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The technological evolution and widespread availability of wearables and handheld ECG devices capable of screening for atrial fibrillation (AF), and their promotion directly to consumers, has focused attention of health care professionals and patient organizations on consumer-led AF screening. In this Frontiers review, members of the AF-SCREEN International Collaboration provide a critical appraisal of this rapidly evolving field to increase awareness of the complexities and uncertainties surrounding consumer-led AF screening. Although there are numerous commercially available devices directly marketed to consumers for AF monitoring and identification of unrecognized AF, health care professional–led randomized controlled studies using multiple ECG recordings or continuous ECG monitoring to detect AF have failed to demonstrate a significant reduction in stroke. Although it remains uncertain if consumer-led AF screening reduces stroke, it could increase early diagnosis of AF and facilitate an integrated approach, including appropriate anticoagulation, rate or rhythm management, and risk factor modification to reduce complications. Companies marketing AF screening devices should report the accuracy and performance of their products in high- and low-risk populations and avoid claims about clinical outcomes unless improvement is demonstrated in randomized clinical trials. Generally, the diagnostic yield of AF screening increases with the number, duration, and temporal dispersion of screening sessions, but the prognostic importance may be less than for AF detected by single–time point screening, which is largely permanent, persistent, or high-burden paroxysmal AF. Consumer-initiated ECG recordings suggesting possible AF always require confirmation by a health care professional experienced in ECG reading, whereas suspicion of AF on the basis of photoplethysmography must be confirmed with an ECG. Consumer-led AF screening is unlikely to be cost-effective for stroke prevention in the predominantly young, early adopters of this technology. Studies in older people at higher stroke risk are required to demonstrate both effectiveness and cost-effectiveness. The direct interaction between companies and consumers creates new regulatory gaps in relation to data privacy and the registration of consumer apps and devices. Although several barriers for optimal use of consumer-led screening exist, results of large, ongoing trials, powered to detect clinical outcomes, are required before health care professionals should support widespread adoption of consumer-led AF screening.

Computerized electrocardiography (ECG) has been widely used and allows linkage to electronic medical records. The present study describes the development and clinical applications of an electronic cohort derived from a digital ECG database obtained by the Telehealth Network of Minas Gerais, Brazil, for the period 2010–2017, linked to the mortality data from the national information system, the Clinical Outcomes in Digital Electrocardiography (CODE) dataset. From 2,470,424 ECGs, 1,773,689 patients were identified. A total of 1,666,778 (94%) underwent a valid ECG recording for the period 2010 to 2017, with 1,558,421 patients over 16 years old, 40.2% were men, with a mean age of 51.7 [SD 17.6] years. During a mean follow-up of 3.7 years, the mortality rate was 3.3%. ECG abnormalities assessed were: atrial fibrillation (AF), right bundle branch block (RBBB), left bundle branch block (LBBB), atrioventricular block (AVB), and ventricular pre-excitation. Most ECG abnormalities (AF: Hazard ratio [HR] 2.10, 95% CI 2.03–2.17, RBBB: HR 1.32, 95%CI 1.27–1.36, LBBB: HR 1.69, 95% CI 1.62–1.76, first degree AVB: Relative survival [RS]: 0.76, 95% CI0.71–0.81, 2:1 AVB: RS 0.21 95% CI0.09–0.52, and RS 0.36, third degree AVB: 95% CI 0.26–0.49) were predictors of overall mortality, except for ventricular pre-excitation (HR 1.41, 95% CI 0.56–3.57) and Mobitz I AVB (RS 0.65, 95% CI 0.34–1.24). In conclusion, a large ECG database established by a telehealth network can be a useful tool for facilitating new advances in the fields of digital electrocardiography, clinical cardiology and cardiovascular epidemiology.

