Automated echocardiography view classification and quality assessment with recognition of unknown views.

Purpose: Interpreting echocardiographic exams requires substantial manual interaction as videos lack scan-plane information and have inconsistent image quality, ranging from clinically relevant to unrecognizable. Thus, a manual prerequisite step for analysis is to select the appropriate views that showcase both the target anatomy and optimal image quality. To automate this selection process, we present a method for automatic classification of routine views, recognition of unknown views, and quality assessment of detected views. Approach: We train a neural network for view classification and employ the logit activations from the neural network for unknown view recognition. Subsequently, we train a linear regression algorithm that uses feature embeddings from the neural network to predict view quality scores. We evaluate the method on a clinical test set of 2466 echocardiography videos with expert-annotated view labels and a subset of 438 videos with expert-rated view quality scores. A second observer annotated a subset of 894 videos, including all quality-rated videos. Results: The proposed method achieved an accuracy of 84.9 % ± 0.67 for the joint objective of routine view classification and unknown view recognition, whereas a second observer reached an accuracy of 87.6%. For view quality assessment, the method achieved a Spearman's rank correlation coefficient of 0.71, whereas a second observer reached a correlation coefficient of 0.62. Conclusion: The proposed method approaches expert-level performance, enabling fully automatic selection of the most appropriate views for manual or automatic downstream analysis.

Digital consults in heart failure care: a randomized controlled trial.

Guideline-directed medical therapy (GDMT) has clear benefits on morbidity and mortality in patients with heart failure; however, GDMT use remains low. In the multicenter, open-label, investigator-initiated ADMINISTER trial, patients (n = 150) diagnosed with heart failure and reduced ejection fraction (HFrEF) were randomized (1:1) to receive usual care or a strategy using digital consults (DCs). DCs contained (1) digital data sharing from patient to clinician (pharmacotherapy use, home-measured vital signs and Kansas City Cardiomyopathy Questionnaires); (2) patient education via a text-based e-learning; and (3) guideline recommendations to all treating clinicians. All remotely gathered information was processed into a digital summary that was available to clinicians in the electronic health record before every consult. All patient interactions were standardly conducted remotely. The primary endpoint was change in GDMT score over 12 weeks (ΔGDMT); this GDMT score directly incorporated all non-conditional class 1 indications for HFrEF therapy with equal weights. The ADMINISTER trial met its primary outcome of achieving a higher GDMT in the DC group after a follow-up of 12 weeks (ΔGDMT score in the DC group: median 1.19, interquartile range (0.25, 2.3) arbitrary units versus 0.08 (0.00, 1.00) in usual care; P < 0.001). To our knowledge, this is the first multicenter randomized controlled trial that proves a DC strategy is effective to achieve GDMT optimization. ClinicalTrials.gov registration: NCT05413447 .

Social media for cardiac imagers: a review.

Cardiac imaging plays a pivotal role in the diagnosis and management of cardiovascular diseases. In the burgeoning landscape of digital technology and social media platforms, it becomes essential for cardiac imagers to know how to effectively increase the visibility and the impact of their activity. With the availability of social sites like X (formerly Twitter), Instagram, and Facebook, cardiac imagers can now reach a wider audience and engage with peers, sharing their findings, insights, and discussions. The integration of persistent identifiers, such as digital object identifiers (DOIs), facilitates traceability and citation of cardiac imaging publications across various digital platforms, further enhancing their discoverability. To maximize visibility, practical advice is provided, including the use of visually engaging infographics and videos, as well as the strategic implementation of relevant hashtags and keywords. These techniques can significantly improve the discoverability of cardiac imaging research through search engine optimization and social media algorithms. Tracking impact and engagement is crucial in the digital age, and this review discusses various metrics and tools to gauge the reach and influence of cardiac imaging publications. This includes traditional citation-based metrics and altmetrics. Moreover, this review underscores the importance of creating and updating professional profiles on social platforms and participating in relevant scientific communities online. The adoption of digital technology, social platforms, and a strategic approach to publication sharing can empower cardiac imaging professionals to enhance the visibility and impact of their research, ultimately advancing the field and improving patient care.

Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome.

