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

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice

Phosphorus Magnetic Resonance Spectroscopy (31P MRS) and cardiovascular disease: The importance of energy

Background and Objectives: The heart is the organ with the highest metabolic demand in the body, and it relies on high ATP turnover and efficient energy substrate utilisation in order to function normally. The derangement of myocardial energetics may lead to abnormalities in cardiac metabolism, which herald the symptoms of heart failure (HF). In addition, phosphorus magnetic resonance spectroscopy (31P MRS) is the only available non-invasive method that allows clinicians and researchers to evaluate the myocardial metabolic state in vivo. This review summarises the importance of myocardial energetics and provides a systematic review of all the available research studies utilising 31P MRS to evaluate patients with a range of cardiac pathologies. Materials and Methods: We have performed a systematic review of all available studies that used 31P MRS for the investigation of myocardial energetics in cardiovascular disease. Results: A systematic search of the Medline database, the Cochrane library, and Web of Science yielded 1092 results, out of which 62 studies were included in the systematic review. The 31P MRS has been used in numerous studies and has demonstrated that impaired myocardial energetics is often the beginning of pathological processes in several cardiac pathologies. Conclusions: The 31P MRS has become a valuable tool in the understanding of myocardial metabolic changes and their impact on the diagnosis, risk stratification, and prognosis of patients with cardiovascular diseases

Electrocardiographic features of immune checkpoint inhibitor-associated myocarditis.

Immune checkpoint inhibitors (ICIs) are associated with immune-related adverse events including myocarditis, whilst improving cancer-related outcomes. There is thus a clinical need to identify electrocardiographic manifestations of ICI-related myocarditis to guide clinical management. PubMed was searched for clinical studies and case reports describing electrocardiographic changes in patients with ICI-related myocarditis. A total of six clinical studies and 79 case reports were included. This revealed a range of presentations for patients on ICIs, including supraventricular arrhythmias, ventricular arrhythmias and heart block, and new changes of ST-T segment unrelated to coronary artery disease, ST-segment elevation or depression and T-wave abnormalities. Several patients showed low voltages in multiple leads and new onset Q-wave development. Patients with ICI-related myocarditis may develop new arrhythmia and ST-T changes, and infrequently low voltages in multiple leads. [Abstract copyright: Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

Contemporary role of cardiac magnetic resonance in the management of patients with suspected or known coronary artery disease

Cardiac magnetic resonance imaging (CMR) is a useful non-invasive radiation-free imaging modality for the management of patients with coronary artery disease (CAD). CMR cine imaging provides the “gold standard” assessment of ventricular function, late gadolinium enhancement (LGE) provides useful data for the diagnosis and extent of myocardial scar and viability, while stress imaging is an established technique for the detection of myocardial perfusion defects indicating ischemia. Beyond its role in the diagnosis of CAD, CMR allows accurate risk stratification of patients with established CAD. This review aims to summarize the data regarding the role of CMR in the contemporary management of patients with suspected or known coronary artery disease

Meta-analysis of T peak –T end and T peak –T end /QT ratio for risk stratification in congenital long QT syndrome

Background and objectives: Congenital long QT syndrome (LQTS) predisposes affected individuals to ventricular tachycardia/fibrillation (VF/VF), potentially resulting in sudden cardiac death. The Tpeak–Tend interval and the Tpeak–Tend/QT ratio, electrocardiographic markers of dispersion of ventricular repolarization, were proposed for risk stratification but their predictive values in LQTS have been controversial. A systematic review and meta-analysis was conducted to examine the value of Tpeak–Tend intervals and Tpeak–Tend/QT ratios in predicting arrhythmic and mortality outcomes in congenital LQTS. Method: PubMed and Embase databases were searched until 9th May 2017, identifying 199 studies. Results: Five studies on long QT syndrome were included in the final meta-analysis. Tpeak–Tend intervals were longer (mean difference [MD]: 13 ms, standard error [SE]: 4 ms, P = 0.002; I2 = 34%) in congenital LQTS patients with adverse events [syncope, ventricular arrhythmias or sudden cardiac death] compared to LQTS patients without such events. By contrast, Tpeak–Tend/QT ratios were not significantly different between the two groups (MD: 0.02, SE: 0.02, P = 0.26; I2 = 0%). Conclusion: This meta-analysis showed that Tpeak–Tend interval is significant higher in individuals who are at elevated risk of adverse events in congenital LQTS, offering incremental value for risk stratification

Comparing the performance of published risk scores in Brugada syndrome: a multi-center cohort study.