ObjectivesPersistently impaired culprit artery flow (MethodsIn T-TIME (trial of low-dose adjunctive alTeplase during primary PCI), patients ≤6 hours from onset of ST-elevation myocardial infarction (STEMI) were randomised to placebo, alteplase 10 mg or alteplase 20 mg, administered by infusion into the culprit artery, pre-stenting. In this prespecified, secondary analysis, coronary flow was assessed angiographically at the point immediately before drug administration. Microvascular obstruction, myocardial haemorrhage and infarct size were assessed by cardiovascular magnetic resonance (CMR) at 2–7 days and 3 months.ResultsTIMI flow was assessed after first treatment (balloon angioplasty/aspiration thrombectomy), immediately pre-drug administration, in 421 participants (mean age 61±10 years, 85% male) and was 3, 2 or 1 in 267, 134 and 19 participants respectively. In patients with TIMI flow ≤2 pre-drug, there was higher incidence of microvascular obstruction with alteplase (alteplase 20 mg (53.1%) and 10 mg (59.5%) combined versus placebo (34.1%); OR=2.47 (95% CI 1.16 to 5.22, p=0.018) interaction p=0.005) and higher incidence of myocardial haemorrhage (alteplase 20 mg (53.1%) and 10 mg (57.9%) combined vs placebo (27.5%); OR=3.26 (95% CI 1.44 to 7.36, p=0.004) interaction p=0.001). These effects were not observed in participants with TIMI 3 flow pre-drug. There were no interactions between TIMI flow pre-drug, alteplase and 3-month CMR findings.ConclusionIn patients with impaired culprit artery flow (Trial registration numberNCT02257294.

ObjectiveCardiovascular diseases (CVDs) are highly preventable non-communicable diseases. ECG is a potential tool for risk stratification with respect to CVD. Our aim was to evaluate ECG’s role in all-cause and cardiovascular mortality prediction.MethodsParticipants from the Brazilian Longitudinal Study of Adult Health, free of known CVD at baseline were included. A 12-lead ECG was obtained at baseline (2008–2010). Participants were followed up to 2018 by annual interviews. Deaths were independently reviewed. Cox as well as Fine and Grey multivariable regression models were applied to evaluate if the presence of any major electrocardiographic abnormality (MEA), defined according to the Minnesota Code system, would predict total and cardiovascular deaths. We also evaluated the Net Reclassification Index of adding MEA to the Systematic Coronary Risk Evaluation (SCORE).ResultsThe 13 428 participants (median age 51 years, 45% men) were followed up for 8±1 years. All-cause and cardiovascular mortality occurred in 2.8% and 1.2% of the population, respectively. Prevalent MEA was an independent predictor of overall (HR=2.3, 95% CI 1.7 to 2.9) and cardiovascular mortality (HR=4.6, 95% CI 3.0 to 7.0) after adjustments for age, race, education and traditional cardiovascular risk factors. Adding MEA to the SCORE resulted in 9% mis-reclassification in the non-event subgroup and 33% correct reclassification in those with a fatal cardiovascular event.ConclusionPresence of MEA was an independent predictor of overall and cardiovascular mortality. ECG may have a role in risk prediction of cardiovascular mortality in primary care.

Background Microvascular angina is caused by cardiac small vessel disease, and dysregulation of the endothelin system is implicated. The minor G allele of the non-coding single nucleotide polymorphism (SNP) rs9349379 enhances expression of the endothelin 1 gene in human vascular cells, increasing circulating concentrations of ET-1. The prevalence of this allele is higher in patients with ischemic heart disease. Zibotentan is a potent, selective inhibitor of the ETA receptor. We have identified zibotentan as a potential disease-modifying therapy for patients with microvascular angina. Methods We will assess the efficacy and safety of adjunctive treatment with oral zibotentan (10 mg daily) in patients with microvascular angina and assess whether rs9349379 (minor G allele; population prevalence ~36%) acts as a theragnostic biomarker of the response to treatment with zibotentan. The PRIZE trial is a prospective, randomized, double-blind, placebo-controlled, sequential cross-over trial. The study population will be enriched to ensure a G-allele frequency of 50% for the rs9349379 SNP. The participants will receive a single-blind placebo run-in followed by treatment with either 10 mg of zibotentan daily for 12 weeks then placebo for 12 weeks, or vice versa, in random order. The primary outcome is treadmill exercise duration using the Bruce protocol. The primary analysis will assess the within-subject difference in exercise duration following treatment with zibotentan versus placebo. Conclusion PRIZE invokes precision medicine in microvascular angina. Should our hypotheses be confirmed, this developmental trial will inform the rationale and design for undertaking a larger multicenter trial.

