ECG is the most used tools in cardiology. Some life-threatening arrhythmias and cardiac abnormalities can be diagnosed with ECG. The aim is to assess the role of ECG for the detection of cardiac disease in children, from September 2014 to January 2020, ECG was performed in 52 primary school in Como. 5793 children (49.4% female) aged 8.1 (± 1.3) years had an ECG; 79 (1.3%) were abnormal due to the following findings: 34 (43%) right bundle branch block (RBBB), 9 (11%) abnormal ventricular repolarization, 7 (9%) long QT, 7 atrio-ventricular block (AVB), 7 (9%) accessory pathway, 5 (5%) premature ventricular beat, 5 (5%) anterior hemiblock, 4 (5%) left ventricular hypertrophy, 1 (2%) ST alteration. Long QT syndrome was excluded in all children with long QT. Among those with RBBB, 2 had severe inter-atrial septal defect (one underwent to closure), while one had moderate mitral valve regurgitation and bicuspid aortic, and 2 had moderate mitral regurgitation. One child with AVB had pacemaker implantation. One child with accessory pathway had Wolf-Parkinson syndrome and undergone to catheter ablation. Among apparently healthy children, ECG may be useful to detect cardiac disease with potential consequence in the adulthood.

Left atrial appendage aneurysm (LAAA) is an exceptionally rare cardiac abnormality that may be congenital or acquired. Although many patients remain asymptomatic for years, common presentations include palpitations, dyspnoea, and thromboembolic events.

An 11-year-old male neutered Birman cat was referred for investigation of pyelonephritis. On physical examination, gallop sounds and bradycardia were noted. There was mild azotaemia and marked neutrophilia, attributed to the pyelonephritis. Transthoracic echocardiography revealed a non-specific cardiomyopathy phenotype with biatrial enlargement. Electrocardiography showed wide QRS complexes, bradycardia and synchronous dissociation between atrial and ventricular activity. During the first examination, the synchronisation appeared mostly fixed, with consistent PR intervals, on subsequent evaluations, the PR intervals appeared to vary on a beat-to-beat basis. The diagnosis was of isorhythmic atrioventricular dissociation type I, with an initial long phase of fixed synchronisation, mimicking an isorhythmic atrioventricular dissociation type II, and concomitant ventricular depolarisation with a left bundle branch block morphology.

Artificial intelligence enhanced electrocardiography (AI-ECG) has shown promise in detecting cardiac abnormalities, but validation against cardiac magnetic resonance (CMR), the reference standard for cardiac structure and function, remains limited. We evaluated the diagnostic performance of six AI-ECG models in identifying CMR-defined cardiac abnormalities in a population-based cohort. This cohort study included 38,804 UK Biobank participants with paired 12-lead ECG and CMR data. AI-ECG models, originally developed using Korean hospital-based datasets, were externally validated in this sample. Cardiac abnormalities were defined as the top 1% of CMR-derived values. Outcomes were the performance of AI-ECG in detecting left and right ventricular dysfunction, based on ejection fraction (QCG-LVD and QCG-RVD) and strain (ECG-LVGLS and ECG-RVGLS), and structural abnormalities including left ventricular hypertrophy (LVH) and left atrial enlargement (LAE). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), with subgroup analyses by age, sex, and comorbidities. Among 38,804 participants (mean age 64.2 years; 48.2% male), AI-ECG models showed high accuracy for detecting left ventricular dysfunction (AUC 0.887 for QCG-LVD and 0.896 for ECG-LVGLS) and right ventricular dysfunction (AUC 0.778 for QCG-RVD and 0.825 for ECG-RVGLS). In a sub-cohort of 21,267 individuals, AUCs were 0.824 for detecting LVH and 0.883 for LAE. Subgroup analyses showed consistent performance, with higher accuracy among older individuals, males, and those with hypertension or ischemic heart disease. In this large multiethnic cohort, AI-ECG models demonstrated strong performance in detecting CMR-defined abnormalities, supporting their potential as a scalable, noninvasive screening tool for cardiovascular disease.

