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Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF. Expected final online publication date for the Annual Review of Medicine, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

One of the most critical and challenging skills is the distinction of wide complex tachycardias into ventricular tachycardia or supraventricular wide complex tachycardia. Prompt and accurate differentiation of wide complex tachycardias naturally influences short- and long-term management decisions and may directly affect patient outcomes. Currently, there are many useful electrocardiographic criteria and algorithms designed to distinguish ventricular tachycardia and supraventricular wide complex tachycardia accurately; however, no single approach guarantees diagnostic certainty. In this review, we offer an in-depth analysis of available methods to differentiate wide complex tachycardias by retrospectively examining its rich literature base – one that spans several decades.

Background Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification. Methods A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one “all‐inclusive” model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance. Results The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X‐lead QRS amplitude change, Y‐lead QRS amplitude change, and Z‐lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95). Conclusion Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.

With an ageing population, the growing prevalence of atrial fibrillation (AF) is becoming a public health crisis. In the USA alone, the prevalence of AF is expected to more than double over the next 50 years.1 This undoubtedly has important clinical implications given the morbidity, mortality and tremendous healthcare cost burden associated with AF.2 3 In addition to the increasing numbers of known AF cases, another issue looms: a subset of individuals with silent and subclinical AF coexists in whom capturing the arrhythmia on single standard 12-lead ECG or even extended non-invasive ambulatory ECG monitoring remains a challenge. In fact, the first clinical presentation of those with asymptomatic AF may be an acute ischaemic stroke or decompensated heart failure.4 Therefore, the ability to identify individuals at increased risk of AF can aid in screening, surveillance, management and prevention strategies. The current study by Sanz-Garcia and colleagues,5 represents an innovative approach to doing just that. Until recently, AF prediction has been based on clinical variables integrated into scoring systems such as CHARGE-AF (Cohorts for Aging and Research in Genomic Epidemiology–AF) score, which is the best studied and most well-established clinical AF scoring tool.6 Others include the FHS (Framingham Heart Study), ARIC (Atherosclerosis Risk in Communities) and CHA2DS2-VASc risk scores.6 7 These approaches, however, require time-intensive data abstraction of multiple variables—some …

Atrial fibrillation (AF) is known to affect at least 30 million people worldwide [1,2], although this may be an underestimation. AF can be asymptomatic and fleeting and often goes undetected. In fa...

The prevalence of atrial fibrillation (AF) continues to grow in an aging population, and its impact on both patients and the health care system has has made it a global burden. There are limited available options to detect individuals at risk of AF that may benefit from prevention and treatment strategies. The ECG may be an effective tool do so. In this work, we discuss the latest work by Hayiroglu and colleagues related to this work and the use of novel ECG prediction tools to identify individuals individuals that could benefit from early and proactive screening, surveillance, and management strategies.

It is hard to imagine something more central to the practice of medicine than the performance and interpretation of the ECG. This simple, non-invasive, and ubiquitous test is essential for the diagnosis of a wide range of medical conditions, across a broad range of medical settings—the emergency room, primary care office, intensive care unit and beyond. For nearly 100 years, this test has held up as a fundamental pillar of medical practice. Unfortunately, proficiency in ECG interpretation is not a universal skill, and the barriers in achieving ECG fluency are rooted at multiple levels of medical curricula. To aid in solving the problem of declining ECG competency, clearly defined skill competencies for all healthcare professionals are needed. Once competency expectations are set, centralised educational resources may be used to achieve these benchmarks. The medical education community has been the driver to spearhead such societal initiatives and will need to be integral in the rejuvenation of proper ECG interpretation. In this context, medical educators ought to inspire the next generation as well as current providers to revisit and reinvigorate the complexities and beauty of ECG interpretation. The ECG is one of the most powerful diagnostic tools in modern medicine. As a graphical display of the heart’s electrical activity, the ECG communicates unique and vital information regarding the electrophysiological properties, anatomical structure, myocardial tissue characteristics, haemodynamic condition, and enveloping metabolic milieu of a patient’s beating heart. The ECG is one of the most used diagnostic tests, with an estimated 300 million ECGs performed annually in the USA alone. More recently, the application of artificial intelligence to the ECG has unlocked new promising functions, such as predicting a patient’s chronological age and sex,1 likelihood of developing atrial fibrillation,2 and underlying cardiac function.3 With new smartphone-based electrodes and the potential to serve …

The accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) remains problematic despite numerous manually-operated electrocardiogram (ECG) interpretation methods. We sought to create a new WCT differentiation method that could be automatically implemented by computerized ECG interpretation (CEI) software.In a two-part study, we developed and validated a logistic regression model (i.e. WCT Formula) that utilizes computerized measurements and computations derived from patients' paired WCT and subsequent baseline ECGs. In Part 1, a derivation cohort of paired WCT and baseline ECGs was examined to identify independent VT predictors to be incorporated into the WCT Formula. In Part 2, a separate validation cohort of paired WCT and baseline ECGs was used to prospectively evaluate the WCT Formula's diagnostic performance.The derivation cohort was comprised of 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. A logistic regression model (i.e. WCT Formula) incorporating WCT QRS duration (ms) (p 0.001), frontal percent amplitude change (%) (p 0.001), and horizontal percent amplitude change (%) (p 0.001) yielded effective WCT differentiation (AUC of 0.96). The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. The WCT Formula achieved favorable accuracy (91.5%) with strong sensitivity (89.7%) and specificity (92.9%) for VT.The WCT Formula is an example of how contemporary CEI software could be used to successfully differentiate WCTs. The incorporation of similar automated methods into CEI software may improve clinicians' ability to accurately distinguish VT and SWCT.

Background An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram [TTE]) in cardiac intensive care unit (CICU) patients. Method We included unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG and TTE within 7 days, at least one of which was during hospitalization. Discrimination of the AI-ECG for LVSD was determined using receiver-operator characteristic curve (AUC) values. Results We included 5680 patients with a mean age of 68 ± 15 years (37% females). Acute coronary syndrome (ACS) was present in 55%. LVSD was present in 34% of patients (mean LVEF 48 ± 16%). The AI-ECG had an AUC of 0.83 (95% confidence interval 0.82–0.84) for discrimination of LVSD. Using the optimal cut-off, the AI-ECG had 73%, specificity 78%, negative predictive value 85% and overall accuracy 76% for LVSD. AUC values were higher for patients aged Conclusions The AI-ECG algorithm had very good discrimination for LVSD in this critically-ill CICU cohort with a high prevalence of LVSD. Performance was better in younger male patients and those without ACS, highlighting those CICU patients in whom screening for LVSD using AI ECG may be more effective. The AI-ECG might potentially be useful for identification of LVSD in resource-limited settings when TTE is unavailable.

The presence of left ventricular systolic dysfunction (LVSD) alters clinical management and prognosis in most acute and chronic cardiovascular conditions. While transthoracic echocardiography (TTE) remains the most common diagnostic tool to screen for LVSD, it is operator-dependent, time-consuming, effort-intensive, and relatively expensive. Recent work has demonstrated the ability of an artificial intelligence-augment ECG (AI-ECG) model to accurately predict LVSD in critical intensive care unit (CICU) patients. We demonstrate that the AI-ECG algorithm can maintain its performance in these patients with and without AF despite their clinical differences. An AI-ECG algorithm can serve as a non-invasive, inexpensive, and rapid screening tool for early detection of LVSD in resource-limited settings, and potentially expedite clinical decision making and guideline-directed therapies in the acute care setting.

Objective To validate an artificial intelligence–augmented electrocardiogram (AI-ECG) algorithm for the detection of preclinical left ventricular systolic dysfunction (LVSD) in a large community-based cohort. Methods We identified a randomly selected community-based cohort of 2041 subjects age 45 years or older in Olmsted County, Minnesota. All participants underwent a study echocardiogram and ECG. We first assessed the performance of the AI-ECG to identify LVSD (ejection fraction ≤40%). After excluding participants with clinical heart failure, we further assessed the AI-ECG to detect preclinical LVSD among all patients (n=1996) and in a high-risk subgroup (n=1348). Next we modelled an imputed screening program for preclinical LVSD detection where a positive AI-ECG triggered an echocardiogram. Finally, we assessed the ability of the AI-ECG to predict future LVSD. Participants were enrolled between January 1, 1997, and September 30, 2000; and LVSD surveillance was performed for 10 years after enrollment. Results For detection of LVSD in the total population (prevalence, 2.0%), the area under the receiver operating curve for AI-ECG was 0.97 (sensitivity, 90%; specificity, 92%); in the high-risk subgroup (prevalence 2.7%), the area under the curve was 0.97 (sensitivity, 92%; specificity, 93%). In an imputed screening program, identification of one preclinical LSVD case would require 88.3 AI-ECGs and 8.7 echocardiograms in the total population and 65.7 AI-ECGs and 5.5 echocardiograms in the high-risk subgroup. The unadjusted hazard ratio for a positive AI-ECG for incident LVSD over 10 years was 2.31 (95% CI, 1.32 to 4.05; P=.004). Conclusion Artificial intelligence–augmented ECG can identify preclinical LVSD in the community and warrants further study as a screening tool for preclinical LVSD.