Background: The pathophysiology and trajectory of multiorgan involvement in post-COVID-19 syndrome is uncertain. Methods: A prospective, multicenter, longitudinal, cohort study involving post-COVID-19 patients enrolled in-hospital or early post-discharge (visit 1) and re-evaluated 28-60 days post-discharge (visit 2). Multisystem investigations included chest computed tomography with pulmonary and coronary angiography, cardiovascular and renal magnetic resonance imaging, digital electrocardiography, and multisystem biomarkers. The primary outcome was the adjudicated likelihood of myocarditis.Results: 161 patients (mean age 55 years, 43% female) and 27 controls with similar age, sex, ethnicity, and vascular risk factors were enrolled from 22 May 2020 to 2 July 2021 and had a primary outcome evaluation. Compared to controls, at 28-60 days post-discharge, patients with COVID-19 had persisting evidence of cardio-renal involvement, systemic inflammation, and hemostasis pathway activation. Myocarditis was adjudicated as being not likely (n=17; 10%), unlikely (n=56; 35%), probable (n=67; 42%) or very likely (n=21; 13%). Acute kidney injury (odds ratio, 95% confidence interval: 3.40 (1.13, 11.84); p=0.038) and low hemoglobin A1c (0.26 (0.07, 0.87); p=0.035) were multivariable associates of adjudicated myocarditis. During convalescence, compared to controls, COVID-19 was associated with worse health-related quality of life (EQ5D-5L) (pConclusions: The illness trajectory of COVID-19 includes persisting cardio-renal inflammation, lung damage and hemostasis activation. Adjudicated myocarditis occurred in one in eight hospitalized patients and was associated with impairments in health status, physical and psychological wellbeing during community convalescence. Public registration: ClinicalTrials.gov identifier is NCT04403607.

The QT interval is an electrocardiographic measure representing the sum of ventricular depolarization (QRS duration) and repolarization (JT interval). Abnormalities of the QT interval are associated with potentially fatal ventricular arrhythmia. We conducted genome-wide multi-ancestry analyses in >250,000 individuals and identified 177, 156 and 121 independent loci for QT, JT and QRS, respectively, including a male-specific X-chromosome locus. Using gene-based rare-variant methods, we identified associations with Mendelian disease genes. Enrichments were observed in established pathways for QT and JT, with new genes indicated in insulin-receptor signalling and cardiac energy metabolism. In contrast, connective tissue components and processes for cell growth and extracellular matrix interactions were significantly enriched for QRS. We demonstrate polygenic risk score associations with atrial fibrillation, conduction disease and sudden cardiac death. Prioritization of druggable genes highlighted potential therapeutic targets for arrhythmia. Together, these results substantially advance our understanding of the genetic architecture of ventricular depolarization and repolarization.

