A Comprehensive Review on the Electrocardiographic Manifestations of Cardiac Sarcoidosis: Patterns and Prognosis.

PURPOSE OF REVIEW: Cardiac sarcoidosis (CS) refers to cardiac involvement in sarcoidosis and is usually associated with worse outcomes. This comprehensive review aims to elucidate the electrocardiographic (ECG) signs and features associated with CS, as well as examine modern techniques and their importance in CS evaluation. RECENT FINDINGS: The exact pathogenesis of CS is still unclear, but it stems from an abnormal immunological response triggered by environmental factors in individuals with genetic predisposition. CS presents with non-cardiac symptoms; however, conduction system abnormalities are common in patients with CS. The most common electrocardiographic (ECG) signs include atrioventricular blocks and ventricular tachyarrhythmia. Distinct patterns, such as fragmented QRS complexes, T-wave alternans, and bundle branch blocks, are critical indicators of myocardial involvement. The application of advanced ECG techniques such as signal-averaged ECG, Holter monitoring, wavelet-transformed ECG, microvolt T-wave alternans, and artificial intelligence-supported analysis holds promising outcomes for opportune detection and monitoring of CS. Timely utilisation of inexpensive and readily available ECG possesses the potential to allow early detection and intervention for CS. The integration of artificial intelligence models into ECG analysis is a promising approach for improving the ECG diagnostic accuracy and further risk stratification of patients with CS.

ST-segment depression and left ventricular systolic function recovery post-atrial fibrillation ablation in heart failure.

AIMS: Catheter ablation (CA) of atrial fibrillation (AF) improves left ventricular ejection fraction (LVEF) in patients with heart failure with reduced ejection fraction (HFrEF). The impact of ST-segment depression before CA on LVEF recovery and clinical outcomes remains unknown. In the present study, we aimed to investigate the relationship between ST-segment depression during AF rhythm before CA and improvement in the LVEF and clinical outcomes in persistent atrial fibrillation (PerAF) patients with HFrEF. METHODS AND RESULTS: The present study included 122 PerAF patients (male; 98 patients, 80%, mean age: 69 [56, 76] years) from the Osaka Rosai Atrial Fibrillation ablation (ORAF) registry who had LVEF < 50% and underwent an initial ablation. The patients who underwent percutaneous coronary intervention or coronary artery bypass grafting within the past 1 month were not included in the enrolled patients. We assigned the patients based on the presence of ST-segment depression before CA during AF rhythm and evaluated improvement in the LVEF (LVEF ≥ 15%) 1 year after CA and the relationship between ST-segment depression and heart failure (HF) hospitalization/major adverse cardiovascular events (MACE), which are defined as a composite of HF hospitalization, cardiovascular death, hospitalization due to coronary artery disease, ventricular arrhythmia requiring hospitalization and stroke. The percentage of patients with improvement in the LVEF 1 year after CA was significantly lower in the patients with ST-segment depression than those without (58.6% vs. 79.7%, P = 0.012). Multiple regression analysis showed ST-segment depression was independently and significantly associated with improvement in the LVEF 1 year after CA (HR: 0.35; 95% CI: 0.129-0.928, P = 0.035). Kaplan-Meier analysis showed that the patients with ST-segment depression significantly had higher risk of HF hospitalization and MACE than those without (log rank P = 0.022 and log rank P = 0.002, respectively). Multivariable Cox proportional hazards analysis showed that ST-segment depression was independently and significantly associated with a higher risk of MACE (HR: 2.82; 95% CI: 1.210-6.584, P = 0.016). CONCLUSIONS: ST-segment depression before CA during AF rhythm was useful prognostic predictor of improvement in the LVEF and clinical outcomes including HF hospitalization and MACE in PerAF patients with HFrEF.

Virtual Reality Simulation for the Acquisition and Retention of Electrocardiogram Interpretation Skills: A Randomized Controlled Trial Among Undergraduate Medical Students.

