Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device

Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.

Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review

Background

Despite the potential of artificial intelligence (AI) in enhancing cardiovascular care, its integration into clinical practice is limited by a lack of evidence on its effectiveness with respect to human experts or gold standard practices in real-world settings.

Objectives

The purpose of this study was to identify AI interventions in cardiology that have been prospectively validated against human expert benchmarks or gold standard practices, assessing their effectiveness, and identifying future research areas.

Methods

We systematically reviewed Scopus and MEDLINE to identify peer-reviewed publications that involved prospective human validation of AI-based interventions in cardiology from January 2015 to December 2023.

Results

Of 2,351 initial records, 64 studies were included. Among these studies, 59 (92.2%) were published after 2020. A total of 11 (17.2%) randomized controlled trials were published. AI interventions in 44 articles (68.75%) reported definite clinical or operational improvements over human experts. These interventions were mostly used in imaging (n = 14, 21.9%), ejection fraction (n = 10, 15.6%), arrhythmia (n = 9, 14.1%), and coronary artery disease (n = 12, 18.8%) application areas. Convolutional neural networks were the most common predictive model (n = 44, 69%), and images were the most used data type (n = 38, 54.3%). Only 22 (34.4%) studies made their models or data accessible.

Conclusions

This review identifies the potential of AI in cardiology, with models often performing equally well as human counterparts for specific and clearly scoped tasks suitable for such models. Nonetheless, the limited number of randomized controlled trials emphasizes the need for continued validation, especially in real-world settings that closely examine joint human AI decision-making.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review

PURPOSE OF REVIEW

Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG.

RECENT FINDINGS

Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

Detecting QT prolongation from a single-lead ECG with deep learning

For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with continuous monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would enable out-of-hospital care. We therefore develop a deep learning model that infers QT intervals from ECG Lead-I-the lead that is often available in ambulatory ECG monitors-and use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. QTNet-a deep neural network that infers QT intervals from Lead-I ECG-was trained using over 3 million ECGs from 653 thousand patients at the Massachusetts General Hospital and tested on an internal-test set consisting of 633 thousand ECGs from 135 thousand patients. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another healthcare institution. On both evaluations, the model achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). Finally, QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. These results show that drug-induced QT prolongation risk can be tracked from ECG Lead-I using deep learning.

A Human-Centered AI Framework for Efficient Labelling of ECGs From Drug Safety Trials

Drug safety trials require substantial ECG labelling like, in thorough QT studies, measurements of the QT interval, whose prolongation is a biomarker of proarrhythmic risk. The traditional method of manually measuring the QT interval is time-consuming and error-prone. Studies have demonstrated the potential of deep learning (DL)-based methods to automate this task but expert validation of these computerized measurements remains of paramount importance, particularly for abnormal ECG recordings. In this paper, we propose a highly automated framework that combines such a DL-based QT estimator with human expertise. The framework consists of 3 key components: (1) automated QT measurement with uncertainty quantification (2) expert review of a few DL-based measurements, mostly those with high model uncertainty and (3) recalibration of the unreviewed measurements based on the expert-validated data. We assess its effectiveness on 3 drug safety trials and show that it can significantly reduce effort required for ECG labelling-in our experiments only 10% of the data were reviewed per trial-while maintaining high levels of QT accuracy. Our study thus demonstrates the possibility of productive human-machine collaboration in ECG analysis without any compromise on the reliability of subsequent clinical interpretations.

QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence-Enabled Electrocardiograms

BACKGROUND

Prediction of drug-induced long QT syndrome (diLQTS) is of critical importance given its association with torsades de pointes. There is no reliable method for the outpatient prediction of diLQTS.

OBJECTIVES

This study sought to evaluate the use of a convolutional neural network (CNN) applied to electrocardiograms (ECGs) to predict diLQTS in an outpatient population.

METHODS

We identified all adult outpatients newly prescribed a QT-prolonging medication between January 1, 2003, and March 31, 2022, who had a 12-lead sinus ECG in the preceding 6 months. Using risk factor data and the ECG signal as inputs, the CNN QTNet was implemented in TensorFlow to predict diLQTS.

RESULTS

Models were evaluated in a held-out test dataset of 44,386 patients (57% female) with a median age of 62 years. Compared with 3 other models relying on risk factors or ECG signal or baseline QTc alone, QTNet achieved the best (P < 0.001) performance with a mean area under the curve of 0.802 (95% CI: 0.786-0.818). In a survival analysis, QTNet also had the highest inverse probability of censorship-weighted area under the receiver-operating characteristic curve at day 2 (0.875; 95% CI: 0.848-0.904) and up to 6 months. In a subgroup analysis, QTNet performed best among males and patients ≤50 years or with baseline QTc <450 ms. In an external validation cohort of solely suburban outpatient practices, QTNet similarly maintained the highest predictive performance.

CONCLUSIONS

An ECG-based CNN can accurately predict diLQTS in the outpatient setting while maintaining its predictive performance over time. In the outpatient setting, our model could identify higher-risk individuals who would benefit from closer monitoring.

KardiaMobile 6L for measuring QT interval in people having antipsychotic medication to inform early value assessment: a systematic review

Background

The indication for this assessment is the use of the KardiaMobile six-lead electrocardiogram device for the assessment of QT interval-based cardiac risk in service users prior to the initiation of, or for the monitoring of, antipsychotic medications, which are associated with an established risk of QT interval prolongation.

Objectives

To provide an early value assessment of whether KardiaMobile six-lead has the potential to provide an effective and safe alternative to 12-lead electrocardiogram for initial assessment and monitoring of QT interval-based cardiac risk in people taking antipsychotic medications.

