Keni C S Lee, Boris Breznen, Anastasia Ukhova, Friedrich Koehler, Seth S Martin
Graphical AbstractAdherence to cardiac rehabilitation following a primary event has been demonstrated to improve quality of life, increase functional capacity, and decrease hospitalizations and mortality. Mobile technologies offer an opportunity to improve both the quality and utilization of cardiac rehabilitation, and recent clinical studies investigated this technology. This literature review summarizes the current use of mobile health, wearable activity monitors (WAMs), and other multi-component technologies deployed to support home-based virtual cardiac rehabilitation. The methodology was adapted from the Cochrane Handbook for Systematic Reviews of Interventions. We identified 2094 records, of which 113 were eligible for qualitative analysis. Different virtual cardiac rehabilitation solutions were implemented in the studies: (i) multi-component interventions in 48 studies (42.5%), (ii) WAMs in 27 studies (23.9%), (iii) web-based communications solutions, and (iv) mobile apps, both in 19 studies (16.4%). Functional capacity was the most frequently reported primary outcome (k = 37, 32.7%), followed by user adherence/compliance (k = 35, 31.0%), physical activity (k = 27, 23.9%), and quality of life (k = 14, 12.4%). Studies provided a mixed assessment of the efficacy of virtual cardiac rehabilitation in attaining either significant improvements over baseline or significant improvements in outcomes compared with conventional rehabilitation. Efficacy outcomes with virtual cardiac rehabilitation sometimes improve on the centre-based outcomes; however, superior clinical efficacy may not necessarily be the only outcome of interest. The promise of virtual cardiac rehabilitation includes the potential for increased user adherence and longer-term patient engagement. If these outcomes can be improved, that would be a significant justification for using this technology.
{"title":"Virtual healthcare solutions for cardiac rehabilitation: a literature review.","authors":"Keni C S Lee, Boris Breznen, Anastasia Ukhova, Friedrich Koehler, Seth S Martin","doi":"10.1093/ehjdh/ztad005","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad005","url":null,"abstract":"<p><p>Graphical AbstractAdherence to cardiac rehabilitation following a primary event has been demonstrated to improve quality of life, increase functional capacity, and decrease hospitalizations and mortality. Mobile technologies offer an opportunity to improve both the quality and utilization of cardiac rehabilitation, and recent clinical studies investigated this technology. This literature review summarizes the current use of mobile health, wearable activity monitors (WAMs), and other multi-component technologies deployed to support home-based virtual cardiac rehabilitation. The methodology was adapted from the <i>Cochrane Handbook for Systematic Reviews of Interventions</i>. We identified 2094 records, of which 113 were eligible for qualitative analysis. Different virtual cardiac rehabilitation solutions were implemented in the studies: (i) multi-component interventions in 48 studies (42.5%), (ii) WAMs in 27 studies (23.9%), (iii) web-based communications solutions, and (iv) mobile apps, both in 19 studies (16.4%). Functional capacity was the most frequently reported primary outcome (<i>k</i> = 37, 32.7%), followed by user adherence/compliance (<i>k</i> = 35, 31.0%), physical activity (<i>k</i> = 27, 23.9%), and quality of life (<i>k</i> = 14, 12.4%). Studies provided a mixed assessment of the efficacy of virtual cardiac rehabilitation in attaining either significant improvements over baseline or significant improvements in outcomes compared with conventional rehabilitation. Efficacy outcomes with virtual cardiac rehabilitation sometimes improve on the centre-based outcomes; however, superior clinical efficacy may not necessarily be the only outcome of interest. The promise of virtual cardiac rehabilitation includes the potential for increased user adherence and longer-term patient engagement. If these outcomes can be improved, that would be a significant justification for using this technology.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/39/df/ztad005.PMC10039430.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9567933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-07eCollection Date: 2023-03-01DOI: 10.1093/ehjdh/ztad010
Gunhild Brørs, Håvard Dalen, Heather Allore, Christi Deaton, Bengt Fridlund, Cameron D Norman, Pernille Palm, Tore Wentzel-Larsen, Tone M Norekvål
Aims: Fundamental roadblocks, such as non-use and low electronic health (eHealth) literacy, prevent the implementation of eHealth resources. The aims were to study internet usage for health information and eHealth literacy in patients after percutaneous coronary intervention (PCI). Further, we aimed to evaluate temporal changes and determine whether the use of the internet to find health information and eHealth literacy were associated with coronary artery disease (CAD) risk factors at the index admission and 12-month follow-up of the same population.