Introduction The electrocardiogram (ECG) is one of the most important tools in diagnosing cardiac abnormalities, particularly arrhythmias and myocardial infarction. It is one of the certifiable competencies for final-year medical undergraduate students. We determined virtual reality's effectiveness in acquiring and retaining ECG interpretation skills among medical students compared to traditional teaching. Methods One hundred and forty students were randomized into two groups. Seventy-one students (immersion group) were trained using virtual reality simulation to acquire and retain interpretation skills of normal and abnormal ECG. Sixty-nine students (traditional group) were trained in the classroom using chalk and board. The primary outcome of change in acquiring knowledge of the interpretation of ECG was determined by comparing pre and post-test scores. The secondary outcome of retention of knowledge was determined by comparing pre-test and second post-test scores conducted after eight weeks of intervention. The p-value of <0.05 was considered significant. Results Out of 140 students, 50 (35.7%) were males and 90 (64.3%) were female. The mean age of the students was 22.1 (SD 1.1), with 69.3% of them between the ages of 21 and 22 years. Mean pre-test scores for the interpretation of normal ECG among immersion and traditional groups were 9.8 (SD 8.4) and 8.3 (SD 7.5), respectively, and post-test scores for the acquisition of knowledge were 24.3 (SD 5.5) and 24.8 (SD 6.3), respectively. The post-test scores for retention skills were 25.3 (SD 5.6) and 20.7 (SD 6.9) respectively (p<0.001). The mean pre-test scores for the interpretation of abnormal ECG of both groups were 7.0 (SD 6) and 8.3 (SD 6.6), respectively. Mean post-test scores for acquiring knowledge to interpret abnormal ECG were 23.5 (SD 6.2) and 17.7 (SD 9), respectively (p<0.001), and mean post-test scores for retention of interpretation skills of abnormal ECG were 19.2 (SD - 6.9) and 13.3 (SD 10.2) respectively (p=0.001). The pairwise comparison of the immersion group indicates that all the combinations that changed in score from the pre to post-intervention time points, from pre-to-retention time, and from the post-to-retention time were significant (p<0.001). Conclusion Virtual reality teaching had a better impact on acquiring and retaining the skill for interpreting normal and abnormal electrocardiograms.

Can Hyperthermia Unveil Brugada Pattern?

Brugada syndrome (BrS) is characterized by ST segment elevations in the right precordial leads, V1 - V3, with additional findings of ventricular arrhythmias and family history (FH) of sudden cardiac death (SCD) at a young age. Here, we describe a case of hyperthermia, unveiling the Brugada electrocardiography (EKG) pattern and the resolution of EKG findings with appropriate hyperthermia management. It is important to distinguish the Brugada EKG pattern from other causes of ST elevations and treat appropriately to prevent patients from developing ventricular fibrillation and SCD. It is key to identify environmental triggers in patients presenting with Brugada EKG pattern and closely monitor for ventricular fibrillation. Educating patients on prompt fever treatment with antipyretics and avoiding medications like sodium channel blockers during the febrile event is paramount to counter patients going into ventricular fibrillation. It is also crucial for close follow-up of these patients, offering them genetic testing for BrS and screening families of patients with BrS.

Holter ECG monitoring and platelets characteristics in patients with coronary artery disease and atrial fibrillation.