Review methods

Twenty-seven databases were searched to April/May 2022. Review methods followed published guidelines. Where appropriate, study quality was assessed using appropriate risk of bias tools. Results were summarised by research question; accuracy/technical performance; clinical effects (on cardiac and psychiatric outcomes); service user acceptability/satisfaction; costs of KardiaMobile six-lead.

Results

We did not identify any studies which provided information about the diagnostic accuracy of KardiaMobile six-lead, for the detection of corrected QT-interval prolongation, in any population. All studies which reported information about agreement between QT interval measurements (corrected and/or uncorrected) with KardiaMobile six-lead versus 12-lead electrocardiogram were conducted in non-psychiatric populations, used cardiologists and/or multiple readers to interpret electrocardiograms. Where reported or calculable, the mean difference in corrected QT interval between devices (12-lead electrocardiogram vs. KardiaMobile six-lead) was generally small (≤ 10 ms) and corrected QT interval measured using KardiaMobile six-lead was consistently lower than that measured using 12-lead electrocardiogram. All information about the use of KardiaMobile six-lead, in the context of QT interval-based cardiac risk assessment for service users who require antipsychotic medication, was taken from retrospective surveys of staff and service users who had chosen to use KardiaMobile six-lead during pilots, described in two unpublished project reports. It is important to note that both these project reports relate to pilot studies which were not intended to be used in wider evaluations of KardiaMobile six-lead for use in the NHS. Both reports included survey results which indicated that the use of KardiaMobile six-lead may be associated with reductions in the time taken to complete an electrocardiogram and costs, relative to 12-lead electrocardiogram, and that KardiaMobile six-lead was preferred over 12-lead electrocardiogram by almost all responding staff and service users.

Limitations

There was a lack of published evidence about the efficacy of KardiaMobile six-lead for initial assessment and monitoring of QT interval-based cardiac risk in people taking antipsychotic medications.

Conclusions

There is insufficient evidence to support a full diagnostic assessment evaluating the clinical and cost effectiveness of KardiaMobile six-lead, in the context of QT interval-based cardiac risk assessment for service users who require antipsychotic medication. The evidence to inform the aims of this early value assessment (i.e. to assess whether the device has the potential to be clinically effective and cost-effective) was also limited. This report includes a comprehensive list of research recommendations, both to reduce the uncertainty around this early value assessment and to provide the additional data needed to inform a full diagnostic assessment, including cost-effectiveness modelling.

Study registration

This study is registered as PROSPERO CRD42022336695.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135520) and is published in full in Health Technology Assessment; Vol. 28, No. 19. See the NIHR Funding and Awards website for further award information.

Adopting artificial intelligence in cardiovascular medicine: a scoping review

Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.

Knowledge mapping of digital medicine in cardiovascular diseases from 2004 to 2022: A bibliometric analysis

Objective

To review studies on digital medicine in cardiovascular diseases (CVD), discuss its development process, knowledge structure and research hotspots, and provide a perspective for researchers in this field.

Methods

The relevant literature in recent 20 years (January 2004 to October 2022) were retrieved from the Web of Science Core Collection (WoSCC). CiteSpace was used to demonstrate our knowledge of keywords, co-references and speculative frontiers. VOSviewer was used to chart the contributions of authors, institutions and countries and incorporates their link strength into the table.

Results

A total of 5265 English articles in set timespan were included. The number of publications increased steadily annually. The United States (US) produced the highest number of publications, followed by England. Most publications were from Harvard Medicine School, followed by Massachusetts General Hospital and Brigham Women's Hospital. The most authoritative academic journal was JMIR mHealth and uHealth. Noseworthy PA may have the highest influence in this intersected field with the highest number of citations and total link strength. The utilization of wearable mobile devices in the context of CVD, encompassing the identification of risk factors, diagnosis and prevention of diseases, as well as early intervention and remote management of diseases, has been widely acknowledged as a knowledge base and an area of current interest. To investigate the impact of various digital medicine interventions on chronic care and assess their clinical effectiveness, examine the potential of machine learning (ML) in delivering clinical care for atrial fibrillation (AF) and identifying early disease risk factors, as well as explore the development of disease prediction models using neural networks (NNs), ML and unsupervised learning in CVD prognosis, may emerge as future trends and areas of focus.

Conclusion

Recently, there has been a significant surge of interest in the investigation of digital medicine in CVD. This initial bibliometric study offers a comprehensive analysis of the research landscape pertaining to digital medicine in CVD, thereby furnishing related scholars with a dependable reference to facilitate further progress in this domain.

Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached?

Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.

Toward advanced diagnosis and management of inherited arrhythmia syndromes: Harnessing the capabilities of artificial intelligence and machine learning

The use of advanced computational technologies, such as artificial intelligence (AI), is now exerting a significant influence on various aspects of life, including health care and science. AI has garnered remarkable public notice with the release of deep learning models that can model anything from artwork to academic papers with minimal human intervention. Machine learning, a method that uses algorithms to extract information from raw data and represent it in a model, and deep learning, a method that uses multiple layers to progressively extract higher-level features from the raw input with minimal human intervention, are increasingly leveraged to tackle problems in the health sector, including utilization for clinical decision support in cardiovascular medicine. Inherited arrhythmia syndromes are a clinical domain where multiple unanswered questions remain despite unprecedented progress over the past 2 decades with the introduction of large panel genetic testing and the first steps in precision medicine. In particular, AI tools can help address gaps in clinical diagnosis by identifying individuals with concealed or transient phenotypes; enhance risk stratification by elevating recognition of underlying risk burden beyond widely recognized risk factors; improve prediction of response to therapy, and further prognostication. In this contemporary review, we provide a summary of the AI models developed to solve challenges in inherited arrhythmia syndromes and also outline gaps that can be filled with the development of intelligent AI models.