IMPORTANCE: Outcomes of postoperative atrial fibrillation (AF) after noncardiac surgery are not well defined. OBJECTIVE: To determine the association of new-onset postoperative AF vs no AF after noncardiac surgery with risk of nonfatal and fatal outcomes. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study in Olmsted County, Minnesota, involving 550 patients who had their first-ever documented AF within 30 days after undergoing a noncardiac surgery (postoperative AF) between 2000 and 2013. Of these patients, 452 were matched 1:1 on age, sex, year of surgery, and type of surgery to patients with noncardiac surgery who were not diagnosed with AF within 30 days following the surgery (no AF). The last date of follow-up was December 31, 2018. EXPOSURES: Postoperative AF vs no AF after noncardiac surgery. MAIN OUTCOMES AND MEASURES: The primary outcome was ischemic stroke or transient ischemic attack (TIA). Secondary outcomes included subsequent documented AF, all-cause mortality, and cardiovascular mortality. RESULTS: The median age of the 452 matched patients was 75 years (IQR, 67-82 years) and 51.8% of patients were men. Patients with postoperative AF had significantly higher CHA(2)DS(2)-VASc scores than those in the no AF group (median, 4 [IQR, 2-5] vs 3 [IQR, 2-5]; P

Aims An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD by transthoracic echocardiography (TTE). Methods and results We included 11 266 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who underwent AI-ECG after CICU admission. Left ventricular ejection fraction (LVEF) data were extracted for patients with a TTE during hospitalization. Hospital mortality was analysed using multivariable logistic regression. Mean age was 68 ± 15 years, including 37% females. Higher AI-ECG probability of LVSD remained associated with higher hospital mortality [adjusted odds ratio (OR) 1.05 per 0.1 higher, 95% confidence interval (CI) 1.02–1.08, P = 0.003] after adjustment for LVEF, which itself was inversely related with the risk of hospital mortality (adjusted OR 0.96 per 5% higher, 95% CI 0.93–0.99, P = 0.02). Patients with available LVEF data (n = 8242) were divided based on the presence of predicted (by AI-ECG) vs. observed (by TTE) LVSD (defined as LVEF ≤ 35%), using TTE as the gold standard. A stepwise increase in hospital mortality was observed for patients with a true negative, false positive, false negative, and true positive AI-ECG. Conclusion The AI-ECG prediction of LVSD is associated with hospital mortality in CICU patients, affording risk stratification in addition to that provided by echocardiographic LVEF. Our results emphasize the prognostic value of electrocardiographic patterns reflecting underlying myocardial disease that are recognized by the AI-ECG.

Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction 800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

To (i) review the concept of artificial intelligence (AI); (ii) summarize recent developments in artificial intelligence-enabled electrocardiogram (AI-ECG); (iii) address notable inherent limitations and challenges of AI-ECG; and (iv) discuss the future direction of the field. Advancements in machine learning and computing methods have led to application of AI-ECG and potential new applications to patient care. Further study is needed to verify previous findings in diverse populations as well as begin to confront the limitations needed for clinical implementation. Nearly one century after the Nobel Prize was awarded to Willem Einthoven for demonstrating that an electrocardiogram (ECG) could record the electrical signature of the heart, the ECG remains one of the most important diagnostic tests in modern medicine. We now stand at the edge of true ECG innovation. Simultaneous advancements in computing power, wireless technology, digitized data availability, and machine learning have led to the birth of AI-ECG algorithms with novel capabilities and real potential for clinical application. AI has the potential to improve diagnostic accuracy and efficiency by providing fully automated, unbiased, and unambiguous ECG analysis along with promising new findings that may unlock new value in the ECG. These breakthroughs may cause a paradigm shift in clinical workflow as well as patient monitoring and management.