Methods and results: This prospective longitudinal study recruited 2924 adult patients with internet access treated by PCI in two Nordic countries. Assessments were made at baseline and 12-month follow-up, including a de novo question Have you used the internet to find information about health?, the eHealth literacy scale, and assessment of clinical, behavioural, and psychological CAD risk factors. Regression analyses were used. Patients' use of the internet for health information and their eHealth literacy were moderate at baseline but significantly lower at 12-month follow-up. Non-users of the internet for health information were more often smokers and had a lower burden of anxiety symptoms. Lower eHealth literacy was associated with a higher burden of depression symptoms at baseline and lower physical activity and being a smoker at baseline and at 12-month follow-up.
Conclusion: Non-use of the internet and lower eHealth literacy need to be considered when implementing eHealth resources, as they are associated with behavioural and psychological CAD risk factors. eHealth should therefore be designed and implemented with high-risk CAD patients in mind.
{"title":"The association of electronic health literacy with behavioural and psychological coronary artery disease risk factors in patients after percutaneous coronary intervention: a 12-month follow-up study.","authors":"Gunhild Brørs, Håvard Dalen, Heather Allore, Christi Deaton, Bengt Fridlund, Cameron D Norman, Pernille Palm, Tore Wentzel-Larsen, Tone M Norekvål","doi":"10.1093/ehjdh/ztad010","DOIUrl":"10.1093/ehjdh/ztad010","url":null,"abstract":"<p><strong>Aims: </strong>Fundamental roadblocks, such as non-use and low electronic health (eHealth) literacy, prevent the implementation of eHealth resources. The aims were to study internet usage for health information and eHealth literacy in patients after percutaneous coronary intervention (PCI). Further, we aimed to evaluate temporal changes and determine whether the use of the internet to find health information and eHealth literacy were associated with coronary artery disease (CAD) risk factors at the index admission and 12-month follow-up of the same population.</p><p><strong>Methods and results: </strong>This prospective longitudinal study recruited 2924 adult patients with internet access treated by PCI in two Nordic countries. Assessments were made at baseline and 12-month follow-up, including a <i>de novo</i> question <i>Have you used the internet to find information about health?</i>, the eHealth literacy scale, and assessment of clinical, behavioural, and psychological CAD risk factors. Regression analyses were used. Patients' use of the internet for health information and their eHealth literacy were moderate at baseline but significantly lower at 12-month follow-up. Non-users of the internet for health information were more often smokers and had a lower burden of anxiety symptoms. Lower eHealth literacy was associated with a higher burden of depression symptoms at baseline and lower physical activity and being a smoker at baseline and at 12-month follow-up.</p><p><strong>Conclusion: </strong>Non-use of the internet and lower eHealth literacy need to be considered when implementing eHealth resources, as they are associated with behavioural and psychological CAD risk factors. eHealth should therefore be designed and implemented with high-risk CAD patients in mind.</p><p><strong>Clinical trial registration: </strong>ClinicalTrials.gov NCT03810612 https://clinicaltrials.gov/ct2/show/NCT03810612.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a8/75/ztad010.PMC10039428.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9552635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-03eCollection Date: 2023-03-01DOI: 10.1093/ehjdh/ztad007
Michele Orini, Stefan van Duijvenboden, William J Young, Julia Ramírez, Aled R Jones, Andrew Tinker, Patricia B Munroe, Pier D Lambiase
Aims: Wearable devices are transforming the electrocardiogram (ECG) into a ubiquitous medical test. This study assesses the association between premature ventricular and atrial contractions (PVCs and PACs) detected on wearable-format ECGs (15 s single lead) and cardiovascular outcomes in individuals without cardiovascular disease (CVD).