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Digital electrocardiographs are now widely available and a large number of digital electrocardiograms (ECGs) have been recorded and stored. The present study describes the development and clinical applications of a large database of such digital ECGs, namely the CODE (Clinical Outcomes in Digital Electrocardiology) study. ECGs obtained by the Telehealth Network of Minas Gerais, Brazil, from 2010 to 17, were organized in a structured database. A hierarchical free-text machine learning algorithm recognized specific ECG diagnoses from cardiologist reports. The Glasgow ECG Analysis Program provided Minnesota Codes and automatic diagnostic statements. The presence of a specific ECG abnormality was considered when both automatic and medical diagnosis were concordant; cases of discordance were decided using heuristisc rules and manual review. The ECG database was linked to the national mortality information system using probabilistic linkage methods. From 2,470,424 ECGs, 1,773,689 patients were identified. After excluding the ECGs with technical problems and patients16 years-old, 1,558,415 patients were studied. High performance measures were obtained using an end-to-end deep neural network trained to detect 6 types of ECG abnormalities, with F1 scores80% and specificity99% in an independent test dataset. We also evaluated the risk of mortality associated with the presence of atrial fibrillation (AF), which showed that AF was a strong predictor of cardiovascular mortality and mortality for all causes, with increased risk in women. In conclusion, a large database that comprises all ECGs performed by a large telehealth network can be useful for further developments in the field of digital electrocardiography, clinical cardiology and cardiovascular epidemiology.

Introduction We investigated improvement of electrocardiographic LVH detection by adding measures of adiposity and/or novel electrocardiographic measures. Left ventricular hypertrophy (LVH) is an important risk factor for adverse cardiovascular outcomes. Improvement of electrocardiographic criteria for LVH is desirable, since electrocardiography is widely used. Methods We included 1091 participants of the Netherlands Epidemiology of Obesity Study (NEO) who underwent cardiac magnetic resonance imaging (MRI). Performance of Sokolow–Lyon and Cornell voltage and product criteria was assessed. Stepwise regression analysis was performed with each conventional electrocardiographic criterion and age, sex, body mass index (BMI), waist circumference, and waist:hip ratio (p-entry 0.10). T-wave abnormalities or the spatial QRS-T angle (SA) were added to the improved models. Results The study population had a mean (SD) age of 56 (6) years, BMI of 26.1 (4.0) kg/m2 and 46% were men. MRI-LVH was present in 10% of participants. The c-statistic for Sokolow–Lyon voltage was 0.58, R2 was 0.02 and sensitivity at 90% specificity was 16%, for Sokolow–Lyon product this was 0.62, 0.02, and 21%, for Cornell voltage 0.65, 0.04, and 28% and for Cornell product 0.67, 0.04, and 25%. Best performing models were obtained by addition of both BMI and SA (Sokolow-Lyon voltage: c-statistic 0.74, R2 0.11, sensitivity of 41% at 90% specificity; Sokolow-Lyon product: 0.75, 0.12, 42%; Cornell voltage: c-statistic 0.70, R2 0.08, sensitivity of 38% at 90% specificity; Cornell product: c-statistic 0.72, R2 0.08, sensitivity of 44% at 90% specificity). Conclusions Electrocardiographic detection of LVH improved by adding BMI and SA to a model with conventional electrocardiographic criteria. This approach would require little extra effort and application in clinical practice is feasible. However, results should first be replicated in high-risk populations. Electronic supplementary material The online version of this article (10.1007/s40119-019-00151-9) contains supplementary material, which is available to authorized users.

IMPORTANCE Increased free thyroxine (FT4) and decreased thyrotropin are associated with increased risk of atrial fibrillation (AF) in observational studies, but direct involvement is unclear.OBJECTIVE To evaluate the potential direct involvement of thyroid traits on AF.DESIGN, SETTING. AND PARTICIPANTS Study-level mendelian randomization (MR) included 11 studies, and summary-level MR included 55 114 AF cases and 482 295 referents, all of European ancestry.EXPOSURES Genomewide significant variants were used as instruments for standardized FT4 and thyrotropin levels within the reference range, standardized triiodothyronine (FT3):FT4 ratio, hypothyroidism, standardized thyroid peroxidase antibody levels, and hyperthyroidism. Mendelian randomization used genetic risk scores in study-level analysis or individual single-nucleotide polymorphisms in 2-sample MR for the summary-level data.MAIN OUTCOMES AND MEASURES Prevalent and incident AF.RESULTS The study-level analysis included 7679 individuals with AF and 49 233 referents (mean age [standard error], 62 [3] years; 15 859 men [29.7%]). In study-level random-effects meta-analysis, the pooled hazard ratio of FT4 levels (nanograms per deciliter) for incident AF was 1.55 (95% CI, 1.09-2.20; P = .02; I-2= 76%) and the pooled odds ratio (OR) for prevalent AF was 2.80 (95% CI, 1.41-5.54; P = .003; I-2 = 64%) in multivariable-adjusted analyses. The FT4 genetic risk score was associated with an increase in FT(4 )by 0.082 SD (standard error, 0.007; P CONCLUSIONS AND RELEVANCE Genetically increased FT3:FT(4 )ratio and hyperthyroidism, but not FT4 within the reference range, were associated with increased AF, and increased thyrotropin within the reference range and hypothyroidism were associated with decreased AF, supporting a pathway involving the pituitary-thyroid-cardiac axis.

