centered approach aims to optimize the clinical management of AF catheter ablation candidates, focusing on knowledge gaps and adherence to guideline-based care to enhance overall outcomes.\u0000 \u0000 \u0000 \u0000 The application automates pre-operative assessments and post-operative support, facilitating seamless patient-clinician communication. During the observation period (September 2022 to April 2023), 63 patients installed the app.\u0000 \u0000 \u0000 \u0000 Patient adherence to the pathway was strong, with 98% of patients actively engaging in the treatment pathway and with 81% completing all pre-operative tasks. The average enrollment-to-admission duration was 14 days, and post-ablation tasks were fulfilled by 62% of patients within an average of 36 days. Operators perceived the solution as user-friendly and effective in enhancing patient connectivity. Patient satisfaction was high, and knowledge about AF improved notably through the solution, particularly concerning the recognition of symptoms and anticoagulation therapy-related complications.\u0000 \u0000 \u0000 \u0000 Our findings demonstrates the successful implementation of the app-based Ablation Solution, showcasing widespread patient use, improved adherence, and enhanced understanding of AF and its treatments. The system effectively connects healthcare providers with patients, offering a promising approach to streamline AF catheter ablation management and improve patient outcomes.\u0000","PeriodicalId":508387,"journal":{"name":"European Heart Journal - Digital Health","volume":"45 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103628","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}
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