Methods and results: Premature atrial contractions and PVCs were identified in 15 s single-lead ECGs from N = 54 016 UK Biobank participants (median age, interquartile range, age 58, 50-63 years, 54% female). Cox regression models adjusted for traditional risk factors were used to determine associations with atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), stroke, life-threatening ventricular arrhythmias (LTVAs), and mortality over a period of 11.5 (11.4-11.7) years. The strongest associations were found between PVCs (prevalence 2.2%) and HF (hazard ratio, HR, 95% confidence interval = 2.09, 1.58-2.78) and between PACs (prevalence 1.9%) and AF (HR = 2.52, 2.11-3.01), with shorter prematurity further increasing risk. Premature ventricular contractions and PACs were also associated with LTVA (P < 0.05). Associations with MI, stroke, and mortality were significant only in unadjusted models. In a separate UK Biobank sub-study sample [UKB-2, N = 29,324, age 64, 58-60 years, 54% female, follow-up 3.5 (2.6-4.8) years] used for independent validation, after adjusting for risk factors, PACs were associated with AF (HR = 1.80, 1.12-2.89) and PVCs with HF (HR = 2.32, 1.28-4.22).
Conclusion: In middle-aged individuals without CVD, premature contractions identified in 15 s single-lead ECGs are strongly associated with an increased risk of AF and HF. These data warrant further investigation to assess the role of wearable ECGs for early cardiovascular risk stratification.
{"title":"Premature atrial and ventricular contractions detected on wearable-format electrocardiograms and prediction of cardiovascular events.","authors":"Michele Orini, Stefan van Duijvenboden, William J Young, Julia Ramírez, Aled R Jones, Andrew Tinker, Patricia B Munroe, Pier D Lambiase","doi":"10.1093/ehjdh/ztad007","DOIUrl":"10.1093/ehjdh/ztad007","url":null,"abstract":"<p><strong>Aims: </strong>Wearable devices are transforming the electrocardiogram (ECG) into a ubiquitous medical test. This study assesses the association between premature ventricular and atrial contractions (PVCs and PACs) detected on wearable-format ECGs (15 s single lead) and cardiovascular outcomes in individuals without cardiovascular disease (CVD).</p><p><strong>Methods and results: </strong>Premature atrial contractions and PVCs were identified in 15 s single-lead ECGs from <i>N</i> = 54 016 UK Biobank participants (median age, interquartile range, age 58, 50-63 years, 54% female). Cox regression models adjusted for traditional risk factors were used to determine associations with atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), stroke, life-threatening ventricular arrhythmias (LTVAs), and mortality over a period of 11.5 (11.4-11.7) years. The strongest associations were found between PVCs (prevalence 2.2%) and HF (hazard ratio, HR, 95% confidence interval = 2.09, 1.58-2.78) and between PACs (prevalence 1.9%) and AF (HR = 2.52, 2.11-3.01), with shorter prematurity further increasing risk. Premature ventricular contractions and PACs were also associated with LTVA (<i>P</i> < 0.05). Associations with MI, stroke, and mortality were significant only in unadjusted models. In a separate UK Biobank sub-study sample [UKB-2, <i>N</i> = 29,324, age 64, 58-60 years, 54% female, follow-up 3.5 (2.6-4.8) years] used for independent validation, after adjusting for risk factors, PACs were associated with AF (HR = 1.80, 1.12-2.89) and PVCs with HF (HR = 2.32, 1.28-4.22).</p><p><strong>Conclusion: </strong>In middle-aged individuals without CVD, premature contractions identified in 15 s single-lead ECGs are strongly associated with an increased risk of AF and HF. These data warrant further investigation to assess the role of wearable ECGs for early cardiovascular risk stratification.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10039429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9567928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-13eCollection Date: 2023-03-01DOI: 10.1093/ehjdh/ztad001
Demilade Adedinsewo, Heather D Hardway, Andrea Carolina Morales-Lara, Mikolaj A Wieczorek, Patrick W Johnson, Erika J Douglass, Bryan J Dangott, Raouf E Nakhleh, Tathagat Narula, Parag C Patel, Rohan M Goswami, Melissa A Lyle, Alexander J Heckman, Juan C Leoni-Moreno, D Eric Steidley, Reza Arsanjani, Brian Hardaway, Mohsin Abbas, Atta Behfar, Zachi I Attia, Francisco Lopez-Jimenez, Peter A Noseworthy, Paul Friedman, Rickey E Carter, Mohamad Yamani
Aims: Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG).