OBJECTIVE: Aim: To check the relationships between platelet characteristics and Holter ECG monitoring results in patients with atrial fibrillation (AF) and coronary artery disease (CAD). PATIENTS AND METHODS: Materials and Methods: 300 investigated patients were separated into three groups: I (CAD) - 149 patients with CAD without arrhythmias, II (CAD and AF) - 124 patients with CAD and AF paroxysm, and the control group (CG) - 27 patients without CAD and arrhythmias. RESULTS: Results: In the II group was detected an increase in mean platelet volume (MPV) (9.30%) and platelet-to-leucocyte ratio (PLR) (41.12%) and a decrease in platelet count (PC) (12.20%) in comparison with the I group, P<0.05. Also, in the II group was found an increase in platelet leucine (12.63%), isoleucine (10.73%), and a decrease in serine (5.06%), threonine (23.05%), valine (30.83%), glycine (32.21%) levels in comparison with the I group, P<0.05. PC, MPV, and PLR ratios were correlated with supraventricular extrasystoles per hour (r=-0.352, r=0.308, and r=0.359, consequently), P<0.05. Platelets distribution width (PDW) was correlated with ST-segment changes (r=0.371), P<0.05. Platelet threonine, serine, glycine, alanine, and valine levels were correlated with total supraventricular extrasystoles (r=-0.374, r=-0.358, r=-0.402, r=-0.307, r=-0.312, consequently) and supraventricular extrasystoles per hour (r=-0.374, r=-0.358, r=-0.402, r=-0.307, r=-0.312, consequently), P<0.05. Platelet lysine, taurine, cysteine, and phenylalanine levels were correlated with ST-segment changes (r=-0.319, r=-0.344, r=-0.376, and r=0.317, consequently), P<0.05. CONCLUSION: Conclusions: Platelet features (PC, MPV, PDW, PLR, and amino acid spectrum) are significantly correlated with supraventricular arrhythmias and ST-segment episodes, which shows their role in AF and CAD pathogenesis.

Structural analysis of prognostic diagnostics of cardiovascular system adaptive capacity and assessment of psycho physiological resistance to stressоgenic cognitive loads.

OBJECTIVE: Aim: To conduct a structural analysis of cardiological signs of adaptation to stressogenic cognitive loads by identifying factor features of correlations between heart rate variability (HRV) and coping-testing data indicators. PATIENTS AND METHODS: Materials and Methods: 43 people aged 19.7±1.8 years (23 boys and 20 girls) were monitored for their HRV. Methods included DC-06000 portable ECG recorder, 3X series "badge" type (single channel) and COPE Test. The study process includes four stages. RESULTS: Results: As a result of further factor correlation analysis, it was revealed that Factor 1 "HRV Stress Indicators" has a negative correlation (p<0.05) of "moderate" strength ρs= -0.363 with Factor 2 "Strategies to avoid problems and stresses" and a positive correlation of "weak" strength ρs=0.167 with Factor 3 "Psychoemotional Indicators". If two factors correlate with each other, it indicates they are related and can interact, which is important for adequate interpretation of the results of factor analysis. CONCLUSION: Conclusions: Structural analysis of the complex of cardiological signs of adaptivity to stressogenic cognitive loads and coping-testing data revealed the existence of three correlated factors: Factor 1 "HRV Stress Scores", Factor 2 "Strategies to avoid problems and stress", Factor 3 "Psychoemotional indicators". The revealed negative correlation of Factors 1 and 2 may indicate that in case the impact of Factor 2 "Strategies to avoid problems and stress" increases, the intensity of Factor 1 "HRV Stress Scores" (i.e., stress signs according to the indicators of heart rate variability) may decrease.

Comparison of corrected QT (QTc) interval of electrocardiogram calculated from various formulas.

QT interval in an electrocardiogram (ECG) is interpreted after correction (QTc) by various formulas. This study aimed to compare the QTcs calculated by nine formulas. Sinus rhythm ECG reports of 1140 anonymous subjects showed uncorrected QT interval of 388.49 ± 42.74 ms. The QTc calculated by Bazett (443.96 ± 57.58 ms), Fridericia (424.37 ± 50.1 ms), Dmitrienko (433.59 ± 53.37 ms), Framingham (422.59 ± 45.55 ms), Schlamowitz (433.89 ± 48.05 ms), Hodges (421.6 ± 46.4 ms), Ashman (434.33 ± 54.05 ms), Rautaharju (427.75 ± 47.4 ms), and Sarma (429.22 ± 48.67 ms) showed a significant difference F (8, 10251) = 22.78 p < 0.0001. Hence, ECG should contain the formula for proper reporting and ease of interpretation by clinicians.