This paper provides a brief description of how computer programs are used to automatically interpret electrocardiograms (ECGs), and also provides a discussion regarding new opportunities. The algorithms that are typically used today in hospitals are knowledge engineered where a computer programmer manually writes computer code and logical statements which are then used to deduce a possible diagnosis. The computer programmer's code represents the criteria and knowledge that is used by clinicians when reading ECGs. This is in contrast to supervised machine learning (ML) approaches which use large, labelled ECG datasets to induct their own ‘rules’ to automatically classify ECGs. Although there are many ML techniques, deep neural networks are being increasingly explored as ECG classification algorithms when trained on large ECG datasets. Whilst this paper presents some of the pros and cons of each of these approaches, perhaps there are opportunities to develop hybridised algorithms that combine both knowledge and data driven techniques. In this paper, it is pointed out that open ECG data can dramatically influence what international ECG ML researchers focus on and that, ideally, open datasets could align with real world clinical challenges. In addition, some of the pitfalls and opportunities for ML with ECGs are outlined. A potential opportunity for the ECG community is to provide guidelines to researchers to help guide ECG ML practices. For example, whilst general ML guidelines exist, there is perhaps a need to recommend approaches for ‘stress testing’ and evaluating ML algorithms for ECG analysis, e.g. testing the algorithm with noisy ECGs and ECGs acquired using common lead and electrode misplacements. This paper provides a primer on ECG ML and discusses some of the key challenges and opportunities.

Background Experience with implantable loop recorders (ILRs) in Brugada syndrome (BrS) is limited. Objective The purpose of this study was to evaluate the indications and yield of ILR monitoring in a single-center BrS registry. Methods Demographic, clinical and follow-up data of BrS patients with ILR were collected. Results Of 415 BrS patients recruited consecutively, 50 (12%) received an ILR (58% male). Mean age at ILR implantation was 44 ± 15 years. Thirty-one (62%) had experienced syncopal or presyncopal episodes, and 23 (46%) had palpitations. During median follow-up of 28 months (range 1–68), actionable events were detected in 11 subjects (22%); 7 had recurrences of syncope/presyncope, with 4 showing defects in sinus node function or atrioventricular conduction. New supraventricular tachyarrhythmias were recorded in 6 subjects; a run of fast nonsustained ventricular tachycardia was detected in 1 patient. Patients implanted with an ILR were less likely to show a spontaneous type 1 pattern or depolarization electrocardiographic (ECG) abnormalities compared to those receiving a primary prevention implantable-cardioverter defibrillator. Age at implantation, gender, Shanghai score, and ECG parameters did not differ between subjects with and those without actionable events. ILR-related complications occurred in 3 cases (6%). Conclusion In a large cohort of BrS patients, continuous ILR monitoring yielded a diagnosis of tachy- or bradyarrhythmic episodes in 22% of cases. Recurrences of syncope were associated with bradyarrhythmic events. Use of ILR can be helpful in guiding the management of low-/intermediate-risk BrS patients and ascertaining the cause of unexplained syncope.