The primary goal of the initial ECG evaluation of every wide complex tachycardia is to determine whether the tachyarrhythmia has a ventricular or supraventricular origin. The answer to this question drives immediate patient care decisions, ensuing clinical workup, and long‐term management strategies. Thus, the importance of arriving at the correct diagnosis cannot be understated and has naturally spurred rigorous research, which has brought forth an ever‐expanding abundance of manually applied and automated methods to differentiate wide complex tachycardias. In this review, we provide an in‐depth analysis of traditional and more contemporary methods to differentiate ventricular tachycardia and supraventricular wide complex tachycardia. In doing so, we: (1) review hallmark wide complex tachycardia differentiation criteria, (2) examine the conceptual and structural design of standard wide complex tachycardia differentiation methods, (3) discuss practical limitations of manually applied ECG interpretation approaches, and (4) highlight recently formulated methods designed to differentiate ventricular tachycardia and supraventricular wide complex tachycardia automatically.

Early recognition of ST-segment elevation myocardial infarction equivalent electrocardiogram patterns is of paramount importance. Successful identification of these ischemic patterns helps ensure proper triage of patients needing urgent restoration of coronary perfusion. The so-called de Winter sign has become increasingly recognized as a ST-segment elevation myocardial infarction equivalent pattern due to proximal left anterior descending artery occlusion. Yet, despite the de Winter pattern's well-defined electrocardiographic-angiographic relationship, the electrophysiologic explanation for its characteristic electrocardiographic manifestations remains unclear. Herein, we report a case in which an ischemic lateral lead variant of the de Winter pattern emerged from a patient inflicted by an abrupt thrombotic occlusion of the ostial left anterior descending artery, which developed in series with a high-grade stenosis of the distal left main coronary artery. We examine the patient's presenting electrocardiographic findings and clinical course to (i) establish causal inferences that align with the distribution of myocardial ischemia supported by coronary angiography and (ii) provide an accompanying analysis of the relevant scientific literature.

In a recent publication [1], we introduced and described a novel means (i.e. VT Prediction Model) to correctly categorize wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) using routine measurements shown on electrocardiogram (ECG) paper recordings. In this article, we summarize data components relating to the derivation and validation of the VT Prediction Model.

Differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional manually-operated electrocardiogram (ECG) interpretation methods is difficult. Recent research has shown that accurate WCT differentiation may be accomplished by automated approaches (e.g., WCT Formula) implemented by computerized ECG interpretation software.We sought to develop a new automated means to differentiate WCTs.First, a derivation cohort of paired WCT and baseline ECGs was examined to secure independent VT predictors to be incorporated into a logistic regression model (i.e., WCT Formula II). Second, the WCT Formula II was trialed against a separate validation cohort of paired WCT and baseline ECGs.The derivation cohort comprised 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. The WCT Formula II was composed of baseline QRS duration (p = 0.02), WCT QRS duration (p 0.001), frontal percent time-voltage area change (p 0.001), and horizontal percent time-voltage area change (p 0.001). The area under the curve (AUC) for VT and SWCT differentiation was 0.96 (95% CI 0.94-0.98) for the derivation cohort. The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. WCT Formula II implementation on the validation cohort yielded effective WCT differentiation (AUC 0.96; 95% CI 0.94-0.98).The WCT Formula II is an example of how contemporary ECG interpretation software could be used to differentiate WCTs successfully.

Despite many technological advances in the field of cardiology, accurate differentiation of wide complex tachycardias into ventricular tachycardia or supraventricular wide complex tachycardia continues to be challenging. After decades of rigorous clinical research, a wide variety of electrocardiographic criteria and algorithms have been developed to provide an accurate means to distinguish these two entities as accurately as possible. Recently, promising automated differentiation methods that utilize computerized electrocardiographic interpretation software have emerged. In this review, we aim to (1) highlight the clinical importance of accurate wide complex tachycardia differentiation, (2) provide an overview of the conventional manually-applied differentiation algorithms, and (3) describe novel automated approaches to differentiate wide complex tachycardia.