Continuous blood pressure prediction system using Conv-LSTM network on hybrid latent features of photoplethysmogram (PPG) and electrocardiogram (ECG) signals.

Continuous blood pressure (BP) monitoring is essential for managing cardiovascular disease. However, existing devices often require expert handling, highlighting the need for alternative methods to simplify the process. Researchers have developed various methods using physiological signals to address this issue. Yet, many of these methods either fall short in accuracy according to the BHS, AAMI, and IEEE standards for BP measurement devices or suffer from low computational efficiency due to the complexity of their models. To solve this problem, we developed a BP prediction system that merges extracted features of PPG and ECG from two pulses of both signals using convolutional and LSTM layers, followed by incorporating the R-to-R interval durations as additional features for predicting systolic (SBP) and diastolic (DBP) blood pressure. Our findings indicate that the prediction accuracies for SBP and DBP were 5.306 ± 7.248 mmHg with a 0.877 correlation coefficient and 3.296 ± 4.764 mmHg with a 0.918 correlation coefficient, respectively. We found that our proposed model achieved a robust performance on the MIMIC III dataset with a minimum architectural design and high-level accuracy compared to existing methods. Thus, our method not only meets the passing category for BHS, AAMI, and IEEE guidelines but also stands out as the most rapidly accurate deep-learning-based BP measurement device currently available.

Smartwatch-Detected Arrhythmias in Patients After Transcatheter Aortic Valve Replacement (TAVR): Analysis of the SMART TAVR Trial.

BACKGROUND: There are limited data available on the development of arrhythmias in patients at risk of high-degree atrioventricular block (HAVB) or complete heart block (CHB) following transcatheter aortic valve replacement (TAVR). OBJECTIVE: This study aimed to explore the incidence and evolution of arrhythmias by monitoring patients at risk of HAVB or CHB after TAVR using smartwatches. METHODS: We analyzed 188 consecutive patients in the prospective SMART TAVR (smartwatch-facilitated early discharge in patients undergoing TAVR) trial. Patients were divided into 2 groups according to the risk of HAVB or CHB. Patients were required to trigger a single-lead electrocardiogram (ECG) recording and send it to the Heart Health App via their smartphone. Physicians in the central ECG core lab would then analyze the ECG. The incidence and timing of arrhythmias and pacemaker implantation within a 30-day follow-up were compared. All arrhythmic events were adjudicated in a central ECG core lab. RESULTS: The mean age of the patients was 73.1 (SD 7.3) years, of whom 105 (55.9%) were men. The mean discharge day after TAVR was 2.0 (SD 1.8) days. There were no statistically significant changes in the evolution of atrial fibrillation or atrial flutter, Mobitz I, Mobitz II, and third-degree atrial ventricular block over time in the first month after TAVR. The incidence of the left bundle branch block (LBBB) increased in the first week and decreased in the subsequent 3 weeks significantly (P<.001). Patients at higher risk of HAVB or CHB received more pacemaker implantation after discharge (n=8, 9.6% vs n=2, 1.9%; P=.04). The incidence of LBBB was higher in the group with higher HAVB or CHB risk (n=47, 56.6% vs n=34, 32.4%; P=.001). The independent predictors for pacemaker implantation were age, baseline atrial fibrillation, baseline right bundle branch block, Mobitz II, and third-degree atrioventricular block detected by the smartwatch. CONCLUSIONS: Except for LBBB, no change in arrhythmias was observed over time in the first month after TAVR. A higher incidence of pacemaker implantation after discharge was observed in patients at risk of HAVB or CHB. However, Mobitz II and third-degree atrioventricular block detected by the smartwatch during follow-ups were more valuable indicators to predict pacemaker implantation after discharge from the index TAVR. TRIAL REGISTRATION: ClinicalTrials.gov NCT04454177; https://clinicaltrials.gov/study/NCT04454177.

Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.

BACKGROUND: Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability. OBJECTIVE: We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF. METHODS: We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF). RESULTS: Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001). CONCLUSIONS: Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843.