The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over recent decades, there has been a steady increase in the number of research studies that are using machine learning (ML) to read or interrogate ECG data.The aim of this study is to review the use of ML with ECG data using a time series approach.Papers that address the subject of ML and the ECG were identified by systematically searching databases that archive papers from January 1995 to October 2019. Time series analysis was used to study the changing popularity of the different types of ML algorithms that have been used with ECG data over the past two decades. Finally, a meta-analysis of how various ML techniques performed for various diagnostic classifications was also undertaken.A total of 757 papers was identified. Based on results, the use of ML with ECG data started to increase sharply (p lt; 0.001) from 2012. Healthcare applications, especially in heart abnormality classification, were the most common application of ML when using ECG data (p lt; 0.001). However, many new emerging applications include using ML and the ECG for biometrics and driver drowsiness. The support vector machine was the technique of choice for a decade. However, since 2018, deep learning has been trending upwards and is likely to be the leading technique in the coming few years. Despite the accuracy paradox, accuracy was the most frequently used metric in the studies reviewed, followed by sensitivity, specificity, F1 score and then AUC.Applying ML using ECG data has shown promise. Data scientists and physicians should collaborate to ensure that clinical knowledge is being applied appropriately and is informing the design of ML algorithms. Data scientists also need to consider knowledge guided feature engineering and the explicability of the ML algorithm as well as being transparent in the algorithm's performance to appropriately calibrate human-AI trust. Future work is required to enhance ML performance in ECG classification.

Background Microvascular obstruction commonly affects patients with acute ST-segment elevation myocardial infarction and is independently associated with adverse outcomes. Objective To determine whether or not a strategy involving low-dose intracoronary fibrinolytic therapy infused early after coronary reperfusion will reduce microvascular obstruction. Design This was a multicentre, randomised, double-blind, parallel-group, placebo-controlled, dose-ranging trial. Setting The trial took place at 11 hospitals in the UK between 17 March 2016 and 21 December 2017. Participants Patients with acute ST-segment elevation myocardial infarction and a symptom onset to reperfusion time of ≤ 6 hours were eligible for randomisation. Radial artery access was a requirement, and further angiographic criteria included a proximal-to-middle coronary artery occlusion or impaired coronary flow in the presence of a definite thrombus in the culprit coronary artery. Exclusion criteria included a functional coronary collateral supply to the infarct-related artery, any contraindication to fibrinolysis and lack of informed consent. Additional exclusion criteria for safety were (1) requirement for immunosuppressive drug therapy for ≤ 3 months and (2) treatment with an antimicrobial agent. Intervention A total of 440 participants were randomly assigned 1 : 1 : 1 to treatment with placebo (n = 151), 10 mg of alteplase (n = 144) or 20 mg of alteplase (n = 145) administered by manual infusion directly into the infarct-related coronary artery over 5–10 minutes. The intervention was scheduled to happen after reperfusion and before stent implantation. Outcomes The primary outcome was the amount of microvascular obstruction (percentage of left ventricular mass) demonstrated by contrast-enhanced cardiac magnetic resonance imaging at 2–7 days after enrolment. The primary analysis was the comparison between the 20 mg of alteplase group and the placebo group; if this comparison was not significant, the comparison of the 10 mg of alteplase group with the placebo group was considered as a secondary analysis. Sample size A total of 618 patients (minimum of 558 patients). Recruitment was halted on 21 December 2017 given that conditional power for the primary outcome based on a prespecified analysis of the first 267 randomised participants was Methods The primary outcome was compared between groups using a stratified Wilcoxon rank-sum test (van Elteren test), stratified by the location of the myocardial infarction. Results Among the 440 patients (mean age of 60.5 years; 15% women), the primary end point was measured in 396 (90%) patients, 17 (3.9%) withdrew, seven died and all other patients were followed up to 3 months. The amount (mean percentage of left ventricular mass) of microvascular obstruction was 2.3% versus 2.6% versus 3.5% in the placebo, 10 mg of alteplase and 20 mg of alteplase groups, respectively. In the primary analysis, microvascular obstruction did not differ between the 20 mg of alteplase group and the placebo group: 3.5% versus 2.3%, estimated difference 1.16% (95% confidence interval –0.08% to 2.41%; p = 0.32). In the secondary analysis, microvascular obstruction did not differ between the 10 mg of alteplase group and the placebo group: 2.6% versus 2.3%, estimated difference 0.29% (95% confidence interval –0.76% to 1.35%; p = 0.74). By 3 months, major adverse cardiac events (cardiac death, non-fatal myocardial infarction and unplanned hospitalisation for heart failure) had occurred in 15 (10.1%) patients in the placebo group, 18 (12.9%) in the 10 mg of alteplase group and 12 (8.2%) in the 20 mg of alteplase group. Conclusions Adjunctive low-dose intracoronary alteplase given during the primary percutaneous intervention did not reduce microvascular obstruction compared with placebo. Limitations Premature discontinuation of enrolment limited the power of the secondary and safety analyses. Future work Low-dose intracoronary alteplase or tenecteplase could be compared with placebo at the end of primary percutaneous coronary intervention in patients with an ischaemic time of Trial registration This trial is registered as ClinicalTrials.gov NCT02257294. Funding This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a Medical Research Council (MRC) and National Institute for Health Research (NIHR) partnership. This will be published in full in Efficacy and Mechanism Evaluation; Vol. 7, No. 5. See the NIHR Journals Library website for further project information.

