Investigating the Effect of Online Gamification on Electrocardiogram Interpretation and Self-Directed Learning in Nursing Students.

Introduction: Knowing and interpreting the Electrocardiogram correctly and taking care based on it is one of the most important ways to save the patient's life. Therefore, this study aimed to investigate the effect of online gamification on the ability to interpret Electrocardiograms and Self-Directed Learning in nursing students. Methods: The present study was carried out quasi-experimentally on 44 nursing students (22 participants for each group) in the fifth semester in Ilam city in 2023 using a convenience sampling method by four permutations balanced block randomization to control and intervention groups. During four sessions in four weeks, the participants in the intervention group received Electrocardiogram interpretation as training on the Storyline platform and playing games in the Socrative software. The control group received the same content in the form of education from the same lecturer. ECG Interpretation Learning Test, Self-Directed Learning, Mini-Mental Status Examination, and demographic questionnaires were filled out before the intervention (pre-test). Then, Electrocardiogram Interpretation Learning Test and Self-Directed Learning questionnaires were completed two weeks after the end of the intervention (post-test), and the data were analyzed in the standard error of 0.05 using Shapiro-Wilk, independent t-test, paired t-test, chi-square, and multiple linear regression tests in SPSS V.16. Results: The mean and standard deviation of the participants' age was 21.35 ± 1.02, and 59% of the samples were male. Before the intervention, no significant difference was observed between the two groups, but after the intervention, the mean and standard deviation of the ECG interpretation scores in the intervention group increased significantly (14.1±2.94) compared to the control group (8.8±1.36) (P=0.007). Also, Self-Directed Learning in the intervention group (168±10.42) compared to the control group (125±4.3) showed a statistically significant difference (P=0.003). Conclusion: Using online gamification as a new educational method in nursing students improves the ability to interpret Electrocardiograms and Self-Directed Learning. It is suggested that this method should be used to teach lessons that are more useful and important at the hospital.

Assessing the Reliability of Machine Learning Explanations in ECG Analysis Through Feature Attribution.

Feature attribution methods stand as a popular approach for explaining the decisions made by convolutional neural networks. Given their nature as local explainability tools, these methods fall short in providing a systematic evaluation of their global meaningfulness. This limitation often gives rise to confirmation bias, where explanations are crafted after the fact. Consequently, we conducted a systematic investigation of feature attribution methods within the realm of electrocardiogram time series, focusing on R-peak, T-wave, and P-wave. Using a simulated dataset with modifications limited to the R-peak and T-wave, we evaluated the performance of various feature attribution techniques across two CNN architectures and explainability frameworks. Extending our analysis to real-world data revealed that, while feature attribution maps effectively highlight significant regions, their clarity is lacking, even under the simulated ideal conditions, resulting in blurry representations.

Benchmarking Approaches: Time Series Versus Feature-Based Machine Learning in ECG Analysis on the PTB-XL Dataset.

Extensive research has been conducted on time series and tabular data in the context of classification tasks, considering their distinct data domains. While feature extraction enables the transformation of series into tabular data, direct comparative comparisons between these data types remain scarce. Especially in the domain of medical data, such as electrocardiograms (ECGs), deep learning faces challenges due to its lack of easy and fast interpretability and explainability. However, these are crucial aspects for a wide and reliable adoption in the field. In our study, we assess the performance of XGBoost and InceptionTime on ECG features and time series data respectively. Our findings reveal that features extracted from ECG signals not only achieve competitive performance but also retain advantages during training and inference. These advantages encompass accuracy, resource efficiency, stability, and a high level of explainability.

A Novel Method and Python Library for ECG Signal Quality Assessment.

Electrocardiogram (ECG) is one of the reference cardiovascular diagnostic exams. However, the ECG signal is very prone to being distorted through different sources of artifacts that can later interfere with the diagnostic. For this reason, signal quality assessment (SQA) methods that identify corrupted signals are critical to improve the robustness of automatic ECG diagnostic methods. This work presents a review and open-source implementation of different available indices for SQA as well as introducing an index that considers the ECG as a dynamical system. These indices are then used to develop machine learning models which evaluate the quality of the signals. The proposed index along the designed ML models are shown to improve SQA for ECG signals.