Progranulin, sICAM-1, and sVCAM-1 May Predict an Increased Risk for Ventricular Arrhythmias in Patients with Systemic Sclerosis.

In systemic sclerosis (SSc), fibrosis of the myocardium along with ongoing autoimmune inflammation can alter the electric function of the cardiac myocytes, which may increase the risk for ventricular arrhythmias and sudden cardiac death. We analyzed the electrocardiographic (ECG) variables describing ventricular repolarization such as QT interval, QT dispersion (QTd), T wave peak-to-end interval (Tpe), and arrhythmogeneity index (AIX) of 26 patients with SSc and 36 healthy controls. Furthermore, echocardiographic and laboratory parameters were examined, with a focus on inflammatory proteins like C-reactive ptotein (CRP), soluble intracellular adhesion molecule-1 (sICAM-1), soluble vascular adhesion molecule-1 (sVCAM-1), and progranulin (PGRN). The CRP, sICAM-1, and sVCAM-1 levels were positively correlated with the length of the QT interval. Although the serum PGRN levels were not increased in the SSc group compared to the controls, in SSc patients, the PGRN levels were positively correlated with the QT interval and the AIX. According to our results, we conclude that there may be a potential association between autoimmune inflammation and the risk for ventricular arrhythmias in patients with SSc. We emphasize that the measurement of laboratory parameters of inflammatory activity including CRP, PGRN, sVCAM-1, and sICAM-1 could be helpful in the prediction of sudden cardiac death in patients with SSc.

Cardiologic scoring system as predictors of mortality in geriatric COVID-19 patients.

OBJECTIVE: To develop a cardiologic scoring system to predict mortality among geriatric coronavirus disease-2019 patients. METHODS: The retrospective cohort study was conducted after approval from the ethics review committee of the Dr Soetomo General Academic Hospital, Surabaya, Indonesia, and comprised data from March 1, 2020, to April 30, 2021, of geriatric patients of either gender confirmed for coronavirus disease-2019 from several referral hospitals in East Java, Indonesia. Data on comorbidities, electrocardiograph, and chest X-ray findings was examined to develop a comprehensive scoring system. Data was analysed using SPSS 26. RESULTS: Of the 3,893 cases reviewed, data of 322(8.27%) patients was analysed; 191(59.3%) males and 131(40.7%) females. The overall mean age was 66±4.1 years (range: 60-75 years). Of them, 267(82.9%) subjects were alive upon hospital discharge, while 55(17.1%) died during hospitalisation. Of the 24 variables analysed, 5(21%) were found to be significant: cardiomegaly, sinus tachycardia, ST-segment abnormalities, pathological T-wave inversions, and axis deviation (p<0.05). Receiver operating characteristic curve analysis showed an area under the curve 0.86, cut-off point ≤4 with sensitivity 89% and specificity 69%. CONCLUSIONS: The scoring system was found to have the potential to predict mortality in geriatric coronavirus disease-2019 patients based on cardiac findings during hospital admission.

Clinical findings and electrocardiography predicted mortality among COVID-19 patients with pre-existing hypertension: A cross-sectional study of East Java Province, Indonesia.

OBJECTIVE: To evaluate the factors associated with mortality among coronavirus disease-2019 patients with preexisting hypertension. METHODS: The retrospective, cross-sectional study was conducted from June 15 to July 7, 2021, after approval from Dr Soetomo General Province Hospital, Indonesia, and comprised data from the coronavirus disease-2019 registry in the East Java province of Indonesia from March 2020 to June 2021. Data was collected for adult patients infected by coronavirus disease-2019 with pre-existing hypertension Data was analysed using SPSS 23. RESULTS: Of the 2,732 patients in the registry, 425(15.6%) with median age 56.5 years (interquartile range: 50-64 years) had pre-existing hypertension. Of them, 251(59.06%) were males, and 110(25.9%) had died while in hospital. Mortality was associated with older age; higher white blood cell counts at admission and lower platelet count (p<0.05). In addition, electrocardiogram parameters associated with mortality were faster heart rate and ST abnormality (p<0.05). CONCLUSIONS: Older age, high white blood cell level, lower platelet count, faster heart rate, and ST abnormality at admission were found to be the predictors of mortality among hospitalised coronavirus disease-2019 patients with pre-existing hypertension.