The management of Brugada Syndrome (BrS) patients at intermediate risk of arrhythmic events remains controversial. The present study evaluated the predictive performance of different risk scores in an Asian BrS population and its intermediate risk subgroup. This retrospective cohort study included consecutive patients diagnosed with BrS from January 1 , 1997 to June 20 , 2020 from Hong Kong. The primary outcome is sustained ventricular tachyarrhythmias. Two novel risk risk scores and seven machine learning-based models (random survival forest, Ada boost classifier, Gaussian naïve Bayes, light gradient boosting machine, random forest classifier, gradient boosting classifier and decision tree classifier) were developed. The area under the receiver operator characteristic (ROC) curve (AUC) [95% confidence intervals] was compared between the different models. This study included 548 consecutive BrS patients (7% female, age at diagnosis: 50±16 years, follow-up: 84±55 months). For the whole cohort, the score developed by Sieira et al. showed the best performance (AUC: 0.806 [0.747-0.865]). A novel risk score was developed using the Sieira score and additional variables significant on univariable Cox regression (AUC: 0.855 [0.808-0.901]). A simpler score based on non-invasive results only showed a statistically comparable AUC (0.784 [0.724-0.845]), improved using random survival forests (AUC: 0.942 [0.913-0.964]). For the intermediate risk subgroup (N=274), a gradient boosting classifier model showed the best performance (AUC: 0.814 [0.791-0.832]). A simple risk score based on clinical and electrocardiographic variables showed a good performance for predicting VT/VF, improved using machine learning. Abstract: The management of Brugada Syndrome (BrS) patients at intermediate risk of arrhythmic events remains controversial. This study evaluated the predictive performance of published risk scores in a cohort of BrS patients from Hong Kong (N=548) and its intermediate risk subgroup (N=274). A novel risk score developed by modifying the best performing existing score (by. Sieira et al.) showed an area under the curve of 0.855 and 0.760 for the whole BrS cohort and the intermediate risk subgroup, respectively. The performance of the different scores was significantly improved machine learning-based methods, such as random survival forests and gradient boosting classifier. [Abstract copyright: Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

Virtual medical research mentoring

Background Medical research is important for professional advancement, and mentoring is a key means by which students and early-career doctors can engage in research. Contrasting international research collaborations, research mentoring programmes are often geographically limited. As the COVID-19 pandemic has led to increased use of online technology for classes and conferences, a virtual, international approach to medical research mentoring may be valuable. Approach We hereby describe our experience at the Cardiovascular Analytics Group, a virtual international medical research mentoring group established in 2015. We make use of virtual platforms in multi-level mentoring with peer mentoring and emphasise active participation, early leadership, an open culture, accessible research support and a distributed research workflow. Evaluation With 63 active members from 14 different countries, the Group has been successful in training medical students and early-career medical graduates in academic medicine. Our members have led over 100 peer-reviewed publications of original research and reviews since 2015, winning 13 research prizes during this time. Implications Our accessible-distributed model of virtual international medical research collaboration and multi-level mentoring is viable and efficient and caters to the needs of contemporary healthcare. Others should consider building similar models to improve medical research mentoring globally

Association between sodium-glucose cotransporter-2 inhibitors and incident atrial fibrillation/atrial flutter in heart failure patients with reduced ejection fraction: a meta-analysis of randomized controlled trials

Atrial fibrillation (AF) and atrial flutter (AFL) are associated with adverse outcomes in patients with heart failure and reduced ejection fraction (HFrEF). We investigated the effects of sodium-glucose cotransporter-2 inhibitors (SGLT2i) on the incidence of AF and/or AFL in HFrEF patients. PubMed and linicalTrials.gov were systematically searched until March 2022 for randomized controlled trials (RCTs) that enrolled patients with HFrEF. A total of six RCTs with 9467 patients were included (N=4731 in the SGLT2i arms; N=4736 in the placebo arms). Compared to placebo, SGLT2i treatment was associated with a significant reduction in the risk of AF [relative risk (RR) 0.62, 95% confidence interval CI 0.44–0.86; P=0.005] and AF/AFL (RR 0.64, 95% CI 0.47–0.87; P=0.004). Subgroup analysis showed that empagliflozin use resulted in a significant reduction in the risk of AF (RR 0.55, 95% CI 0.34–0.89; P=0.01) and AF/AFL (RR 0.50, 95% CI 0.32–0.77; P=0.002). By contrast, dapagliflozin use was not associated with a significant reduction in the risk of AF (RR 0.69, 95% CI 0.43–1.11; P=0.12) or AF/AFL (RR 0.82, 95% CI 0.53–1.27; P=0.38). Additionally, a “shorter” duration (<1.5 years) of treatment with SGLT2i remained associated with a reduction in the risk of AF (<1.5 years; RR 0.58, 95% CI 0.36–0.91; P=0.02) and AF/AFL (<1.5 years; RR 0.52, 95% CI 0.34–0.80; P=0.003). In conclusion, SGLT2i therapy was associated with a signifcant reduction in the risk of AF and AF/AFL in patients with HFrEF. These results reinforce the value of using SGLT2i in this setting

Electrocardiographic features of immune checkpoint inhibitor-associated myocarditis

Immune checkpoint inhibitors (ICIs) are associated with immune-related adverse events including myocarditis, whilst improving cancer-related outcomes. There is thus a clinical need to identify electrocardiographic manifestations of ICI-related myocarditis to guide clinical management. PubMed was searched for clinical studies and case reports describing electrocardiographic changes in patients with ICI-related myocarditis. A total of six clinical studies and 79 case reports were included. This revealed a range of presentations for patients on ICIs, including supraventricular arrhythmias, ventricular arrhythmias and heart block, and new changes of ST-T segment unrelated to coronary artery disease, ST-segment elevation or depression and T-wave abnormalities. Several patients showed low voltages in multiple leads and new onset Q-wave development. Patients with ICI-related myocarditis may develop new arrhythmia and ST-T changes, and infrequently low voltages in multiple leads