Aims: The European Society of Cardiology guidelines recommend risk stratification with limited clinical parameters such as left ventricular (LV) function in patients with chronic coronary syndrome (CCS). Machine learning (ML) methods enable an analysis of complex datasets including transthoracic echocardiography (TTE) studies. We aimed to evaluate the accuracy of ML using clinical and TTE data to predict all-cause 5-year mortality in patients with CCS and to compare its performance with traditional risk stratification scores. Methods and results: Data of consecutive patients with CCS were retrospectively collected if they attended the outpatient clinic of Amsterdam UMC location AMC between 2015 and 2017 and had a TTE assessment of the LV function. An eXtreme Gradient Boosting (XGBoost) model was trained to predict all-cause 5-year mortality. The performance of this ML model was evaluated using data from the Amsterdam UMC location VUmc and compared with the reference standard of traditional risk scores. A total of 1253 patients (775 training set and 478 testing set) were included, of which 176 patients (105 training set and 71 testing set) died during the 5-year follow-up period. The ML model demonstrated a superior performance [area under the receiver operating characteristic curve (AUC) 0.79] compared with traditional risk stratification tools (AUC 0.62-0.76) and showed good external performance. The most important TTE risk predictors included in the ML model were LV dysfunction and significant tricuspid regurgitation. Conclusion: This study demonstrates that an explainable ML model using TTE and clinical data can accurately identify high-risk CCS patients, with a prognostic value superior to traditional risk scores.

Digital Solutions to Optimize Guideline-Directed Medical Therapy Prescriptions in Heart Failure Patients: Current Applications and Future Directions.

PURPOSEOF REVIEW: Guideline-directed medical therapy (GDMT) underuse is common in heart failure (HF) patients. Digital solutions have the potential to support medical professionals to optimize GDMT prescriptions in a growing HF population. We aimed to review current literature on the effectiveness of digital solutions on optimization of GDMT prescriptions in patients with HF. RECENT FINDINGS: We report on the efficacy, characteristics of the study, and population of published digital solutions for GDMT optimization. The following digital solutions are discussed: teleconsultation, telemonitoring, cardiac implantable electronic devices, clinical decision support embedded within electronic health records, and multifaceted interventions. Effect of digital solutions is reported in dedicated studies, retrospective studies, or larger studies with another focus that also commented on GDMT use. Overall, we see more studies on digital solutions that report a significant increase in GDMT use. However, there is a large heterogeneity in study design, outcomes used, and populations studied, which hampers comparison of the different digital solutions. Barriers, facilitators, study designs, and future directions are discussed. There remains a need for well-designed evaluation studies to determine safety and effectiveness of digital solutions for GDMT optimization in patients with HF. Based on this review, measuring and controlling vital signs in telemedicine studies should be encouraged, professionals should be actively alerted about suboptimal GDMT, the researchers should consider employing multifaceted digital solutions to optimize effectiveness, and use study designs that fit the unique sociotechnical aspects of digital solutions. Future directions are expected to include artificial intelligence solutions to handle larger datasets and relieve medical professional's workload.

Digital consults to optimize guideline-directed therapy: design of a pragmatic multicenter randomized controlled trial.

AIMS: Many heart failure (HF) patients do not receive optimal guideline-directed medical therapy (GDMT) despite clear benefit on morbidity and mortality outcomes. Digital consults (DCs) have the potential to improve efficiency on GDMT optimization to serve the growing HF population. The investigator-initiated ADMINISTER trial was designed as a pragmatic multicenter randomized controlled open-label trial to evaluate efficacy and safety of DC in patients on HF treatment. METHODS AND RESULTS: Patients (n = 150) diagnosed with HF with a reduced ejection fraction will be randomized to DC or standard care (1:1). The intervention group receives multifaceted DCs including (i) digital data sharing (e.g. exchange of pharmacotherapy use and home-measured vital signs), (ii) patient education via an e-learning, and (iii) digital guideline recommendations to treating clinicians. The consults are performed remotely unless there is an indication to perform the consult physically. The primary outcome is the GDMT prescription rate score, and secondary outcomes include time till full GDMT optimization, patient and clinician satisfaction, time spent on healthcare, and Kansas City Cardiomyopathy Questionnaire. Results will be reported in accordance to the CONSORT statement. CONCLUSIONS: The ADMINISTER trial will offer the first randomized controlled data on GDMT prescription rates, time till full GDMT optimization, time spent on healthcare, quality of life, and patient and clinician satisfaction of the multifaceted patient- and clinician-targeted DC for GDMT optimization.

The impact of valvular heart disease in patients with chronic coronary syndrome.