Methods and results: Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78-0.90] and 95% (19/20; 95% CI: 75-100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61-0.96) and 100% (2/2; 95% CI: 16-100%) sensitivity.
Conclusion: An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.
{"title":"Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model.","authors":"Demilade Adedinsewo, Heather D Hardway, Andrea Carolina Morales-Lara, Mikolaj A Wieczorek, Patrick W Johnson, Erika J Douglass, Bryan J Dangott, Raouf E Nakhleh, Tathagat Narula, Parag C Patel, Rohan M Goswami, Melissa A Lyle, Alexander J Heckman, Juan C Leoni-Moreno, D Eric Steidley, Reza Arsanjani, Brian Hardaway, Mohsin Abbas, Atta Behfar, Zachi I Attia, Francisco Lopez-Jimenez, Peter A Noseworthy, Paul Friedman, Rickey E Carter, Mohamad Yamani","doi":"10.1093/ehjdh/ztad001","DOIUrl":"10.1093/ehjdh/ztad001","url":null,"abstract":"<p><strong>Aims: </strong>Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG).</p><p><strong>Methods and results: </strong>Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (<i>n</i> = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78-0.90] and 95% (19/20; 95% CI: 75-100%) sensitivity. A prospective proof-of-concept screening study (<i>n</i> = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61-0.96) and 100% (2/2; 95% CI: 16-100%) sensitivity.</p><p><strong>Conclusion: </strong>An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/35/4b/ztad001.PMC10039431.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9567931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aims: Remote monitoring (RM) is the standard of care for follow up of patients with cardiac implantable electronic devices. The aim of this study was to compare smartphone-based RM (SM-RM) using patient applications (myMerlinPulse™ app) with traditional bedside monitor RM (BM-RM).
Methods and results: The retrospective study included de-identified US patients who received either SM-RM or BM-RM capable of implantable cardioverter defibrillators or cardiac resynchronization therapy defibrillators (Abbott, USA). Patients in SM-RM and BM-RM groups were propensity-score matched on age and gender, device type, implant year, and month. Compliance with RM was quantified as the proportion of patients enrolling in the RM system (Merlin.net™) and transmitting data at least once. Connectivity was measured by the median number of days between consecutive transmissions per patient. Of the initial 9714 patients with SM-RM and 26 679 patients with BM-RM, 9397 patients from each group were matched. Remote monitoring compliance was higher in SM-RM; significantly more patients with SM-RM were enrolled in RM compared with BM-RM (94.4 vs. 85.0%, P < 0.001), similar number of patients in the SM-RM group paired their device (95.1 vs. 95.0%, P = 0.77), but more SM-RM patients transmitted at least once (98.1 vs. 94.3%, P < 0.001). Connectivity was significantly higher in the SM-RM, with patients transmitting data every 1.2 (1.1, 1.7) vs. every 1.7 (1.5, 2.0) days with BM-RM (P < 0.001) and remained better over time. Significantly more SM-RM patients utilized patient-initiated transmissions compared with BM-RM (55.6 vs. 28.1%, P < 0.001).
Conclusion: In this large real-world study, patients with SM-RM demonstrated improved compliance and connectivity compared with BM-RM.