Introduction: Paroxysmal supraventricular tachycardia (SVT) can be very bothersome and may potentially lead to considerable health-care utilization. Non-parenteral medication is currently unavailable for the rapid termination of paroxysmal SVT. However, an intranasal spray formulation of etripamil, a short-acting calcium-channel blocker, is under investigation as a convenient, safe, and rapidly efficacious means to terminate paroxysmal SVT.Areas covered: This review summarizes the clinical rationale, potential benefit, and clinical trials safety and efficacy data for the use of etripamil nasal spray to terminate paroxysmal SVT.Expert opinion: Based on the efficacy and tolerability demonstrated in phase 1 and 2 clinical trials, etripamil nasal spray is a potential convenient, safe, and effective means for patients to terminate paroxysmal SVT. It has the potential to improve quality of life, reduce health-care burden, and alter the current management paradigm for many patients with SVT. Further ongoing evaluation in ambulatory patients will help to determine its real-life practicality, safety, and effectiveness.

Objectives The objective of this clinical trial was to evaluate the long-term effect of endothelin receptor antagonism with bosentan on the morbidity and mortality of patients with severe chronic heart failure. Background Endothelin may play a role in heart failure, but short-term clinical trials with endothelin receptor antagonists have reported disappointing results. Long-term trials are lacking. Methods In 2 identical double-blind trials, we randomly assigned 1,613 patients with New York Heart Association functional class IIIb to IV heart failure and an ejection fraction Results Bosentan did not influence clinical status at 9 months in either trial (p = 0.928 and p = 0.263). In addition, 321 patients in the placebo group and 312 patients in the bosentan group died or were hospitalized for heart failure (hazard ratio [HR]: 1.01; 95% confidence interval [CI]: 0.86 to 1.18; p = 0.90). The bosentan group experienced fluid retention within the first 2 to 4 weeks, as evidenced by increased peripheral edema, weight gain, decreases in hemoglobin, and an increased risk of hospitalization for heart failure, despite intensification of background diuretics. During follow-up, 173 patients died in the placebo group and 160 patients died in the bosentan group (HR: 0.94; 95% CI: 0.75 to 1.16). About 10% of the bosentan group showed meaningful increases in hepatic transaminases, but none had acute or chronic liver failure. Conclusions Bosentan did not improve the clinical course or natural history of patients with severe chronic heart failure and but caused early and important fluid retention.

BACKGROUND The accurate separation of undifferentiated wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional, manually-applied 12-lead electrocardiogram (ECG) interpretation methods is difficult. PURPOSE We sought to devise a new WCT differentiation method that operates solely on automated measurements routinely provided by computerized ECG interpretation software. METHODS In a two-part analysis, we developed and validated a logistic regression model (ie, VT Prediction Model) that utilizes routinely available computerized measurements derived from patients' paired WCT and baseline ECGs. RESULTS The derivation cohort consisted of 601 paired WCT (273 VT, 328 SWCT) and baseline ECGs from 421 patients. The VT Prediction Model, composed of WCT QRS duration (ms) (P

The standard 12‑lead electrocardiogram (ECG) has become a mainstay diagnostic tool in patients suspected to have myocardial ischemia. The identification of hallmark electrocardiographic abnormalities, such as ST-segment deviation or serial T wave changes, not only helps identify the presence of myocardial ischemia but also may help localize myocardial territories with an ongoing injury. Widespread ST-segment depression is commonly attributed to diffuse subendocardial ischemia precipitated by severe multivessel or left main coronary artery disease. However, among patients with prior coronary revascularization, clear electrocardiographic-angiographic relationships responsible for widespread ST-segment depressions have not been well defined. We report a case in which diffuse ST-segment depression emerged from a patient with prior coronary artery bypass grafting. In this report, we examine the patient's presenting ECG pattern as to (1) establish causal inferences which align with the distribution of myocardial ischemia supported by angiography and (2) provide an accompanying analysis of the relevant scientific literature.