Ventricular arrhythmias originating from different portions of the communicating vein of the left ventricular summit: electrocardiographic characteristics and catheter ablation.

BACKGROUND: Idiopathic ventricular arrhythmias (IVAs) arising from different portions of the communicating vein of the left ventricular summit (summit-CV) are not a rare phenomenon. Whereas its electrocardiographic (ECG) and electrophysiological characteristics are not fully investigated. OBJECTIVE: This study aimed to identify distinct ECG and electrophysiological features of IVAs originating from different portions of summit-CV. METHODS: Nineteen patients confirmed arising from summit-CV were included in this study. RESULTS: The 19 patients were divided into proximal and distal portion groups based on their target sites in summit-CV. In the proximal portion group, 100% (11/11) VAs showed dominant negative (rs or QS) waves in lead I, while in the distal portion group, 87.5% (7/8) showed dominant positive waves (R, Rs or r) (p < 0.000). In lead V1, 100% (11/11) of the proximal portion group showed dominant positive waves (R or Rs), while 62.50% (5/8) of the distal portion group showed positive and negative bidirectional or negative waves (RS or rS) (p < 0.005). RI>4mV, SI<3.5mV, RV1<13mV, SV1>3.5mV, RI/SI>0.83, and RV1/SV1< 2.6 indicated a distal portion of summit-CV with the predictive value of 0.909, 1.000, 0.653, 0.972, 0.903, 0.966, respectively. A more positive wave in lead I and a more negative wave in lead V1 indicated more distal origin in summit-CV. Target sites in proximal and distal summit-CV groups showed similar electrophysiological characteristics during mapping. CONCLUSIONS: There were significant differences in ECG characteristics of VAs at different portions of summit-CV, which could aid pre-procedure planning and facilitate radiofrequency catheter ablation (RFCA) procedures.

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification.

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrhythmia classification using machine learning or deep learning. However, the issue of low classification rates of individual classes in inter-patient heartbeat classification remains a challenge. This study proposes a method for inter-patient heartbeat classification by fusing dual-channel squeeze-and-excitation residual neural networks (SE-ResNet) and expert features. In the preprocessing stage, ECG heartbeats extracted from both leads of ECG signals are filtered and normalized. Additionally, nine features representing waveform morphology and heartbeat contextual information are selected to be fused with the deep neural networks. Using different filter and kernel sizes for each block, the SE-residual block-based model can effectively learn long-term features between heartbeats. The divided ECG heartbeats and extracted features are then input to the improved SE-ResNet for training and testing according to the inter-patient scheme. The focal loss is utilized to handle the heartbeat of the imbalance category. The proposed arrhythmia classification method is evaluated on three open-source databases, and it achieved an overall F1-score of 83.39 % in the MIT-BIH database. This system can be applied in the scenario of daily monitoring of ECG and plays a significant role in diagnosing arrhythmias.

Benefit of prehospital electrocardiogram on door-to-device time in ST-segment elevation myocardial infarction with cardiogenic shock: Data from the Kanagawa Acute Cardiovascular Registry.