This study aims to assess the prevalence of electrocardiographic abnormalities based on the Minnesota code from a large sample of 1.01 m adult Brazilian primary care patients and to evaluate its association with age, sex, and cardiovascular risk factors. A large electrocardiogram (ECG) database of such patients aged ≥18 years (mean age 52 ± 17 years-old with 60.2% female). was analyzed and the results were stratified by sex and age. The prevalence of abnormalities was more common in men and the elderly population. Major electrocardiographic abnormalities were observed in 20.2% of adults and 43.1% of elderly patients, and minor abnormalities were found in 46.0% 39.4%, respectively. For adult patients, the number and percentage of major abnormalities rise depending on the number of cardiovascular risk factors. For elderly patients, the correlation between these variables is not clear, and the percentage of major abnormalities remains stable independently of the number of risk factors.

Background COVID-19 is typically a primary respiratory illness with multisystem involvement. The prevalence and clinical significance of cardiovascular and multisystem involvement in COVID-19 remain unclear. Methods This is a prospective, observational, multicentre, longitudinal, cohort study with minimal selection criteria and a near-consecutive approach to screening. Patients who have received hospital care for COVID-19 will be enrolled within 28 days of discharge. Myocardial injury will be diagnosed according to the peak troponin I in relation to the upper reference limit (URL, 99th centile) (Abbott Architect troponin I assay; sex-specific URL, male: >34 ng/L; female: >16 ng/L). Multisystem, multimodality imaging will be undertaken during the convalescent phase at 28 days post-discharge (Visit 2). Imaging of the heart, lung, and kidneys will include multiparametric, stress perfusion, cardiovascular magnetic resonance imaging, and computed tomography coronary angiography. Health and well-being will be assessed in the longer term. The primary outcome is the proportion of patients with a diagnosis of myocardial inflammation. Conclusion CISCO-19 will provide detailed insights into cardiovascular and multisystem involvement of COVID-19. Our study will inform the rationale and design of novel therapeutic and management strategies for affected patients. Clinical trial registration ClinicalTrials.gov identifier NCT04403607.