Automated computerized electrocardiogram (ECG) interpretation algorithms are designed to enhance physician ECG interpretation, minimize medical error, and expedite clinical workflow. However, the performance of current computer algorithms is notoriously inconsistent. We aimed to develop and validate

Left ventricular apical aneurysm (LVAA) in hypertrophic cardiomyopathy (HCM) is a rare subset of this disease. It is associated with regional scarring/fibrosis, a higher risk for arrhythmic sudden death, and thromboembolic events. Surgical resection is often considered in severely symptomatic

Accurate WCT differentiation by means of 12-lead ECG interpretation remains fundamental to patient care. Despite the many manual ECG criteria available, accurate WCT differentiation remains a challenging. We sought to create a WCT differentiation method from mathematically-synthesized

Assessment of acute dyspnea in the emergency department (ED) is challenging. Performance of a point of care ultrasound in the ER improves diagnostic precision, but is operator dependent and requires training. Our goal was to utilize an artificial intelligence (AI)-enabled ECG to identify patients

Lyme carditis is an uncommon presentation of the early-disseminated phase of Lyme disease, although it is recognizable and often curable. Because of its rarity, diagnosing Lyme carditis requires a high level of suspicion, especially when young patients in certain endemic areas present with symptoms of bradycardia and/or evidence of high-degree atrioventricular (AV) block. Temporary cardiac pacing along with antibiotic therapy has been shown to aid in the management of Lyme carditis until symptoms and conduction blocks have resolved. Herein, we report two cases of Lyme carditis-induced AV block that were successfully managed and reversed with temporary cardiac pacing and antibiotics. In order to monitor for any late sequela that may arise, we also recommend close follow-up for patients treated for Lyme carditis with high-degree AV block. .

Summary Background Functional magnetic resonance imaging (fMRI) has been used to evaluate the long-term consequences of early exposure to neurotoxic agents. fMRI shows that different patterns of brain activation occur in ethanol-exposed subjects performing a go/no-go response inhibition task. Pharmacologically, ethanol and general anesthetics have similar receptor-level activity in the brain. This study utilizes fMRI to examine brain activation patterns in children exposed to general anesthesia and surgery during early brain development. Methods After obtaining Nationwide Children's Hospital IRB approval, a surgical database was utilized to identify children aged 10–17 years with a history of at least 1 h of exposure to general anesthetics and surgery when they were between 0 and 24 months of age. Age- and gender-matched children without anesthesia exposure were recruited as a control group. All subjects were scanned while being presented with a go/no-go response inhibition task. Reaction time and accuracy data were acquired, and the blood-oxygen-level-dependent (BOLD) fMRI signal was measured as a biomarker for regional neuronal activity. Results There were no differences in terms of performance accuracy and response time. The analysis did not reveal any significant activation differences in the primary region of interest (prefrontal cortex and caudate nucleus); however, activation differences were seen in other structures, including the cerebellum, cingulate gyrus, and paracentral lobule. Conclusions Early anesthetic exposure and surgery did not affect accuracy, response time, or activation patterns in the primary region of interest during performance of the task. Intergroup differences in activation patterns in other areas of the brain were observed, and the significance of these findings is unknown. fMRI appears to be a useful tool in evaluating the long-term effects of early exposure to general anesthesia.

Over the last two decades and more, the American Center for Reproductive Medicine (ACRM) at Cleveland Clinic has made great contributions to medicine that have helped in deepening our understanding of oxidative stress (OS) and its effects on human reproduction, particularly in the male. In this chapter, we will explore the findings of past research conducted by the Cleveland Clinic researchers to date, which spans from reactive oxygen species (ROS), OS and its measurement, effects of OS on sperm preparation and cryopreservation during assisted reproduction procedures as well as on semen parameters, the physiological and pathophysiological effects (both direct and indirect) of endogenous and exogenous ROS, the role of antioxidants in reversing the effects of oxidants and finally, the use of proteomics and bioinformatics tools in discovering the biological processes and pathways underlying oxidative stress-induced infertility.

A recent publication (May et al., 2019) introduced a novel means (i.e. WCT Formula) to automatically distinguish ventricular tachycardia and supraventricular wide complex tachycardia using modern-day computerized electrocardiogram software measurements. In this article, a summary of data components relating to the derivation and validation of the WCT Formula is presented.