目的:远程监测(RM)是心脏植入式电子装置患者随访的标准护理。本研究的目的是比较使用患者应用程序(myMerlinPulse™应用程序)的基于智能手机的RM (SM-RM)与传统床边监护RM (BM-RM)。方法和结果:回顾性研究纳入了接受SM-RM或BM-RM的美国患者,这些患者能够植入心律转复除颤器或心脏再同步化治疗除颤器(Abbott, USA)。SM-RM组和BM-RM组患者在年龄和性别、器械类型、种植年份和月份上的倾向评分相匹配。RM的依从性被量化为入组RM系统(Merlin.net™)并至少传送一次数据的患者比例。连通性通过每位患者连续传输之间的中位数天数来衡量。在最初的9714例SM-RM患者和26679例BM-RM患者中,每组匹配9397例患者。SM-RM的远程监控依从性较高;与BM-RM相比,更多的SM-RM患者参加了RM (94.4 vs. 85.0%, P < 0.001), SM-RM组中配对设备的患者数量相似(95.1 vs. 95.0%, P = 0.77),但更多的SM-RM患者至少传播一次(98.1 vs. 94.3%, P < 0.001)。SM-RM的连通性显著更高,患者每1.2(1.1,1.7)天传输数据,而BM-RM每1.7(1.5,2.0)天传输数据(P < 0.001),并且随着时间的推移保持更好。与BM-RM相比,SM-RM患者使用患者源性传播的比例明显更高(55.6% vs. 28.1%, P < 0.001)。结论:在这项大型现实世界研究中,与BM-RM相比,SM-RM患者表现出更好的依从性和连通性。
{"title":"Smartphone-based cardiac implantable electronic device remote monitoring: improved compliance and connectivity.","authors":"Harish Manyam, Haran Burri, Ruben Casado-Arroyo, Niraj Varma, Carsten Lennerz, Didier Klug, Gerald Carr-White, Kranthi Kolli, Ignacio Reyes, Yelena Nabutovsky, Giuseppe Boriani","doi":"10.1093/ehjdh/ztac071","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac071","url":null,"abstract":"<p><strong>Aims: </strong>Remote monitoring (RM) is the standard of care for follow up of patients with cardiac implantable electronic devices. The aim of this study was to compare smartphone-based RM (SM-RM) using patient applications (myMerlinPulse™ app) with traditional bedside monitor RM (BM-RM).</p><p><strong>Methods and results: </strong>The retrospective study included de-identified US patients who received either SM-RM or BM-RM capable of implantable cardioverter defibrillators or cardiac resynchronization therapy defibrillators (Abbott, USA). Patients in SM-RM and BM-RM groups were propensity-score matched on age and gender, device type, implant year, and month. Compliance with RM was quantified as the proportion of patients enrolling in the RM system (Merlin.net™) and transmitting data at least once. Connectivity was measured by the median number of days between consecutive transmissions per patient. Of the initial 9714 patients with SM-RM and 26 679 patients with BM-RM, 9397 patients from each group were matched. Remote monitoring compliance was higher in SM-RM; significantly more patients with SM-RM were enrolled in RM compared with BM-RM (94.4 vs. 85.0%, <i>P</i> < 0.001), similar number of patients in the SM-RM group paired their device (95.1 vs. 95.0%, <i>P</i> = 0.77), but more SM-RM patients transmitted at least once (98.1 vs. 94.3%, <i>P</i> < 0.001). Connectivity was significantly higher in the SM-RM, with patients transmitting data every 1.2 (1.1, 1.7) vs. every 1.7 (1.5, 2.0) days with BM-RM (<i>P</i> < 0.001) and remained better over time. Significantly more SM-RM patients utilized patient-initiated transmissions compared with BM-RM (55.6 vs. 28.1%, <i>P</i> < 0.001).</p><p><strong>Conclusion: </strong>In this large real-world study, patients with SM-RM demonstrated improved compliance and connectivity compared with BM-RM.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8c/8f/ztac071.PMC9890086.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10663269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Loes J Peters, Alezandra Torres-Castaño, Faridi S van Etten-Jamaludin, Lilisbeth Perestelo Perez, Dirk T Ubbink
Aims: Although digital decision aids (DAs) have been developed to improve shared decision-making (SDM), also in the cardiovascular realm, its implementation seems challenging. This study aims to systematically review the predictors of successful implementation of digital DAs for cardiovascular diseases.
Methods and results: Searches were conducted in MEDLINE, Embase, PsycInfo, CINAHL, and the Cochrane Library from inception to November 2021. Two reviewers independently assessed study eligibility and risk of bias. Data were extracted by using a predefined list of variables. Five good-quality studies were included, involving data of 215 patients and 235 clinicians. Studies focused on DAs for coronary artery disease, atrial fibrillation, and end-stage heart failure patients. Clinicians reported DA content, its effectivity, and a lack of knowledge on SDM and DA use as implementation barriers. Patients reported preference for another format, the way clinicians used the DA and anxiety for the upcoming intervention as barriers. In addition, barriers were related to the timing and Information and Communication Technology (ICT) integration of the DA, the limited duration of a consultation, a lack of communication among the team members, and maintaining the hospital's number of treatments. Clinicians' positive attitude towards preference elicitation and implementation of DAs in existing structures were reported as facilitators.