BACKGROUND: The benefit of prehospital 12­lead electrocardiogram (PH-ECG) performed by emergency medical service personnel at the site of first medical contact (FMC) in patients with ST-segment elevation myocardial infarction (STEMI) with cardiogenic shock (CS-STEMI) remains unclear. This study aimed to investigate the effect of PH-ECG on door-to-device time in patients with CS-STEMI. METHODS: This study enrolled CS-STEMI (Killip class IV) patients who were transferred directly to hospitals by ambulance (n = 517) from the Kanagawa Acute Cardiovascular Registry database. Patients were divided into PH-ECG (+) (n = 270) and PH-ECG (-) (n = 247) groups. Patients who experienced out-of-hospital cardiac arrest, who did not undergo emergent coronary intervention, or whose data were missing were excluded. Patient characteristics, FMC-to-door time, door-to-device time, and in-hospital mortality were compared between the groups. RESULTS: The patient backgrounds of the PH-ECG (+) and PH-ECG (-) groups were comparable. The peak creatinine kinase level was greater in the PH-ECG (+) group than in the PH-ECG (-) group [2756 (1292-6009) IU/ml vs. 2270 (957-5258) IU/ml, p = 0.048]. The FMC-to-door time was similar between the two groups [25 (20-33) min vs. 27 (20-35) min, p = 0.530], while the door-to-device time was significantly shorter in the PH-ECG group [74 (52-103) min vs. 83 (62-111) min, p = 0.007]. In-hospital mortality did not differ between the two groups (18 % vs. 21 %, p = 0.405). Multivariable logistic regression analyses revealed that PH-ECG (+) was independently associated with a door-to-device time < 60 min [odds ratio (95 % confidence intervals): 1.88 (1.24-2.83), p = 0.003]. CONCLUSIONS: PH-ECG was significantly associated with shorter door-to-device times in patients with CS-STEMI. Further studies with larger populations and more defined protocols are required to evaluate the utility of PH-ECG in patients with CS-STEMI.

Rationale and Design of the Australasian Registry of Screening ECGs in National Athletes Project.

BACKGROUND: Cardiac screening of elite athletes is widely recommended by Australasian sporting federations, but data are not structured to be shared. Data are lacking from underrepresented groups to inform ECG interpretation guidelines. The ARENA (Australasian Registry of Screening ECGs in National Athletes) project is a retrospective and prospective, multicenter, longitudinal, observational registry of athlete cardiac screening results and outcomes. The aim is to create a repository to improve our understanding of the diagnoses and outcomes of screening. METHODS: Participating sports that conduct cardiac screening of athletes will contribute data. This includes an initial collection (retrospective data, waiver of consent) and future prospective data (opt-out consent). Data include sex, age, sport/event, screening date, ECG findings, cardiac test results, follow-up details, sport participation status, cardiac diagnoses, and major cardiovascular outcomes defined as sudden cardiac arrest/death, cardiac syncope or implanted cardioverter defibrillator shock, cardiac hospitalization, and arrhythmias requiring intervention. Comparisons will be made between diagnoses, outcomes, and ECG features and analyzed by sport and sex. The ARENA project was developed in collaboration with sporting bodies, team physicians, and players association representatives and endorsed by the Australasian College of Sport & Exercise Physicians and Sports Medicine Australia. CONCLUSIONS: The ARENA project will provide a long-term international data repository to improve our understanding of ECG interpretation, cardiac screening and diagnoses, and the prevalence of cardiovascular outcomes in screened athletes. A unique aim is to address evidence gaps in underrepresented athlete groups, specifically female athletes and Indigenous populations. Results will inform screening policies and guidelines.

Do electrocardiographic changes induced during intracoronary vasospasm provocation testing reflect those during spontaneous angina episodes in patients with vasospastic angina?: a case series.

Background: According to the Coronary Vasomotor Disorders International Study (COVADIS) group, the ECG criteria supporting the diagnosis of vasospastic angina (VSA) in spontaneous episodes or induced during intracoronary spasm testing are similar. However, it remains elusive whether acetylcholine-induced ECG changes during epicardial spasms reflect ECG changes that occur during the height of a spontaneous episode. Case summary: We present four patients diagnosed with VSA during intracoronary spasm testing, of whom the ECG characteristics during spasm testing and a spontaneous angina episode are described. All patients have >90% coronary epicardial vasoconstriction in one or more vessels during acetylcholine provocation. ECGs at the height of a spontaneous episode and during acetylcholine-induced coronary spasm are found to be different in three out of four patients. Discussion: In patients with VSA, the ECG at the height of a spontaneous episode and during acetylcholine-induced coronary artery spasm may differ substantially. In patients with symptoms suspicious of VSA, every effort should be undertaken to obtain ECGs during the height of a spontaneous episode of angina pectoris and there should be a low threshold to perform intracoronary function testing.