Introduction The usefulness of qualitative or quantitative volumetric magnetic resonance imaging (MRI) in early detection of brain structural changes and prediction of adverse outcomes in neonatal illnesses warrants further investigation. Our aim was to correlate certain brain injuries and the brain volume of feeding-related cortical and subcortical regions with feeding method at discharge among preterm dysphagic infants. Materials and methods Using a retrospective observational study design, we examined MRI data among 43 (22 male; born at 31.5 ± 0.8 week gestation) infants who went home on oral feeding or gastrostomy feeding (G-tube). MRI scans were segmented, and volumes of brainstem, cerebellum, cerebrum, basal ganglia, thalamus, and vermis were quantified, and correlations were made with discharge feeding outcomes. Chi-squared tests were used to evaluate MRI findings vs. feeding outcomes. ANCOVA was performed on the regression model to measure the association of maturity and brain volume between groups. Results Out of 43 infants, 44% were oral-fed and 56% were G-tube fed at hospital discharge (but not at time of the study). There was no relationship between qualitative brain lesions and feeding outcomes. Volumetric analysis revealed that cerebellum was greater (p

Purpose: To identify the neural circuitry of idiopathic infantile nystagmus syndrome (INS), characterized by an early onset alternating series of slow and rapid eye movements that can manifest in different waveforms and genetic lines. The neural circuitry of INS is currently unknown. Methods: A novel functional magnetic resonance imaging (fMRI) method, referred to as the null zone fMRI technique, was used to identify the neural circuitry for INS. In the null zone fMRI technique, a gaze position with minimal nystagmus within the null zone was linked to the fMRI “off” condition and a gaze position with robust nystagmus outside of the null zone was linked to the fMRI “on” condition. Eye movements were monitored with an fMRI compatible eye tracker and observed in real time to ensure subject compliance in “on” and “off” states. Subjects with INS (n = 4) included three family members (a mother and two daughters) with presumed autosomal dominant INS, as well as age- and gender-matched normal controls (n = 3). Results: Three of four subjects with INS demonstrated significant increased activation of the declive of the cerebellum, whereas no normal subjects under identical conditions showed activation of the declive of the cerebellum. Both groups showed significant activation in the occipital lobe (Brodmann areas 17, 18, 19, and cuneus). Conclusion: A novel fMRI method demonstrated that the declive of the cerebellum is actively involved in INS. These are the first results to identify the cerebellum, and specifically the declive, as a possible site involved in the ocular motor dysfunction known as INS.

When apoptosis is improperly activated or regulated in the testis, infertility or even cancer can result. Studies have implicated elevated rates of apoptosis in infertile male patients. Pinpointing how environmental toxicity affects apoptosis is important for the advancement of preventative medicine and behavior, especially as potentially harmful compounds continue to proliferate in households and workplaces. Moreover, familiarity with testicular processes, particularly the induction of apoptosis, is essential for promoting male fertility. This review examines environmental toxicants that have been implicated in testicular apoptosis. We elucidate the mechanistic pathways through which specific xenobiotic compounds trigger cell death in the testis. This review highlights the role of oxidative stress in mediating these apoptotic actions.

Human immunodeficiency virus (HIV) has predominantly been considered the main cause in the progression of acquired immunodeficiency syndrome (AIDS). However, many researchers believe that there are co-factors involved along the way that may play a critical role in its development. Elevated levels of reactive oxygen species (ROS) have been established to be present at the onset of infection. Additionally, the delayed response by the immune system upon infection may be due to an initial depletion of antioxidants, which play a critical role in scavenging excess ROS to maintain normal physiological conditions. This pro-oxidant/antioxidant imbalance results in a condition known as oxidative stress (OS). OS has been reported to be an integral element in the progression of many diseases, including AIDS. Therefore, it is presumed that antioxidant treatment may provide a promising and cost-effective therapeutic approach in treating HIV-infected individuals on a global scale. Although HIV infection presents one of the most arduous complications to humans worldwide, awareness of its origin, adverse effects, and potential treatment remain confined to a limited population. The aim of this article is to inform the reader of how AIDS developed into a global epidemic so rapidly, while emphasizing the relationship between oxidants and antioxidants in the pathogenesis of HIV/AIDS. A basic review of reduction/oxidation (redox) reactions is provided to understand how such simple introductory concepts can have such profound effects on the body. This serves as an essential building block to the topics that follow: ROS, antioxidants, and OS in HIV disease. Moreover, it allows for us to better define and elucidate OS as an often-overlooked link between HIV-infection and the progression of AIDS, as well as potential antioxidant treatment.