Conclusion: To improve digital DA use in cardiovascular diseases, the optimum timing of the DA, training healthcare professionals in SDM and DA usage, and integrating DAs into existing ICT structures need special effort. Current evidence, albeit limited, already offers advice on how to improve DA implementation in cardiovascular medicine.
{"title":"What helps the successful implementation of digital decision aids supporting shared decision-making in cardiovascular diseases? A systematic review.","authors":"Loes J Peters, Alezandra Torres-Castaño, Faridi S van Etten-Jamaludin, Lilisbeth Perestelo Perez, Dirk T Ubbink","doi":"10.1093/ehjdh/ztac070","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac070","url":null,"abstract":"<p><strong>Aims: </strong>Although digital decision aids (DAs) have been developed to improve shared decision-making (SDM), also in the cardiovascular realm, its implementation seems challenging. This study aims to systematically review the predictors of successful implementation of digital DAs for cardiovascular diseases.</p><p><strong>Methods and results: </strong>Searches were conducted in MEDLINE, Embase, PsycInfo, CINAHL, and the Cochrane Library from inception to November 2021. Two reviewers independently assessed study eligibility and risk of bias. Data were extracted by using a predefined list of variables. Five good-quality studies were included, involving data of 215 patients and 235 clinicians. Studies focused on DAs for coronary artery disease, atrial fibrillation, and end-stage heart failure patients. Clinicians reported DA content, its effectivity, and a lack of knowledge on SDM and DA use as implementation barriers. Patients reported preference for another format, the way clinicians used the DA and anxiety for the upcoming intervention as barriers. In addition, barriers were related to the timing and Information and Communication Technology (ICT) integration of the DA, the limited duration of a consultation, a lack of communication among the team members, and maintaining the hospital's number of treatments. Clinicians' positive attitude towards preference elicitation and implementation of DAs in existing structures were reported as facilitators.</p><p><strong>Conclusion: </strong>To improve digital DA use in cardiovascular diseases, the optimum timing of the DA, training healthcare professionals in SDM and DA usage, and integrating DAs into existing ICT structures need special effort. Current evidence, albeit limited, already offers advice on how to improve DA implementation in cardiovascular medicine.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/23/a8/ztac070.PMC9890083.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10663669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hongxing Luo, Jerremy Weerts, Anja Bekkers, Anouk Achten, Sien Lievens, Kimberly Smeets, Vanessa van Empel, Tammo Delhaas, Frits W Prinzen
Aims: Heart failure with preserved ejection fraction (HFpEF) is associated with stiffened myocardium and elevated filling pressure that may be captured by heart sound (HS). We investigated the relationship between phonocardiography (PCG) and echocardiography in symptomatic patients suspected of HFpEF.
Methods and results: Consecutive symptomatic patients with sinus rhythm and left ventricular ejection fraction >45% were enrolled. Echocardiography was performed to evaluate the patients' diastolic function, accompanied by PCG measurements. Phonocardiography features including HS amplitude, frequency, and timing intervals were calculated, and their abilities to differentiate the ratio between early mitral inflow velocity and early diastolic mitral annular velocity (E/e') were investigated. Of 45 patients, variable ratio matching was applied to obtain two groups of patients with similar characteristics but different E/e'. Patients with a higher E/e' showed higher first and second HS frequencies and more fourth HS and longer systolic time intervals. The interval from QRS onset to first HS was the best feature for the prediction of E/e' > 9 [area under the curve (AUC): 0.72 (0.51-0.88)] in the matched patients. In comparison, N-terminal pro-brain natriuretic peptide (NT-proBNP) showed an AUC of 0.67 (0.46-0.85), a value not better than any PCG feature (P > 0.05).
Conclusion: Phonocardiography features stratify E/e' in symptomatic patients suspected of HFpEF with a diagnostic performance similar to NT-proBNP. Heart sound may serve as a simple non-invasive tool for evaluating HFpEF patients.