A Comparative Study of the Cardiovascular Effects of a Lower Dose of Heat-Stable Carbetocin Versus the Standard Dose in the Prevention of Postpartum Hemorrhage During Elective Cesarean Delivery: A Single-Blinded, Randomized, Parallel-Group Trial.

INTRODUCTION: Oxytocin is a uterotonic drug that acts on receptors in the myometrium, causing uterine contractions. However, oxytocin receptors are also present in other organs, including the myocardium. Heat-stable carbetocin, a long-acting analog of oxytocin, is also known to act on these oxytocin receptors. As carbetocin has a long half-life of 40 minutes, its duration of action on the myocardium may be relatively longer than that of oxytocin. Therefore, this study aimed to study the cardiovascular effects of using a lower dose of carbetocin (50 mcg) compared to the standard dose (100 mcg) during elective cesarean delivery. MATERIALS AND METHODS: A total of 212 full-term pregnant women were randomized into two groups: group I received 50 mcg of intravenous carbetocin, and group II received 100 mcg of intravenous carbetocin. Heart rate, blood pressure (BP), oxygen saturation, electrocardiogram changes, and pre- and postoperative (12 hours after cesarean delivery) high-sensitivity cardiac troponin I levels were compared between the groups. RESULTS: No statistically significant differences were observed between the groups with respect to heart rate, BP, electrocardiogram changes, or difference in pre- and postoperative high-sensitivity cardiac troponin I levels (p > 0.05). CONCLUSION: Carbetocin's cardiovascular effects were similar in both groups. None of the participants had adverse cardiovascular effects from the drug, and there were no differences in cardiovascular effects between the groups.

An Updated Safety Review of the Relationship Between Atypical Antipsychotic Drugs, the QTc Interval and Torsades de Pointe As: Implications for Clinical Use.

INTRODUCTION: The rising prevalence of psychiatric disorders has resulted in a significant increase in the use of antipsychotic medications. These agents may prolong the corrected QT interval (QTc), running the risk of precipitating ventricular arrhythmias, notably Torsades de Pointes (TdP). Current recommendations vary regarding the optimal approach to safe prescribing practices and QTc surveillance for antipsychotics. This review summarizes the current literature addressing these clinical concerns. AREAS COVERED: The physiologic basis of the QTc interval, mechanisms underlying its susceptibility to pharmacological influence, specific risks associated with atypical antipsychotic agents, and recommendations for safe prescription practices. We performed a literature review using Pubmed and Embase databases, searching for 'antipsychotics' and 'torsades de pointes.' EXPERT OPINION: Finding a safe and universally accepted protocol for prescribing antipsychotics remains a persistent challenge in medicine. Predictive models that integrate clinical history with demographic and ECG characteristics can help estimate an individual's susceptibility to therapy-associated risks, including QTc prolongation. Agents such as ziprasidone and iloperidone are significantly more likely to prolong the QTc interval compared to others such as brexpiprazole, cariprazine, olanzapine, and clozapine. A personalized approach using low-risk medications when clinically feasible, and at the lowest efficacious dose, offers a promising path toward safer antipsychotic prescribing.

Antipsychotic medications are used to treat conditions such as schizophrenia and bipolar disorder; however, they can also affect cardiac electrical conduction. This effect on cardiac function increases the risk of a dangerous heart rhythm, which can potentially be fatal. Patients and doctors need to be aware of and monitor for these potential heart-related side effects, although antipsychotics can be very helpful for mental health conditions.

The influence of mental calculations on brain regions and heart rates.