In this study, the sperm protein profile was compared between fertile and infertile men using 2-dimensional gel electrophoresis, liquid chromatography mass spectrometer analysis, and matrix-assisted laser desorption ionization-time-of-flight-mass spectrometry. Four unique proteins, semenogelin II precursor, prolactin-induced protein, clusterin isoform 1, and prostate-specific antigen isoform 1 preproprotein, were predominantly present in the semen of healthy men; however, semenogelin II precursor and clusterin isoform 1 were not seen in the semen of infertile men, suggesting unique differences in the spermatozoa protein profiles of fertile and infertile men.

Modifying experimental conditions of optokinetic nystagmus (OKN) result in different outcomes and may not optimally translate into clinical testing. The purpose of this study was to assess the influence of subject instruction on the anatomical correlates of OKN. The instructions were to voluntarily look or stare at the same moving grating with fixed contrast and spatial and temporal frequencies. Look and stare OKN were generated under identical stimulus “ON” conditions (vertical sine wave grating of 1.14 c/deg drifting right to left at 11.4 c/s with binocular viewing). FMRI was undertaken utilizing a 3.0 T GE system and the BOLD technique. Subjects included 6 normal adults ranging in age from 18 to 54 years with normal visual acuity (20/20 or better) and normal stereoacuity (40 s of arc or better). The results reveal that look OKN generated significantly more cortical FMRI activation than stare OKN. Look OKN areas included the culmen, parahippocampal, lingual, middle temporal gyri, inferior and superior parietal lobules and precuneus, all of which were unilaterally activated in the left hemisphere. The middle occipital gyrus was unilaterally activated in the right hemisphere while the cuneus was bilaterally activated. These results show that the activation sites for OKN studies are dependent on subject instruction which influence the type of OKN generated. Specifically, voluntary look OKN involved more brain sites than stare OKN. In so doing, we illustrate the importance of subject instruction and recommend that FMRI investigators of OKN be cognizant of these effects. The anatomical correlates of the look versus stare are discussed.

Despite promising advantages such as low cost and portability of functional near-infrared spectros- copy (fNIRS), it has yet to be widely implemented outside of basic research. Specifically, fNIRS has yet to be proven as a standalone tool within a clinical setting. The objective of this study was to assess hemodynamic concentration changes at the primary and premotor motor cortices as a result of simple whole-hand grasping and sequential finger-opposition (tapping) tasks. These tasks were repeated over 3 days in a randomized manner. Ten healthy young adults (23.8 � 4.8 years) participated in the study. Quantitatively, no statistically significant differences were discovered between the levels of activation for the two motor tasks ( p> 0.05). Overall, the signals were consistent across all 3 days. The findings show that both finger-opposition and hand grasping can be used interchangeably in fNIRS for assessment of motor function which would be useful in further advanc- ing techniques for clinical implementation. © 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.NPh.3.2 .025006)

Background : The American Center for Reproductive Medicine’s summer internship course in reproductive medicine and research at Cleveland Clinic is a rigorous, results-oriented annual program that began in 2008 to train both local and international students in the fundamentals of scientific research and writing. The foremost goal of the program is to encourage premedical and medical students to aspire toward a career as a physician–scientist. The internship provides participants with an opportunity to engage in original bench research and scientific writing while developing theoretical knowledge and soft skills. This study describes selected survey responses from interns who participated in the 2014 internship program. The objective of these surveys was to elicit the interns’ perspective on the internship program, its strengths and weaknesses, and to obtain insight into potential areas for improvement. Methods : Questionnaires were structured around the five fundamental aspects of the program: 1) theoretical knowledge, 2) bench research, 3) scientific writing, 4) mentorship, and 5) soft skills. In addition, an exit survey gathered information on factors that attracted the interns to the program, communication with mentors, and overall impression of the research program. Results : The opportunity to experience hands-on bench research and scientific writing, personalized mentorship, and the reputation of the institution were appreciated and ranked highly among the interns. Nearly 90% of the interns responded that the program was beneficial and well worth the time and effort invested by both interns and faculty. Conclusion : The outcomes portrayed in this study will be useful in the implementation of new programs or refinement of existing medical research training programs. Keywords: research training; translational medical research; biomedical research; summer internship; medical education; medical students; program development; program evaluation (Published: 11 November 2015) Citation: Med Educ Online 2015, 20: 29517 - http://dx.doi.org/10.3402/meo.v20.29517