Wellens Syndrome in a patient with a history of hypertension and chronic obstructive pulmonary disease: a case report.

Wellens syndrome, an ST Elevation Myocardial Infarction (STEMI) equivalent, is also known as T-wave left anterior descending (LAD) coronary artery disease. Wellens syndrome is characterized by a unique electrocardiogram (ECG) pattern that suggests a significant stenosis in the left anterior descending coronary artery that warrants immediate intervention. Hereby, we present a case report of Wellens syndrome in a patient with a history of hypertension and chronic obstructive pulmonary disease (COPD) that may be potentially mistaken for pseudo- Wellens syndrome because the ECG pattern mimics left ventricular strain pattern (LVSP) in left ventricular hypertrophy (LVH). Thus, cautious examination of recent chest pain and ECG is important to differentiate Wellens syndrome and LVSP in patients with hypertension and COPD to perform early detection and aggressive intervention since they may help to lessen the adverse results.

Myocardial injury mimicking acute myocardial infarction due to coronavirus infection in adults with pre-existing apical hypertrophic cardiomyopathy.

Symptoms of apical hypertrophic cardiomyopathy (ApHCM) can mimic acute myocardial infarction (AMI). Following COVID-19 infection, the elevation of troponin in ApHCM might be confusing, due to its similarity with AMI. We report the case of a 64-year-old male patient presenting with exertional dyspnoea and chest discomfort. He had no history of coronary artery disease (CAD), but his swab test was positive for COVID-19. The physical examination was normal. The 12-lead electrocardiogram showed a sinus rhythm of 78 bpm, with deep inverted T waves in leads V2 to V6, I, and aVL, and left ventricular hypertrophy. An Echocardiographic examination showed an 18 mm apical wall thickness of the left ventricle. Laboratory tests revealed elevated hs- Troponin level, but diagnostic coronary angiography was normal. The diagnostic criteria fulfilled apical cardiac hypertrophic cardiomyopathy. Coronavirus can induce atypical cardiovascular symptoms in pre-existing ApHCM. Misdiagnosis and failure to recognize may result in inappropriate therapy and delay in definitive treatment.

Novel interpretable Feature set extraction and classification for accurate atrial fibrillation detection from ECGs.

OBJECTIVE: We present a novel method for detecting atrial fibrillation (AFib) by analyzing Lead II electrocardiograms (ECGs) using a unique set of features. METHODS: For this purpose, we used specific signal processing techniques, such as proper orthogonal decomposition, continuous wavelet transforms, discrete cosine transform, and standard cross-correlation, to extract 48 features from the ECGs. Thus, our approach aims to more effectively capture AFib signatures, such as beat-to-beat variability and fibrillatory waves, than traditional metrics. Moreover, our features were designed to be physiologically interpretable. Subsequently, we incorporated an XGBoost-based ECG classifier to train and evaluate it using the publicly available 'Training' dataset of the 2017 PhysioNet Challenge, which includes 'Normal,' 'AFib,' 'Other,' and 'Noisy' ECG categories. RESULTS: Our method achieved an accuracy of 96 % and an F1-score of 0.83 for 'AFib' detection and 80 % accuracy and 0.85 F1-score for 'Normal' ECGs. Finally, we compared our method's performance with the top-classifiers from the 2017 PhysioNet Challenge, namely ENCASE, Random Forest, and Cascaded Binary. As reported by Clifford et al., 2017, these three best performing models scored 0.80, 0.83, 0.82, in terms of F1-score for 'AFib' detection, respectively. CONCLUSION: Despite using significantly fewer features than the competition's state-of-the-art ECG detection algorithms (48 vs. 150-622), our model achieved a comparable F1-score of 0.83 for successful 'AFib' detection. SIGNIFICANCE: The interpretable features specifically designed for 'AFib' detection results in our method's adaptability in clinical settings for real-time arrhythmia detection and is even effective with short ECGs (<10 heartbeats).