Deep learning in precision medicine and focus on glioma

Deep learning (DL) has been successfully applied to different fields for a range of tasks. In medicine, DL methods have been also used to improve the efficiency of disease diagnosis. In this review, we first summarize the history of the development of artificial intelligence models, demonstrate the features of the subtypes of machine learning and different DL networks, and then explore their application in the different fields of precision medicine, such as cardiology, gastroenterology, ophthalmology, dermatology, and oncology. By digging more information and extracting multilevel features from medical data, we found that DL helps doctors assess diseases automatically and monitor patients' physical health. In gliomas, research regarding application prospect of DL was mainly shown through magnetic resonance imaging and then by pathological slides. However, multi-omics data, such as whole exome sequence, RNA sequence, proteomics, and epigenomics, have not been covered thus far. In general, the quality and quantity of DL datasets still need further improvements, and more fruitful multi-omics characteristics will bring more comprehensive and accurate diagnosis in precision medicine and glioma.

Accuracy of mobile 6-lead electrocardiogram device for assessment of QT interval: a prospective validation study

INTRODUCTION

Ambulatory assessment of the heart rate-corrected QT interval (QTc) can be of diagnostic value, for example in patients on QTc-prolonging medication. Repeating sequential 12-lead electrocardiograms (ECGs) to monitor the QTc is cumbersome, but mobile ECG (mECG) devices can potentially solve this problem. As the accuracy of single-lead mECG devices is reportedly variable, a multilead mECG device may be more accurate.

METHODS

This prospective dual-centre study included outpatients visiting our cardiology clinics for any indication. Participants underwent an mECG recording using a smartphone-enabled 6‑lead mECG device immediately before or immediately after a conventional 12-lead ECG recording. Multiple QTc values in both recordings were manually measured in leads I and II using the tangent method and subsequently compared.

RESULTS

In total, 234 subjects were included (mean ± standard deviation (SD) age: 57 ± 17 years; 58% males), of whom 133 (57%) had cardiac disease. QTc measurement in any lead was impossible due to artefacts in 16 mECGs (7%) and no 12-lead ECGs. Mean (± SD) QTc in lead II on the mECG and 12-lead ECG was 401 ± 30 and 406 ± 31 ms, respectively. Mean (± SD) absolute difference in QTc values between both modalities was 12 ± 9 ms (r = 0.856; p < 0.001). In 55% of the subjects, the absolute difference between QTc values was < 10 ms.

CONCLUSION

A 6-lead mECG allows for QTc assessment with good accuracy and can be used safely in ambulatory QTc monitoring. This may improve patient satisfaction and reduce healthcare costs.

Current gaps in knowledge in inherited arrhythmia syndromes

The 3 most common inherited arrhythmia syndromes-Brugada syndrome, congenital long QT syndrome and catecholaminergic polymorphic ventricular tachycardia-were initially described in the previous century. Since then, research has evolved, which has enabled us to identify patients prior to the onset of potentially life-threatening symptoms. However, there are significant gaps in knowledge that complicate clinical management of these patients today. With this review paper, we aim to highlight the most important knowledge gaps in clinical research of these inherited arrhythmia syndromes.

QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG

QT prolongation often leads to fatal arrhythmia and sudden cardiac death. Antiarrhythmic drugs can increase the risk of QT prolongation and therefore require strict post-administration monitoring and dosage control. Measurement of the QT interval from the 12-lead electrocardiogram (ECG) by a trained expert, in a clinical setting, is the accepted method for tracking QT prolongation. Recent advances in wearable ECG technology, however, raise the possibility of automated out-of-hospital QT tracking. Applications of Deep Learning (DL) - a subfield within Machine Learning - in ECG analysis holds the promise of automation for a variety of classification and regression tasks. In this work, we propose a residual neural network, QTNet, for the regression of QT intervals from a single lead (Lead-I) ECG. QTNet is trained in a supervised manner on a large ECG dataset from a U.S. hospital. We demonstrate the robustness and generalizability of QTNet on four test-sets; one from the same hospital, one from another U.S. hospital, and two public datasets. Over all four datasets, the mean absolute error (MAE) in the estimated QT interval ranges between 9ms and 15.8ms. Pearson correlation coefficients vary between 0.899 and 0.914. By contrast, QT interval estimation on these datasets with a standard method for automated ECG analysis (NeuroKit2) yields MAEs between 22.29ms and 90.79ms, and Pearson correlation coefficients 0.345 and 0.620. These results demonstrate the utility of QTNet across distinct datasets and patient populations, thereby highlighting the potential utility of DL models for ubiquitous QT tracking.Clinical Relevance- QTNet can be applied to inpatient or ambulatory Lead-I ECG signals to track QT intervals. The method facilitates ambulatory monitoring of patients at risk of QT prolongation.

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.

The diagnostic value of electrocardiogram-based machine learning in long QT syndrome: a systematic review and meta-analysis

Introduction

To perform a meta-analysis to discover the performance of ML algorithms in identifying Congenital long QT syndrome (LQTS).

Methods

The searched databases included Cochrane, EMBASE, Web of Science, and PubMed. Our study considered all English-language studies that reported the detection of LQTS using ML algorithms. Quality was assessed using QUADAS-2 and QUADAS-AI tools. The bivariate mixed effects models were used in our study. Based on genotype data for LQTS, we performed a subgroup analysis.