{"title":"Association between phonocardiography and echocardiography in heart failure patients with preserved ejection fraction.","authors":"Hongxing Luo, Jerremy Weerts, Anja Bekkers, Anouk Achten, Sien Lievens, Kimberly Smeets, Vanessa van Empel, Tammo Delhaas, Frits W Prinzen","doi":"10.1093/ehjdh/ztac073","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac073","url":null,"abstract":"<p><strong>Aims: </strong>Heart failure with preserved ejection fraction (HFpEF) is associated with stiffened myocardium and elevated filling pressure that may be captured by heart sound (HS). We investigated the relationship between phonocardiography (PCG) and echocardiography in symptomatic patients suspected of HFpEF.</p><p><strong>Methods and results: </strong>Consecutive symptomatic patients with sinus rhythm and left ventricular ejection fraction >45% were enrolled. Echocardiography was performed to evaluate the patients' diastolic function, accompanied by PCG measurements. Phonocardiography features including HS amplitude, frequency, and timing intervals were calculated, and their abilities to differentiate the ratio between early mitral inflow velocity and early diastolic mitral annular velocity (<i>E</i>/<i>e</i>') were investigated. Of 45 patients, variable ratio matching was applied to obtain two groups of patients with similar characteristics but different <i>E</i>/<i>e</i>'. Patients with a higher <i>E</i>/<i>e</i>' showed higher first and second HS frequencies and more fourth HS and longer systolic time intervals. The interval from QRS onset to first HS was the best feature for the prediction of <i>E</i>/<i>e</i>' > 9 [area under the curve (AUC): 0.72 (0.51-0.88)] in the matched patients. In comparison, N-terminal pro-brain natriuretic peptide (NT-proBNP) showed an AUC of 0.67 (0.46-0.85), a value not better than any PCG feature (<i>P</i> > 0.05).</p><p><strong>Conclusion: </strong>Phonocardiography features stratify <i>E</i>/<i>e</i>' in symptomatic patients suspected of HFpEF with a diagnostic performance similar to NT-proBNP. Heart sound may serve as a simple non-invasive tool for evaluating HFpEF patients.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/96/54/ztac073.PMC9890082.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10663271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aims: Deep learning models (DLMs) have shown superiority in electrocardiogram (ECG) analysis and have been applied to diagnose dyskalaemias. However, no study has explored the performance of DLM-enabled ECG in continuous follow-up scenarios. Therefore, we proposed a dynamic revision of DLM-enabled ECG to use personal pre-annotated ECGs to enhance the accuracy in patients with multiple visits.
Methods and results: We retrospectively collected 168 450 ECGs with corresponding serum potassium (K+) levels from 103 091 patients as development samples. In the internal/external validation sets, the numbers of ECGs with corresponding K+ were 37 246/47 604 from 13 555/20 058 patients. Our dynamic revision method showed better performance than the traditional direct prediction for diagnosing hypokalaemia [area under the receiver operating characteristic curve (AUC) = 0.730/0.720-0.788/0.778] and hyperkalaemia (AUC = 0.884/0.888-0.915/0.908) in patients with multiple visits.
Conclusion: Our method has shown a distinguishable improvement in DLMs for diagnosing dyskalaemias in patients with multiple visits, and we also proved its application in ejection fraction prediction, which could further improve daily clinical practice.