Background: The European Society of Cardiology 2019 Guidelines on chronic coronary syndrome (CCS) recommend echocardiographic measurement of the left ventricular function for risk stratification in all patients with CCS. Whereas CCS and valvular heart disease (VHD) share common pathophysiological pathways and risk factors, data on the impact of VHD in CCS patients are scarce. Methods: Clinical data including treatment and mortality of patients diagnosed with CCS who underwent comprehensive transthoracic echocardiography (TTE) in two tertiary centers were collected. The outcome was all-cause mortality. Data were analyzed with Kaplan-Meier curves and Cox proportional hazard analysis adjusting for significant covariables and time-dependent treatment. Results: Between 2014 and 2021 a total of 1,984 patients with CCS (59% men) with a median age of 65 years (interquartile range [IQR] 57-73) underwent comprehensive TTE. Severe VHD was present in 44 patients and moderate VHD in 325 patients. A total of 654 patients (33%) were treated with revascularization, 39 patients (2%) received valve repair or replacement and 299 patients (15%) died during the median follow-up time of 3.5 years (IQR 1.7-5.6). Moderate or severe VHD (hazard ratio = 1.33; 95% CI 1.02-1.72) was significantly associated with mortality risk, independent of LV function and other covariables, as compared to no/mild VHD. Conclusions: VHD has a significant impact on mortality in patients with CCS additional to LV dysfunction, which emphasizes the need for a comprehensive echocardiographic assessment in these patients.

A comparison of ECG-based home monitoring devices in adults with CHD.

BACKGROUND: Various electrocardiogram (ECG)-based devices are available for home monitoring, but the reliability in adults with CHD is unknown. Therefore, we determined the accuracy of different ECG-based devices compared to the standard 12-lead ECG in adult CHD. METHODS AND RESULTS: This is a single-centre, prospective, cross-sectional study in 176 consecutive adults with CHD (54% male, age 40 ± 16.6 years, 24% severe CHD, 84% previous surgery, 3% atrial fibrillation (AF), 24% right bundle branch block). Diagnostic accuracy of the Withings Scanwatch (lead I), Eko DUO (precordial lead), and Kardia 6L (six leads) was determined in comparison to the standard 12-lead ECG on several tasks: 1) AF classification (percentage correct), 2) QRS-morphology classification (percentage correct), and 3) ECG intervals calculation (QTc time ≤ 40 ms difference). Both tested AF algorithms had high accuracy (Withings: 100%, Kardia 6L: 97%) in ECGs that were classified. However, the Withings algorithm classified fewer ECGs as inconclusive (5%) compared to 31% of Kardia (p < 0.001). Physician evaluation of Kardia correctly classified QRS morphology more frequently (90% accuracy) compared to Eko DUO (84% accuracy) (p = 0.03). QTc was underestimated on all ECG-based devices (p < 0.01). QTc duration accuracy was acceptable in only 51% of Withings versus 70% Eko and 74% Kardia (p < 0.001 for both comparisons). CONCLUSIONS: Although all devices demonstrated high accuracy in AF detection, the Withings automatic algorithm had fewest uninterpretable results. Kardia 6L was most accurate in overall evaluation such as QRS morphology and QTc duration. These findings can inform both patients and caregivers for optimal choice of home monitoring.

Digital health in older adults for the prevention and management of cardiovascular diseases and frailty. A clinical consensus statement from the ESC Council for Cardiology Practice/Taskforce on Geriatric Cardiology, the ESC Digital Health Committee and the ESC Working Group on e-Cardiology.

Digital health technology is receiving increasing attention in cardiology. The rise of accessibility of digital health tools including wearable technologies and smart phone applications used in medical practice has created a new era in healthcare. The coronavirus pandemic has provided a new impetus for changes in delivering medical assistance across the world. This Consensus document discusses the potential implementation of digital health technology in older adults, suggesting a practical approach to general cardiologists working in an ambulatory outpatient clinic, highlighting the potential benefit and challenges of digital health in older patients with, or at risk of, cardiovascular disease. Advancing age may lead to a progressive loss of independence, to frailty, and to increasing degrees of disability. In geriatric cardiology, digital health technology may serve as an additional tool both in cardiovascular prevention and treatment that may help by (i) supporting self-caring patients with cardiovascular disease to maintain their independence and improve the management of their cardiovascular disease and (ii) improving the prevention, detection, and management of frailty and supporting collaboration with caregivers. Digital health technology has the potential to be useful for every field of cardiology, but notably in an office-based setting with frequent contact with ambulatory older adults who may be pre-frail or frail but who are still able to live at home. Cardiologists and other healthcare professionals should increase their digital health skills and learn how best to apply and integrate new technologies into daily practice and how to engage older people and their caregivers in a tailored programme of care.

Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease.

PURPOSE OF REVIEW: Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). RECENT FINDINGS: Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31-14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.

Type D Personality Associated With Increased Risk for Mortality in Adults With Congenital Heart Disease.

BACKGROUND: Type D personality has been previously shown to increase the risk for mortality in patients with acquired heart disease. OBJECTIVE: We aimed to compare mortality in adult patients with congenital heart disease (CHD) with and without type D. METHODS: Survival was assessed using prospective data from the Dutch national Congenital Corvitia registry for adults with CHD. Patients were randomly selected from the registry and characterized at inclusion in 2009 for the presence of type D using the DS14 questionnaire. RESULTS: One thousand fifty-five patients, with 484 (46%) males, a mean (SD) age of 41 (14) years, 613 (58%) having mild CHD, 348 (33%) having moderate CHD, and 94 (9%) having severe CHD, were included. Type D personality was present in 225 patients (21%). Type D was associated with an increased risk for all-cause mortality independent of age, sex, New York Heart Association class, number of prescribed medications, depression, employment status, and marital status (hazard ratio, 1.94; 95% confidence interval, 1.05-3.57; P = .033). CONCLUSION: Type D personality was associated with an increased risk for all-cause mortality in adult patients with CHD.

An implantable loop recorder or smartphone based single-lead electrocardiogram to detect arrhythmia in adults with congenital heart disease?

Background: The European Society of Cardiology (ESC) guidelines for the management of adult congenital heart disease (ACHD) recommend screening in patients at risk for arrhythmic events. However, the optimal mode of detection is unknown. Methods: Baseline and follow-up data of symptomatic ACHD patients who received an implantable loop recorder (ILR) or who participated in a smartphone based single-lead electrocardiogram study were collected. The primary endpoint was time to first detected arrhythmia. Results: In total 116 ACHD patients (mean age 42 years, 44% male) were studied. The ILR group (n = 23) differed from the smartphone based single-lead electrocardiogram group (n = 93) in having a greater part of males and had more severe CHD and (near) syncope as qualifying diagnosis. In the smartphone based single-lead electrocardiogram group history of arrhythmia and palpitations were more frequent (all p < 0.05). Monitoring was performed for 40 and 79 patient-years for the ILR- and smartphone based single-lead electrocardiogram group, respectively. Arrhythmias occurred in 33 patients with an equal median time for both groups to first arrhythmia of 3 months (HR of 0.7, p = 0.81). Furthermore, atrial fibrillation occurred most often (n = 16) and common therapy changes included medication changes (n = 7) and implantation of pacemaker or Implantable Cardioverter Defibrillator (ICD) (N = 4). Symptoms or mode of detection were not a determinant of the first event. Conclusion: Non-invasive smartphone based single-lead electrocardiogram monitoring could be an acceptable alternative for ILR implantation in detecting arrhythmia in symptomatic ACHD patients in respect to diagnostic yield, safety and management decisions, especially in those without syncope.

Routine Echocardiography and Artificial Intelligence Solutions.

Introduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and prognostic purposes. These recommendations have led to an increase in number of performed studies each requiring diligent processing and reviewing. The standard work pattern of image analysis including quantification and reporting has become highly resource intensive and time consuming. Existence of a large number of datasets with digital echocardiography images and recent advent of AI technology have created an environment in which artificial intelligence (AI) solutions can be developed successfully to automate current manual workflow. Methods and Results: We report on published AI solutions for echocardiography analysis on methods' performance, characteristics of the used data and imaged population. Contemporary AI applications are available for automation and advent in the image acquisition, analysis, reporting and education. AI solutions have been developed for both diagnostic and predictive tasks in echocardiography. Left ventricular function assessment and quantification have been most often performed. Performance of automated image view classification, image quality enhancement, cardiac function assessment, disease classification, and cardiac event prediction was overall good but most studies lack external evaluation. Conclusion: Contemporary AI solutions for image acquisition, analysis, reporting and education are developed for relevant tasks with promising performance. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome. Some of the challenges have yet to be overcome, however, none of them are insurmountable.

ESC working group on e-cardiology position paper: use of commercially available wearable technology for heart rate and activity tracking in primary and secondary cardiovascular prevention-in collaboration with the European Heart Rhythm Association, European Association of Preventive Cardiology, Association of Cardiovascular Nursing and Allied Professionals, Patient Forum, and the Digital Health Committee.