Results

Out of 536 studies, 8 met all inclusion criteria. The pooled area under the receiving operating curve (SAUROC) for detecting LQTS was 0.95 (95% CI: 0.31-1.00); sensitivity was 0.87 (95% CI: 0.83-0.90), and specificity was 0.91 (95% CI: 0.88-0.93). Additionally, diagnostic odd ratio (DOR) was 65 (95% CI: 39-109). The positive likelihood ratio (PLR) was 9.3 (95% CI: 7.0-12.3) and the negative likelihood ratio (NLR) was 0.14 (95% CI: 0.11-0.20), with very low heterogeneity (I²= 16%).

Discussion

We found that machine learning can be used to detect features of rare cardiovascular disease like LQTS, thus increasing our understanding of intelligent interpretation of ECG. To improve ML performance in the classification of LQTS subtypes, further research is required.

Systematic Review Registration

identifier PROSPERO CRD42022360122.

AI-Assisted QT Measurements for Highly Automated Drug Safety Studies

Rate-corrected QT interval (QTc) prolongation has been suggested as a biomarker for the risk of drug-induced torsades de pointes, and is therefore monitored during clinical trials for the assessment of drug safety. Manual QT measurements by expert ECG analysts are expensive, laborious and prone to errors. Wavelet-based delineators and other automatic methods do not generalize well to different T wave morphologies and may require laborious tuning. Our study investigates the robustness of convolutional neural networks (CNNs) for QT measurement. We trained 3 CNN-based deep learning models on a private ECG database with human expert-annotated QT intervals. Among these models, we propose a U-Net model, which is widely used for segmentation tasks, to build a novel clinically useful QT estimator that includes QT delineation for better interpretability. We tested the 3 models on four external databases, amongst which a clinical trial investigating four drugs. Our results show that the deep learning models are in stronger agreement with the experts than the state-of-the-art wavelet-based algorithm. Indeed, the deep learning models yielded up to 71% of accurate QT measurements (absolute difference between manual and automatic QT below 15 ms) whereas the wavelet-based algorithm only allowed 52% of QT accuracy. For the 2 studies of drugs with small to no QT prolonging effect, a mean absolute difference of 6 ms (std = 5 ms) was obtained between the manual and deep learning methods. For the other 2 drugs with more significant effect on the volunteers, a mean difference of up to 17 ms (std = 17 ms) was obtained. The proposed models are therefore promising for automated QT measurements during clinical trials. They can analyze various ECG morphologies from a diversity of individuals although some QT-prolonged ECGs can be challenging. The U-Net model is particularly interesting for our application as it facilitates expert review of automatic QT intervals, which is still required by regulatory bodies, by providing QRS onset and T offset positions that are consistent with the estimated QT intervals.

Artificial Intelligence in Cardiology: Applications and Obstacles

Artificial intelligence (AI) technology is poised to alter the flow of daily life, and in particular, medicine, where it may eventually complement the physician's work in diagnosing and treating disease. Despite the recent frenzy and uptick in AI research over the past decade, the integration of AI into medical practice is in its early stages. Cardiology stands to benefit due to its many diagnostic modalities and diverse treatments. AI methods have been applied to various domains within cardiology: imaging, electrocardiography, wearable devices, risk prediction, and disease classification. While many AI-based approaches have been developed that perform equal to or better than the state-of-the-art, few prospective randomized studies have evaluated their use. Furthermore, obstacles at the intersection of medicine and AI remain unsolved, including model understanding, bias, model evaluation, relevance and reproducibility, and legal and ethical dilemmas. We summarize recent and current applications of AI in cardiology, followed by a discussion of the aforementioned complications.

Added value of patient- and drug-related factors to stratify drug-drug interaction alerts for risk of QT prolongation: Development and validation of a risk prediction model

AIMS

Many clinical decision support systems trigger warning alerts for drug-drug interactions potentially leading to QT prolongation and torsades de pointes (QT-DDIs). Unfortunately, there is overalerting and underalerting because stratification is only based on a fixed QT-DDI severity level. We aimed to improve QT-DDI alerting by developing and validating a risk prediction model considering patient- and drug-related factors.

METHODS

We fitted 31 predictor candidates to a stepwise linear regression for 1000 bootstrap samples and selected the predictors present in 95% of the 1000 models. A final linear regression model with those variables was fitted on the original development sample (350 QT-DDIs). This model was validated on an external dataset (143 QT-DDIs). Both true QTc and predicted QTc were stratified into three risk levels (low, moderate and high). Stratification of QT-DDIs could be appropriate (predicted risk = true risk), acceptable (one risk level difference) or inappropriate (two risk levels difference).

RESULTS

The final model included 11 predictors with the three most important being use of antiarrhythmics, age and baseline QTc. Comparing current practice to the prediction model, appropriate stratification increased significantly from 37% to 54% appropriate QT-DDIs (increase of 17.5% on average [95% CI +5.4% to +29.6%], p_adj  = 0.006) and inappropriate stratification decreased significantly from 13% to 1% inappropriate QT-DDIs (decrease of 11.2% on average [95% CI -17.7% to -4.7%], p_adj  ≤ 0.001).

CONCLUSION

The prediction model including patient- and drug-related factors outperformed QT alerting based on QT-DDI severity alone and therefore is a promising strategy to improve DDI alerting.

Applications of Deep Learning in Endocrine Neoplasms

Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multiple disease areas. In cancer, and particularly endocrine cancer, DL approaches have been shown to be useful in tasks ranging from tumor grading to gene expression prediction. This review summarizes the current state of DL research in endocrine cancer histopathology with an emphasis on experimental design, significant findings, and key limitations.