The Fourth Universal Definition of Myocardial Infarction (FUDMI) focuses on the distinction between nonischemic myocardial injury and myocardial infarction (MI), along with the role of cardiovascular magnetic resonance, in order to define the etiology of myocardial injury. As a consequence, there is less emphasis on updating the parts of the definition concerning the electrocardiographic (ECG) changes related to MI. Evidence of myocardial ischemia is a prerequisite for the diagnosis of MI, and the ECG is the main available tool for (a) detecting acute ischemia, (b) triage, and (c) risk stratification upon presentation. This review focuses on multiple aspects of ECG interpretation that we firmly believe should be considered for incorporation in any future update to the Universal Definition of MI.

Background Early prehospital recognition of critical conditions such as ST‐segment–elevation myocardial infarction (STEMI) has prognostic relevance. Current international electrocardiographic STEMI thresholds are predominantly based on individuals of Western European descent. However, because of ethnic electrocardiographic variability both in health and disease, there is a need to reevaluate diagnostic ST‐segment elevation thresholds for different populations. We hypothesized that fulfillment of ST‐segment elevation thresholds of STEMI criteria (STE‐ECGs) in apparently healthy individuals is ethnicity dependent. Methods and Results HELIUS (Healthy Life in an Urban Setting) is a multiethnic cohort study including 10 783 apparently healthy subjects of 6 different ethnicities (African Surinamese, Dutch, Ghanaian, Moroccan, South Asian Surinamese, and Turkish). Prevalence of STE‐ECGs across ethnicities, sexes, and age groups was assessed with respect to the 2 international STEMI thresholds: sex and age specific versus sex specific. Mean prevalence of STE‐ECGs was 2.8% to 3.4% (age/sex‐specific and sex‐specific thresholds, respectively), although with large ethnicity‐dependent variability. Prevalences in Western European Dutch were 2.3% to 3.0%, but excessively higher in young (45% STE‐ECGs. Conclusions The accuracy of diagnostic tests partly relies on background prevalence in healthy individuals. In apparently healthy subjects, there is a highly variable ethnicity‐dependent prevalence of ECGs with ST‐segment elevations exceeding STEMI thresholds. This has potential consequences for STEMI evaluations in individuals who are not of Western European descent, putatively resulting in adverse outcomes with both over‐ and underdiagnosis of STEMI.

Background In the prehospital triage of patients presenting with symptoms suggestive of acute myocardial ischemia, reliable myocardial ischemia detection in the electrocardiogram (ECG) is pivotal. Due to large interindividual variability and overlap between ischemic and nonischemic ECG‐patterns, incorporation of a previous elective (reference) ECG may improve accuracy. The aim of the current study was to explore the potential value of serial ECG analysis using subtraction electrocardiography. Methods SUBTRACT is a multicenter retrospective observational study, including patients who were prehospitally evaluated for acute myocardial ischemia. For each patient, an elective previously recorded reference ECG was subtracted from the ambulance ECG. Patients were classified as myocardial ischemia cases or controls, based on the in‐hospital diagnosis. The diagnostic performance of subtraction electrocardiography was tested using logistic regression of 28 variables describing the differences between the reference and ambulance ECGs. The Uni‐G ECG Analysis Program was used for state‐of‐the‐art single‐ECG interpretation of the ambulance ECG. Results In 1,229 patients, the mean area‐under‐the‐curve of subtraction electrocardiography was 0.80 (95%CI: 0.77–0.82). The performance of our new method was comparable to single‐ECG analysis using the Uni‐G algorithm: sensitivities were 66% versus 67% (p‐value > .05), respectively; specificities were 80% versus 81% (p‐value > .05), respectively. Conclusions In our initial exploration, the diagnostic performance of subtraction electrocardiography for the detection of acute myocardial ischemia proved equal to that of state‐of‐the‐art automated single‐ECG analysis by the Uni‐G algorithm. Possibly, refinement of both algorithms, or even integration of the two, could surpass current electrocardiographic myocardial ischemia detection.

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice., A preliminary version of this work titled: "Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network " was presented in the Machine Learning for Health Workshop at NeurIPS 2018 and was made available under a different identifier: arXiv:1811.12194. The current version subsumes all previous versions