Performing mathematical calculations is a cognitive activity that can affect biological signals. This study aims to examine the changes in electroencephalogram (EEG) and electrocardiogram (ECG) signals of 36 healthy subjects during the performance of arithmetic tasks. To process EEG signals in different frequency bands, the energy and entropy of entropy (EoE) were extracted from the power spectrum and phase spectrum, respectively. Statistical analysis was conducted to determine meaningful features. These features were sent into support vector machine (SVM) and multi-layer perception (MLP) classifiers to assess their capability in separating math and rest classes. Results indicated the highest classification accuracy of 98.4% for classifying good counters in math and rest state using the MLP method. Based on the majority of features selected for each EEG channel, discriminative brain areas were identified. Analyzing EEG signals proved that math calculation may have multiple influences on various parts of the brain. By comparing good counters' brain activities to those in a resting state, prominent changes were observed in the F4, C4, T4, T5, P3, and O2 areas. However, O1 and O2 channels showed significant changes in the brain of bad counters compared to the resting state. Considering ECG signals also demonstrated that during math calculation the number of heart rates per minute surpasses the rest state. These alterations can occur due to cognitive abilities or emotional processes which were observed to be prominent in subjects who performed more accurate calculations.

ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization.

Objective.Cardiovascular diseases are a major cause of mortality globally, and electrocardiograms (ECGs) are crucial for diagnosing them. Traditionally, ECGs are stored in printed formats. However, these printouts, even when scanned, are incompatible with advanced ECG diagnosis software that require time-series data. Digitizing ECG images is vital for training machine learning models in ECG diagnosis, leveraging the extensive global archives collected over decades. Deep learning models for image processing are promising in this regard, although the lack of clinical ECG archives with reference time-series data is challenging. Data augmentation techniques using realistic generative data models provide a solution.Approach.We introduceECG-Image-Kit, an open-source toolbox for generating synthetic multi-lead ECG images with realistic artifacts from time-series data, aimed at automating the conversion of scanned ECG images to ECG data points. The tool synthesizes ECG images from real time-series data, applying distortions like text artifacts, wrinkles, and creases on a standard ECG paper background.Main results.As a case study, we used ECG-Image-Kit to create a dataset of 21 801 ECG images from the PhysioNet QT database. We developed and trained a combination of a traditional computer vision and deep neural network model on this dataset to convert synthetic images into time-series data for evaluation. We assessed digitization quality by calculating the signal-to-noise ratio and compared clinical parameters like QRS width, RR, and QT intervals recovered from this pipeline, with the ground truth extracted from ECG time-series. The results show that this deep learning pipeline accurately digitizes paper ECGs, maintaining clinical parameters, and highlights a generative approach to digitization.Significance.The toolbox has broad applications, including model development for ECG image digitization and classification. The toolbox currently supports data augmentation for the 2024 PhysioNet Challenge, focusing on digitizing and classifying paper ECG images.

ECG and Atrial Appendage Doppler Discordance Is Common in Patients Undergoing Cardiac Surgery: Prospective Study.

Background: Patients with atrial fibrillation (AF) remain at increased risk of thromboembolism despite apparent maintenance of sinus rhythm with the cause often attributed to periods of asymptomatic AF. Atrial mechanical discordance, with the body of the left atrium (LA) in sinus rhythm and the left atrial appendage (LAA) in AF may also be a contributor. Objectives: The purpose of this study was to assess the frequency of electrocardiogram (ECG) rhythm and LAA and/right atrial appendage (RAA) Doppler ejection phenotype (transesophageal echocardiography [TEE]) discordance in patients undergoing cardiac surgery. Methods: A total of 124 patients undergoing coronary artery bypass graft (CABG), CABG and valve surgery, or isolated valve repair or replacement (valve ± CABG) were prospectively studied. Intraoperative surface ECG rhythm strip and TEE were performed before cardiopulmonary bypass. The ECG and TEE LAA/RAA Doppler spectrum were independently classified as sinus or AF. Results: Of 107 patients (age 65 ± 12 years; 31% female; 65% CABG, 31% valve ± CABG) without a history of AF, 39 (36%) had ECG and LAA and/or RAA discordance (ECG/LAA Doppler discordance, n = 12 [11%]; ECG/RAA Doppler discordance, n = 35 [33%]). There was no significant difference between concordant and discordant groups with regard to age, gender, history of hypertension, diabetes, heart failure, or stroke (all P > 0.05). Conclusions: A large minority of patients without a history of AF undergoing cardiac surgery have ECG/atrial appendage Doppler discordance, a setting that may promote thromboembolism in non-anticoagulated patients. Clinical parameters do not identify patients at increased risk for discordance.