Artificial Intelligence Enabled Classification of Hypertrophic Heart Diseases Using Electrocardiograms

Background

Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care.

Objective

To evaluate if artificial intelligence-enabled analysis of the 12-lead electrocardiogram (ECG) facilitates automated detection and classification of LVH.

Methods

We used a pretrained convolutional neural network to derive numerical representations of 12-lead ECG waveforms from patients in a multi-institutional healthcare system who had cardiac diseases associated with LVH (n = 50,709), including cardiac amyloidosis (n = 304), hypertrophic cardiomyopathy (n = 1056), hypertension (n = 20,802), aortic stenosis (n = 446), and other causes (n = 4766). We then regressed LVH etiologies relative to no LVH on age, sex, and the numerical 12-lead representations using logistic regression ("LVH-Net"). To assess deep learning model performance on single-lead data analogous to mobile ECGs, we also developed 2 single-lead deep learning models by training models on lead I ("LVH-Net Lead I") or lead II ("LVH-Net Lead II") from the 12-lead ECG. We compared the performance of the LVH-Net models to alternative models fit on (1) age, sex, and standard ECG measures, and (2) clinical ECG-based rules for diagnosing LVH.

Results

The areas under the receiver operator characteristic curve of LVH-Net by specific LVH etiology were cardiac amyloidosis 0.95 [95% CI, 0.93-0.97], hypertrophic cardiomyopathy 0.92 [95% CI, 0.90-0.94], aortic stenosis LVH 0.90 [95% CI, 0.88-0.92], hypertensive LVH 0.76 [95% CI, 0.76-0.77], and other LVH 0.69 [95% CI 0.68-0.71]. The single-lead models also discriminated LVH etiologies well.

Conclusion

An artificial intelligence-enabled ECG model is favorable for detection and classification of LVH and outperforms clinical ECG-based rules.

Development of an AI-Driven QT Correction Algorithm for Patients in Atrial Fibrillation

BACKGROUND

Prolongation of the QTc interval is associated with the risk of torsades de pointes. Determination of the QTc interval is therefore of critical importance. There is no reliable method for measuring or correcting the QT interval in atrial fibrillation (AF).

OBJECTIVES

The authors sought to evaluate the use of a convolutional neural network (CNN) applied to AF electrocardiograms (ECGs) for accurately estimating the QTc interval and ruling out prolongation of the QTc interval.

METHODS

The authors identified patients with a 12-lead ECG in AF within 10 days of a sinus ECG, with similar (±10 ms) QRS durations, between October 23, 2001, and November 5, 2021. A multilayered deep CNN was implemented in TensorFlow 2.5 (Google) to predict the MUSE (GE Healthcare) software-generated sinus QTc value from an AF ECG waveform, demographic characteristics, and software-generated features.

RESULTS

The study identified 6,432 patients (44% female) with an average age of 71 years. The CNN predicted sinus QTc values with a mean absolute error of 22.2 ms and root mean squared error of 30.6 ms, similar to the intrinsic variability of the sinus QTc interval. Approximately 84% and 97% of the model's predictions were contained within 1 SD (±30.6 ms) and 2 SD (±61.2 ms) from the sinus QTc interval. The model outperformed the AFQTc method, exhibiting narrower error ranges (mean absolute error comparison P < 0.0001). The model performed best for ruling out QTc prolongation (negative predictive value 0.82 male, 0.92 female; specificity 0.92 male, 0.97 female).

CONCLUSIONS

A CNN model applied to AF ECGs accurately predicted the sinus QTc interval, outperforming current alternatives and exhibiting a high negative predictive value.

Sleep Arousal-Related Ventricular Repolarization Lability Is Associated With Cardiovascular Mortality in Older Community-Dwelling Men

BACKGROUND

Sleep is fragmented by brief arousals, and excessive arousal burden has been linked to increased cardiovascular (CV) risk, but mechanisms are poorly understood.

RESEARCH QUESTION

Do arousals trigger cardiac ventricular repolarization lability that may predispose people to long-term cardiovascular mortality?

STUDY DESIGN AND METHODS

This study analyzed 407,541 arousals in the overnight polysomnograms of 2,558 older men in the Osteoporotic Fractures in Men sleep study. QT and RR intervals were measured beat-to-beat starting 15 s prior to arousal onset until 15 s past onset. Ventricular repolarization lability was quantified by using the QT variability index (QTVi).

RESULTS

During 10.1 ± 2.5 years of follow-up, 1,000 men died of any cause, including 348 CV deaths. During arousals, QT and RR variability increased on average by 5 and 55 ms, respectively, resulting in a paradoxical transient decrease in QTVi from 0.07 ± 1.68 to -1.00 ± 1.68. Multivariable Cox proportional hazards analysis adjusted for age, BMI, cardiovascular and respiratory risk factors, sleep-disordered breathing and arousal, diabetes, and Parkinson disease indicated that excessive QTVi during arousal was independently associated with all-cause and CV mortality (all-cause hazard ratio, 1.20 [95% CI, 1.04-1.38; P = .012]; CV hazard ratio, 1.29 [95% CI, 1.01 -1.65; P = .043]).

INTERPRETATION

Arousals affect ventricular repolarization. A disproportionate increase in QT variability during arousal is associated with an increased all-cause and CV mortality and may reflect ventricular repolarization maladaptation to the arousal stimulus. Whether arousal-related QTVi can be used for more tailored risk stratification warrants further study, including evaluating whether arousal suppression attenuates ventricular repolarization lability and reduces subsequent mortality.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov; No.: NCT00070681; URL: www.