This short communication aims at raising an insight about an observation made 26 years ago, describing a transient false positive electrocardiogram (ECG)-based diagnosis of left ventricular hypertrophy (LVH) in patients with various tachycardias (e.g, sinus, supraventricular, atrial fibrillation) with or without evidence of LVH as assessed by cardiac imaging. The mechanism is purported to be due to a tachycardia-mediated shortening of the diastolic left ventricular (LV) dimensions due to tachycardias, with the diastolic LV volume centroid displaced closer to the anterior chest wall (e.g. "Wilson's proximity effect"). This insight prevents an inappropriate diagnosis of LVH; also, it is possible that the absence of such a phenomenon during tachycardias may imply in some cases advanced acute or chronic heart failure, resulting in LV diastolic dilatation, counteracting this ECG phenomenon. The author advocates that automated ECG interpretation algorithms providing interpretation upon recording of an ECG should be modified to reflect on this insight, since many physicians rely inappropriately on the automated ECG interpretation.

Aortic stenosis (AS) is a progressive valvular disease that presents with subtle symptoms in the emergency department, leading to delayed diagnosis. While acute presentations of valvular pathology exhibit dramatic clinical signs, most cases are chronic and commonly discovered incidentally during routine evaluations. Compensatory adaptations, including cardiac remodeling and atrial augmentation of preload, temporarily sustain cardiac function and mask disease severity, increasing the risk of heart failure and sudden cardiac death. This case-based article details the diagnostic journey of a 59-year-old male who presented with elevated blood pressure and lightheadedness. Abnormal electrocardiogram (ECG) findings prompted further evaluation, underscoring the importance of early recognition of ECG abnormalities. The article highlights the crucial role of advanced practice emergency providers in conducting timely cardiac assessments. By proposing a systematic approach from initial ECG findings to definitive imaging, it aims to enhance early intervention and ultimately improve patient outcomes in cases of AS.

Hypertrophic cardiomyopathy (HCM) phenotype represents the most commonly diagnosed cardiac disorder in felines, characterized by heterogeneous clinical presentations and a well-established genetic basis. This study aims to integrate clinical, laboratory, and imaging diagnostic assessments of the feline HCM phenotype, providing a comprehensive perspective on how complementary diagnostic approaches enhance disease understanding and precision. The HCM phenotype is defined by concentric hypertrophy of the left ventricular free wall and/or interventricular septum, often accompanied by secondary left atrial remodeling due to chronic pressure and volume overload. Clinical signs typically emerge with disease progression, frequently culminating in congestive heart failure (CHF) and respiratory signs; however, some cats may remain asymptomatic. Accurate diagnosis of the HCM phenotype requires an integrative approach combining thorough clinical evaluation and advanced imaging modalities to avoid misdiagnosis, which may negatively impact prognosis and quality of life. Detailed clinical history and physical examination are essential for diagnostic orientation, particularly in symptomatic patients. Routine laboratory tests support systemic assessment, although no pathognomonic biomarker has been identified to date. Cardiac biomarkers such as atrial natriuretic peptide (ANP), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and cardiac troponin I (cTnI) provide complementary diagnostic information, albeit with lower sensitivity than imaging techniques. While electrocardiography may reveal conduction disturbances suggestive of HCM, transthoracic echocardiography remains the diagnostic gold standard. In addition to confirming the diagnosis, echocardiographic evaluation allows for disease staging, longitudinal monitoring, and evidence-based therapeutic decision-making. Our study reinforces the need for an integrated diagnostic framework that combines clinical examination, laboratory testing, and imaging evaluation. By promoting a multidimensional diagnostic perspective, this study contributes to refining the understanding of the feline HCM phenotype and supports the development of more precise diagnostic and therapeutic strategies, ultimately improving clinical outcomes in affected cats.