{"title":"Development and validation of a dynamic deep learning algorithm using electrocardiogram to predict dyskalaemias in patients with multiple visits.","authors":"Yu-Sheng Lou, Chin-Sheng Lin, Wen-Hui Fang, Chia-Cheng Lee, Chih-Hung Wang, Chin Lin","doi":"10.1093/ehjdh/ztac072","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac072","url":null,"abstract":"<p><strong>Aims: </strong>Deep learning models (DLMs) have shown superiority in electrocardiogram (ECG) analysis and have been applied to diagnose dyskalaemias. However, no study has explored the performance of DLM-enabled ECG in continuous follow-up scenarios. Therefore, we proposed a dynamic revision of DLM-enabled ECG to use personal pre-annotated ECGs to enhance the accuracy in patients with multiple visits.</p><p><strong>Methods and results: </strong>We retrospectively collected 168 450 ECGs with corresponding serum potassium (K<sup>+</sup>) levels from 103 091 patients as development samples. In the internal/external validation sets, the numbers of ECGs with corresponding K<sup>+</sup> were 37 246/47 604 from 13 555/20 058 patients. Our dynamic revision method showed better performance than the traditional direct prediction for diagnosing hypokalaemia [area under the receiver operating characteristic curve (AUC) = 0.730/0.720-0.788/0.778] and hyperkalaemia (AUC = 0.884/0.888-0.915/0.908) in patients with multiple visits.</p><p><strong>Conclusion: </strong>Our method has shown a distinguishable improvement in DLMs for diagnosing dyskalaemias in patients with multiple visits, and we also proved its application in ejection fraction prediction, which could further improve daily clinical practice.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/20/f7/ztac072.PMC9890087.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10663671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cian M Scannell, Ebraham Alskaf, Noor Sharrack, Reza Razavi, Sebastien Ourselin, Alistair A Young, Sven Plein, Amedeo Chiribiri
Aims: One of the major challenges in the quantification of myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) is the estimation of the arterial input function (AIF). This is due to the non-linear relationship between the concentration of gadolinium and the MR signal, which leads to signal saturation. In this work, we show that a deep learning model can be trained to predict the unsaturated AIF from standard images, using the reference dual-sequence acquisition AIFs (DS-AIFs) for training.
Methods and results: A 1D U-Net was trained, to take the saturated AIF from the standard images as input and predict the unsaturated AIF, using the data from 201 patients from centre 1 and a test set comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2 (n = 44). Fully-automated MBF was compared between the DS-AIF and AI-AIF methods using the Mann-Whitney U test and Bland-Altman analysis. There was no statistical difference between the MBF quantified with the DS-AIF [2.77 mL/min/g (1.08)] and predicted with the AI-AIF (2.79 mL/min/g (1.08), P = 0.33. Bland-Altman analysis shows minimal bias between the DS-AIF and AI-AIF methods for quantitative MBF (bias of -0.11 mL/min/g). Additionally, the MBF diagnosis classification of the AI-AIF matched the DS-AIF in 669/704 (95%) of myocardial segments.
Conclusion: Quantification of stress perfusion CMR is feasible with a single-sequence acquisition and a single contrast injection using an AI-based correction of the AIF.
{"title":"AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance.","authors":"Cian M Scannell, Ebraham Alskaf, Noor Sharrack, Reza Razavi, Sebastien Ourselin, Alistair A Young, Sven Plein, Amedeo Chiribiri","doi":"10.1093/ehjdh/ztac074","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac074","url":null,"abstract":"<p><strong>Aims: </strong>One of the major challenges in the quantification of myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) is the estimation of the arterial input function (AIF). This is due to the non-linear relationship between the concentration of gadolinium and the MR signal, which leads to signal saturation. In this work, we show that a deep learning model can be trained to predict the unsaturated AIF from standard images, using the reference dual-sequence acquisition AIFs (DS-AIFs) for training.</p><p><strong>Methods and results: </strong>A 1D U-Net was trained, to take the saturated AIF from the standard images as input and predict the unsaturated AIF, using the data from 201 patients from centre 1 and a test set comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2 (<i>n</i> = 44). Fully-automated MBF was compared between the DS-AIF and AI-AIF methods using the Mann-Whitney U test and Bland-Altman analysis. There was no statistical difference between the MBF quantified with the DS-AIF [2.77 mL/min/g (1.08)] and predicted with the AI-AIF (2.79 mL/min/g (1.08), <i>P</i> = 0.33. Bland-Altman analysis shows minimal bias between the DS-AIF and AI-AIF methods for quantitative MBF (bias of -0.11 mL/min/g). Additionally, the MBF diagnosis classification of the AI-AIF matched the DS-AIF in 669/704 (95%) of myocardial segments.</p><p><strong>Conclusion: </strong>Quantification of stress perfusion CMR is feasible with a single-sequence acquisition and a single contrast injection using an AI-based correction of the AIF.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9890084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9759049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}