CLINICALTRIALS

gov.

Emerging role of artificial intelligence in cardiac electrophysiology

Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.

Comparison Between QT and Corrected QT Interval Assessment by an Apple Watch With the AccurBeat Platform and by a 12‑Lead Electrocardiogram With Manual Annotation: Prospective Observational Study

Background

Abnormal prolongation or shortening of the QT interval is associated with increased risk for ventricular arrhythmias and sudden cardiac death. For continuous monitoring, widespread use, and prevention of cardiac events, advanced wearable technologies are emerging as promising surrogates for conventional 12‑lead electrocardiogram (ECG) QT interval assessment. Previous studies have shown a good agreement between QT and corrected QT (QTc) intervals measured on a smartwatch ECG and a 12-lead ECG, but the clinical accuracy of computerized algorithms for QT and QTc interval measurement from smartwatch ECGs is unclear.

Objective

The prospective observational study compared the smartwatch-recorded QT and QTc assessed using AccurKardia’s AccurBeat platform with the conventional 12‑lead ECG annotated manually by a cardiologist.

Methods

ECGs were collected from healthy participants (without any known cardiovascular disease) aged >22 years. Two consecutive 30-second ECG readings followed by (within 15 minutes) a 10-second standard 12-lead ECG were recorded for each participant. Characteristics of the participants were compared by sex using a 2-sample t test and Wilcoxon rank sum test. Statistical comparisons of heart rate (HR), QT interval, and QTc interval between the platform and the 12-lead ECG, ECG lead I, and ECG lead II were done using the Wilcoxon sign rank test. Linear regression was used to predict QTc and QT intervals from the ECG based on the platform’s QTc/QT intervals with adjustment for age, sex, and difference in HR measurement. The Bland-Altman method was used to check agreement between various QT and QTc interval measurements.

Results

A total of 50 participants (32 female, mean age 46 years, SD 1 year) were included in the study. The result of the regression model using the platform measurements to predict the 12-lead ECG measurements indicated that, in univariate analysis, QT/QTc intervals from the platform significantly predicted QT/QTc intervals from the 12-lead ECG, ECG lead I, and ECG lead II, and this remained significant after adjustment for sex, age, and change in HR. The Bland-Altman plot results found that 96% of the average QTc interval measurements between the platform and QTc intervals from the 12-lead ECG were within the 95% confidence limit of the average difference between the two measurements, with a mean difference of –10.5 (95% limits of agreement –71.43, 50.43). A total of 94% of the average QT interval measurements between the platform and the 12-lead ECG were within the 95% CI of the average difference between the two measurements, with a mean difference of –6.3 (95% limits of agreement –54.54, 41.94).

Conclusions

QT and QTc intervals obtained by a smartwatch coupled with the platform’s assessment were comparable to those from a 12-lead ECG. Accordingly, with further refinements, remote monitoring using this technology holds promise for the identification of QT interval prolongation.

Moving towards vertically integrated artificial intelligence development

Substantial interest and investment in clinical artificial intelligence (AI) research has not resulted in widespread translation to deployed AI solutions. Current attention has focused on bias and explainability in AI algorithm development, external validity and model generalisability, and lack of equity and representation in existing data. While of great importance, these considerations also reflect a model-centric approach seen in published clinical AI research, which focuses on optimising architecture and performance of an AI model on best available datasets. However, even robustly built models using state-of-the-art algorithms may fail once tested in realistic environments due to unpredictability of real-world conditions, out-of-dataset scenarios, characteristics of deployment infrastructure, and lack of added value to clinical workflows relative to cost and potential clinical risks. In this perspective, we define a vertically integrated approach to AI development that incorporates early, cross-disciplinary, consideration of impact evaluation, data lifecycles, and AI production, and explore its implementation in two contrasting AI development pipelines: a scalable "AI factory" (Mayo Clinic, Rochester, United States), and an end-to-end cervical cancer screening platform for resource poor settings (Paps AI, Mbarara, Uganda). We provide practical recommendations for implementers, and discuss future challenges and novel approaches (including a decentralised federated architecture being developed in the NHS (AI4VBH, London, UK)). Growth in global clinical AI research continues unabated, and introduction of vertically integrated teams and development practices can increase the translational potential of future clinical AI projects.

Qualitative Evaluation of an Artificial Intelligence–Based Clinical Decision Support System to Guide Rhythm Management of Atrial Fibrillation: Survey Study

Background

Despite the numerous studies evaluating various rhythm control strategies for atrial fibrillation (AF), determination of the optimal strategy in a single patient is often based on trial and error, with no one-size-fits-all approach based on international guidelines/recommendations. The decision, therefore, remains personal and lends itself well to help from a clinical decision support system, specifically one guided by artificial intelligence (AI). QRhythm utilizes a 2-stage machine learning (ML) model to identify the optimal rhythm management strategy in a given patient based on a set of clinical factors, in which the model first uses supervised learning to predict the actions of an expert clinician and identifies the best strategy through reinforcement learning to obtain the best clinical outcome—a composite of symptomatic recurrence, hospitalization, and stroke.

Objective

We qualitatively evaluated a novel, AI-based, clinical decision support system (CDSS) for AF rhythm management, called QRhythm, which uses both supervised and reinforcement learning to recommend either a rate control or one of 3 types of rhythm control strategies—external cardioversion, antiarrhythmic medication, or ablation—based on individual patient characteristics.

Methods

Thirty-three clinicians, including cardiology attendings and fellows and internal medicine attendings and residents, performed an assessment of QRhythm, followed by a survey to assess relative comfort with automated CDSS in rhythm management and to examine areas for future development.