Rapidly Progressive Apical Hypertrophic Cardiomyopathy: Not Everything is What It Seems.

Apical hypertrophic cardiomyopathy (HCM) is a rare variant of HCM. A 43-year-old female with a past medical history significant for hypertension and kidney transplantation presented with recurrent syncopal episodes and dyspnea on exertion. Electrocardiogram showed characteristic diffuse giant T-waves inversion, and cardiac magnetic resonance showed HCM with circumferential apical thickening. This case highlights the rapid development of apical HCM and its challenging diagnostic characteristics.

Cardiac Damage After SARS-CoV2 Infection.

COVID-19 is a viral disease that can manifest acutely in the respiratory tract and other organs. In this study, we aimed to investigate potential long-term damage to the heart from COVID-19. For this study, we divided 97 consecutive unselected COVID-19 patients aged 18-80 years at a cardiology practice in Cologne, Germany, into two groups based on the severity of their infection. We performed a resting ECG and a resting transthoracic echocardiography three and six months after SARS-CoV2 infection. The key discriminator determining disease severity was bed confinement or hospital admission. Group 1 included patients with less severe COVID-19, whereas group 2 contained more severe cases. Heart rate as the primary ECG endpoint was lower by a statistically significant amount for the entire study population (p=0.024), subdivided by gender (pwomen <0.001, pmen <0.001) and in group 1 p =0.003 compared to three months. QTc time and repolarization disturbances as primary ECG endpoints and the echocardiographic primary endpoints, left ventricular ejection fraction, and left ventricular end-diastolic diameter (LVEDD), showed no relevant difference between the subgroups at three and six months or between the measurements taken at each point. In contrast, LVEDD normalized to body surface area was statistically significantly lower at six months in women in group 1 compared to group 2 (p=0.048) and in the overall study population at six months compared with the data after three months (p=0.034). E/E' was statistically lower at six months than at three months in the whole population (p=0.004) and in women (p=0.031). All measured echocardiographic and electrocardiographic mean values were within the normal range in all groups and follow-up controls. Overall, the prospective study conducted showed no significant evidence of long-term cardiac damage from COVID-19 disease, as evidenced by electrocardiographic and echocardiographic examinations at three and six months after infection.

The role of routine cardiac investigations before hyperbaric oxygen treatment.

Cardiac complications are a rare but potentially serious consequence of hyperbaric oxygen treatment (HBOT), resulting from increased blood pressure and decreased heart rate and cardiac output associated with treatment. These physiologic changes are generally well-tolerated by patients without preexisting cardiac conditions, although those with known or undetected cardiac disease may be more vulnerable to treatment complications. Currently, there are no universally accepted guidelines for pre-HBOT cardiac screening to identify these patients at heightened risk, leading to variability in practice patterns. In the absence of HBOT-specific evidence, screening protocols might be adapted from the diving medicine community; however, given the important differences in physiological stressors, these may not be entirely applicable to patients undergoing HBOT. Traditional cardiac investigations such as electro- and echo-cardiograms are limited in their ability to detect relevant risk modifying states in the pre-HBOT patient, stymieing their cost-effectiveness as routine tests. In the absence of strong evidence to support routine cardiac investigation, we argue that a comprehensive history and physical exam - tailored to identify high-risk patients based on clinical parameters - may serve as a more practical screening tool. While certain unique patient groups such as those undergoing dialysis or with implanted cardiac devices may warrant specialised assessment, thorough evaluation may be sufficient to identify many patients unlikely to benefit from cardiac investigation in the pre-HBOT setting. A clinical decision-making tool based on suggested low-risk and high-risk features is offered to guide the use of targeted cardiac investigation prior to HBOT.