Commercially available health technologies such as smartphones and smartwatches, activity trackers and eHealth applications, commonly referred to as wearables, are increasingly available and used both in the leisure and healthcare sector for pulse and fitness/activity tracking. The aim of the Position Paper is to identify specific barriers and knowledge gaps for the use of wearables, in particular for heart rate (HR) and activity tracking, in clinical cardiovascular healthcare to support their implementation into clinical care. The widespread use of HR and fitness tracking technologies provides unparalleled opportunities for capturing physiological information from large populations in the community, which has previously only been available in patient populations in the setting of healthcare provision. The availability of low-cost and high-volume physiological data from the community also provides unique challenges. While the number of patients meeting healthcare providers with data from wearables is rapidly growing, there are at present no clinical guidelines on how and when to use data from wearables in primary and secondary prevention. Technical aspects of HR tracking especially during activity need to be further validated. How to analyse, translate, and interpret large datasets of information into clinically applicable recommendations needs further consideration. While the current users of wearable technologies tend to be young, healthy and in the higher sociodemographic strata, wearables could potentially have a greater utility in the elderly and higher-risk population. Wearables may also provide a benefit through increased health awareness, democratization of health data and patient engagement. Use of continuous monitoring may provide opportunities for detection of risk factors and disease development earlier in the causal pathway, which may provide novel applications in both prevention and clinical research. However, wearables may also have potential adverse consequences due to unintended modification of behaviour, uncertain use and interpretation of large physiological data, a possible increase in social inequality due to differential access and technological literacy, challenges with regulatory bodies and privacy issues. In the present position paper, current applications as well as specific barriers and gaps in knowledge are identified and discussed in order to support the implementation of wearable technologies from gadget-ology into clinical cardiology.

22q11.2 deletion syndrome is associated with increased mortality in adults with tetralogy of Fallot and pulmonary atresia with ventricular septal defect.

BACKGROUND: 22q11.2 Deletion syndrome (22q11.2DS) is common in patients with tetralogy of Fallot (TOF) or pulmonary atresia with ventricular septal defect (PA/VSD) and is associated with worse outcomes in children. Whether this impaired prognosis is also translated into adulthood is unknown, as data in adult patients are limited. We aimed to compare long-term outcomes in adults with TOF or PA/VSD both with and without 22q11.2DS. METHODS: This study prospectively followed a nationwide multicenter cohort of TOF or PA/VSD patients with genetically confirmed presence or absence of 22q11.2DS, from inclusion in the Dutch national CONCOR registry for adults with congenital heart disease (CHD) onward. Outcome measures included all-cause mortality, cardiac mortality, need for pulmonary valve replacement (PVR), ventricular arrhythmias (VA), pacemaker implantation, and ICD implantation. RESULTS: In total, 479 patients were included (277 (58%) male, median age 28 [IQR; 21-37] years, 62 (13%) with PA/VSD, 34 (7%) with 22q11.2DS). During a median follow-up of 11 [IQR; 6-13] years, 52 (11%) patients died (8 with 22q11.2DS and 44 without 22q11.2DS). Patients with 22q11.2DS had significant decreased survival after 12 years (76% [95% CI; 62-93]) compared to patients without 22q11.2DS (89% [95% CI; 86-92], p = 0.008). 22q11.2DS was associated with increased risk of all-cause mortality and cardiac-mortality, independent of age, sex, and PA/VSD. No association was found between 22q11.2DS and late complications i.e. PVR, VA, pacemaker, or ICD implantation. CONCLUSIONS: Adults with TOF or PA/VSD with 22q11.2DS have a significantly worse survival than adults without this deletion. In patients with TOF or PA/VSD, genetic analysis for the presence of 22q11.2DS is important for risk stratification and genetic counseling.

How to initiate eHealth in congenital heart disease patients?

Patients with congenital heart disease (CHD) are a vulnerable subgroup of cardiac patients. These patients have a high morbidity and high mortality rate. As the number of patients with CHD keeps growing, while also getting older, new tools for the care and follow-up of these vulnerable patients are warranted. eHealth has an enormous potential to revolutionize health care, and particularly for CHD patients, by expanding care beyond hospital walls and even moving some of the provided care to the comfort of home. As new eHealth tools continue to grow in number, such as invasive eHealth tools, health care delivered through eHealth continues to evolve. This teaching series summarizes current insights and discusses challenges yet to be overcome. Importantly, none of them are insurmountable. This all lays ground for a promising future for eHealth in the care of patients with CHD.