Results

The 33 providers were surveyed with training levels ranging from resident to fellow to attending. Of the characteristics of the app surveyed, safety was most important to providers, with an average importance rating of 4.7 out of 5 (SD 0.72). This priority was followed by clinical integrity (a desire for the advice provided to make clinical sense; importance rating 4.5, SD 0.9), backward interpretability (transparency in the population used to create the algorithm; importance rating 4.3, SD 0.65), transparency of the algorithm (reasoning underlying the decisions made; importance rating 4.3, SD 0.88), and provider autonomy (the ability to challenge the decisions made by the model; importance rating 3.85, SD 0.83). Providers who used the app ranked the integrity of recommendations as their highest concern with ongoing clinical use of the model, followed by efficacy of the application and patient data security. Trust in the app varied; 1 (17%) provider responded that they somewhat disagreed with the statement, “I trust the recommendations provided by the QRhythm app,” 2 (33%) providers responded with neutrality to the statement, and 3 (50%) somewhat agreed with the statement.

Conclusions

Safety of ML applications was the highest priority of the providers surveyed, and trust of such models remains varied. Widespread clinical acceptance of ML in health care is dependent on how much providers trust the algorithms. Building this trust involves ensuring transparency and interpretability of the model.

Wearables in Sports Cardiology

The expanding array and adoption of consumer health wearables is creating a new dynamic to the patient-health-care provider relationship. Providers are increasingly tasked with integrating the biometric data collected from their patients into clinical care. Further, a growing body of evidence is supporting the provider-driven utility of wearables in the screening, diagnosis, and monitoring of cardiovascular disease. Here we highlight existing and emerging wearable health technologies and the potential applications for use within sports cardiology. We additionally highlight how wearables can advance the remote cardiovascular care of patients within the context of the COVID-19 pandemic. Finally, despite these promising advances, we acknowledge some of the significant challenges that remain before wearables can be routinely incorporated into clinical care.

Clinical Validation of Automated Corrected QT-Interval Measurements From a Single Lead Electrocardiogram Using a Novel Smartwatch

Introduction

The Withings Scanwatch (Withings SA, Issy les Moulineaux, France) offers automated analysis of the QTc. We aimed to compare automated QTc-measurements using a single lead ECG of a novel smartwatch (Withings Scanwatch, SW-ECG) with manual-measured QTc from a nearly simultaneously recorded 12-lead ECG.

Methods

We enrolled consecutive patients referred to a tertiary hospital for cardiac workup in a prospective, observational study. The QT-interval of the 12-lead ECG was manually interpreted by two blinded, independent cardiologists through the tangent-method. Bazett's formula was used to calculate QTc. Results were compared using the Bland-Altman method.

Results

A total of 317 patients (48% female, mean age 63 ± 17 years) were enrolled. HR-, QRS-, and QT-intervals were automatically calculated by the SW in 295 (93%), 249 (79%), and 177 patients (56%), respectively. Diagnostic accuracy of SW-ECG for detection of QTc-intervals ≥ 460 ms (women) and ≥ 440 ms (men) as quantified by the area under the curve was 0.91 and 0.89. The Bland-Altman analysis resulted in a bias of 6.6 ms [95% limit of agreement (LoA) -59 to 72 ms] comparing automated QTc-measurements (SW-ECG) with manual QTc-measurement (12-lead ECG). In 12 patients (6.9%) the difference between the two measurements was greater than the LoA.

Conclusion

In this clinical validation of a direct-to-consumer smartwatch we found fair to good agreement between automated-SW-ECG QTc-measurements and manual 12-lead-QTc measurements. The SW-ECG was able to automatically calculate QTc-intervals in one half of all assessed patients. Our work shows, that the automated algorithm of the SW-ECG needs improvement to be useful in a clinical setting.

Congenital Long QT Syndrome

Congenital long QT syndrome (LQTS) encompasses a group of heritable conditions that are associated with cardiac repolarization dysfunction. Since its initial description in 1957, our understanding of LQTS has increased dramatically. The prevalence of LQTS is estimated to be ∼1:2,000, with a slight female predominance. The diagnosis of LQTS is based on clinical, electrocardiogram, and genetic factors. Risk stratification of patients with LQTS aims to identify those who are at increased risk of cardiac arrest or sudden cardiac death. Factors including age, sex, QTc interval, and genetic background all contribute to current risk stratification paradigms. The management of LQTS involves conservative measures such as the avoidance of QT-prolonging drugs, pharmacologic measures with nonselective β-blockers, and interventional approaches such as device therapy or left cardiac sympathetic denervation. In general, most forms of exercise are considered safe in adequately treated patients, and implantable cardioverter-defibrillator therapy is reserved for those at the highest risk. This review summarizes our current understanding of LQTS and provides clinicians with a practical approach to diagnosis and management.

Patient-specific, re-engineered cardiomyocyte model confirms the circumstance-dependent arrhythmia risk associated with the African-specific common SCN5A polymorphism p.S1103Y: Implications for the increased sudden deaths observed in black individuals during the COVID-19 pandemic

BACKGROUND

During the early stages of the coronavirus disease 2019 (COVID-19) pandemic, a marked increase in sudden cardiac death (SCD) was observed. The p.S1103Y-SCN5A common variant, which is present in ∼8% of individuals of African descent, may be a circumstance-dependent, SCD-predisposing, proarrhythmic polymorphism in the setting of hypoxia-induced acidosis or QT-prolonging drug use.

OBJECTIVE

The purpose of this study was to ascertain the effects of acidosis and hydroxychloroquine (HCQ) on the action potential duration (APD) in a patient-specific induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of p.S1103Y-SCN5A.

METHODS

iPSC-CMs were generated from a 14-year-old p.S1103Y-SCN5A-positive African American male. The patient's variant-corrected iPSC-CMs (isogenic control [IC]) were generated using CRISPR/Cas9 technology. FluoVolt voltage-sensitive dye was used to assess APD90 values in p.S1103Y-SCN5A iPSC-CMs compared to IC before and after an acidotic state (pH 6.9) or 24 hours of treatment with 10 μM HCQ.

RESULTS

Under baseline conditions (pH 7.4), there was no difference in APD90 values of p.S1103Y-SCN5A vs IC iPSC-CMs (P = NS). In the setting of acidosis (pH 6.9), there was a significant increase in fold-change of APD90 in p.S1103Y-SCN5A iPSC-CMs compared to IC iPSC-CMs (P <.0001). Similarly, with 24-hour 10 μM HCQ treatment, the fold-change of APD90 was significantly higher in p.S1103Y-SCN5A iPSC-CMs compared to IC iPSC-CMs (P <.0001).

CONCLUSION

Although the African-specific p.S1103Y-SCN5A common variant had no effect on APD90 under baseline conditions, the physiological stress of either acidosis or HCQ treatment significantly prolonged APD90 in patient-specific, re-engineered heart cells.

State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint)

Background

Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals.

Objective

This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications.

Methods

The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches.

Results

We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models.

Conclusions

We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools

Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman's life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.

AI and the cardiologist: when mind, heart and machine unite

Artificial intelligence (AI) and deep learning has made much headway in the consumer and advertising sector, not only affecting how and what people purchase these days, but also affecting behaviour and cultural attitudes. It is poised to influence nearly every aspect of our being, and the field of cardiology is not an exception. This paper aims to brief the clinician on the advances in AI and machine learning in the field of cardiology, its applications, while also recognising the potential for future development in these two mammoth fields. With the advent of big data, new opportunities are emerging to build AI tools, with better accuracy, that will directly aid not only the clinician but also allow nations to provide better healthcare to its citizens.

Implementation of a fully remote randomized clinical trial with cardiac monitoring

Background

The coronavirus disease 2019 (COVID-19) pandemic has challenged researchers performing clinical trials to develop innovative approaches to mitigate infectious risk while maintaining rigorous safety monitoring.

Methods

In this report we describe the implementation of a novel exclusively remote randomized clinical trial (ClinicalTrials.gov NCT04354428) of hydroxychloroquine and azithromycin for the treatment of the SARS-CoV-2–mediated COVID-19 disease which included cardiovascular safety monitoring. All study activities were conducted remotely. Self-collected vital signs (temperature, respiratory rate, heart rate, and oxygen saturation) and electrocardiographic (ECG) measurements were transmitted digitally to investigators while mid-nasal swabs for SARS-CoV-2 testing were shipped. ECG collection relied on a consumer device (KardiaMobile 6L, AliveCor Inc.) that recorded and transmitted six-lead ECGs via participants’ internet-enabled devices to a central core laboratory, which measured and reported QTc intervals that were then used to monitor safety.

Results

Two hundred and thirty-one participants uploaded 3245 ECGs. Mean daily adherence to the ECG protocol was 85.2% and was similar to the survey and mid-nasal swab elements of the study. Adherence rates did not differ by age or sex assigned at birth and were high across all reported race and ethnicities. QTc prolongation meeting criteria for an adverse event occurred in 28 (12.1%) participants, with 2 occurring in the placebo group, 19 in the hydroxychloroquine group, and 7 in the hydroxychloroquine + azithromycin group.

Conclusions

Our report demonstrates that digital health technologies can be leveraged to conduct rigorous, safe, and entirely remote clinical trials.

Influence of baseline QTc on sotalol-induced prolongation of ventricular repolarization in men and women

The extent of sotalol-induced QTc prolongation on the electrocardiogram, is variable among subjects and influenced by sex. However, the influence of baseline QTc on the extent of drug-induced QTc prolongation remains unclear. This was studied around peak plasma concentration in a large cohort of 376 healthy male and 614 healthy female subjects who received 80 mg of sotalol orally. Baseline QTc was 379±16ms in men and 393±15ms in women (p<0.0001). The change in QTc from baseline was highly variable among both sexes and was greater in women than in men (26.5±13.2 vs.13.0±10.8ms; <0.0001). The slope of the regression line between QTc on sotalol and baseline QTc did not significantly differ from unity in men and in women indicating that the extent of QTc prolongation with sotalol was not influenced by baseline QTc. Assessing QTc after administration of an I_Kr blocker may be more important than measuring a baseline QTc.

Artificial intelligence and cardiology: Current status and perspective: Artificial Intelligence and Cardiology

The development of artificial intelligence (AI) began in the mid-20th century but has been rapidly accelerating in the past decade. Reflecting the development of digital health over the past few years, this trend is also seen in medicine. The field of cardiovascular medicine uses a wide variety and a large amount of biosignals, so there are many situations where AI can contribute. The development of AI is in progress for all aspects of the healthcare system, including the prevention, screening, and treatment of diseases and the prediction of the prognosis. AI is expected to be used to provide specialist-level medical care, even in a situation where medical resources are scarce. However, like other medical devices, the concept and mechanism of AI must be fully understood when used; otherwise, it may be used inappropriately, resulting in detriment to the patient. Therefore, it is important to understand what we need to know as a cardiologist handling AI. This review introduces the basics and principles of AI, then shows how far the current development of AI has come, and finally gives a brief introduction of how to start the AI development for those who want to develop their own AI.