Frequency of Electrocardiogram-Defined Cardiac Conduction Disorders in a Multi-Institutional Primary Care Cohort.

Background: Disorders affecting cardiac conduction are associated with substantial morbidity. Understanding the epidemiology and risk factors for conduction disorders may enable earlier diagnosis and preventive efforts. Objectives: The purpose of this study was to quantify contemporary frequency and risk factors for electrocardiogram (ECG)-defined cardiac conduction disorders in a large multi-institutional primary care sample. Methods: We quantified prevalence and incidence of conduction disorders among adults receiving longitudinal primary care between 2001 and 2019, each with at least one 12-lead ECG performed prior to the start of follow-up and at least one ECG during follow-up. We defined conduction disorders using curated terms extracted from ECG diagnostic statements by cardiologists. We grouped conduction disorders by inferred anatomic location of abnormal conduction. We tested associations between clinical factors and incident conduction disease using multivariable proportional hazards regression. Results: We analyzed 189,163 individuals (median age 55 years; 58% female). The overall prevalence of conduction disorders was 27% among men and 15% among women. Among 119,926 individuals (median age 55 years; 51% female), 6,802 developed an incident conduction system abnormality over a median of 10 years (Q1, Q3: 6, 15 years) of follow-up. Incident conduction disorders were more common in men (8.78 events/1,000 person-years) vs women (4.34 events/1,000 person-years, P < 0.05). In multivariable models, clinical factors including older age (HR: 1.25 per 5-year increase [95% CI: 1.24-1.26]) and myocardial infarction (HR: 1.39 [95% CI: 1.26-1.54]) were associated with incident conduction disorders. Conclusions: Cardiac conduction disorders are common in a primary care population, especially among older individuals with cardiovascular risk factors.

The Great Mimicker: A Unique Case of Diffuse Subarachnoid Haemorrhage Simulating Acute Myocardial Infarction.

Subarachnoid haemorrhage (SAH) is a rare yet consequential medical emergency that may mimic an acute myocardial infarction (MI). SAH causes enhanced sympathetic activity, culminating in the development of neurogenic stunned myocardium (NSM), which presents as ST-segment deviations, prolonged QT intervals, T-wave inversions or Q-waves. Reperfusion therapy is contraindicated for SAH because of an increased risk of bleeding and death. Therefore, a prompt diagnosis is crucial. Here, we report a unique case of massive SAH presenting as diffuse ST-segment deviation simulating an acute MI. Our patient was brought to the emergency department after a cardiac arrest and died on day 2 of admission. LEARNING POINTS: Subarachnoid haemorrhage (SAH) can present with EKG changes and significant rise in troponin, mimicking acute coronary syndrome.SAH should be included in the differential diagnosis for patients presenting with neurological symptoms, ST-segment deviations or prolonged QT intervals.Misdiagnosis of SAH as acute coronary syndrome can lead to inappropriate use of anticoagulants or delays in necessary neurological interventions.

Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation.

BACKGROUND: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence after ablation. This study aims to propose a novel algorithm based on Transformer using surface electrocardiogram (ECG) signals and clinical features can predict AF recurrence. METHODS: Between October 2018 to December 2021, patients who underwent index radiofrequency ablation for AF with at least one standard 10-second surface ECG during sinus rhythm were enrolled. An end-to-end deep learning framework based on Transformer and a fusion module was used to predict AF recurrence using ECG and clinical features. Model performance was evaluated using areas under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy and F1-score. RESULTS: A total of 920 patients (median age 61 [IQR 14] years, 66.3% male) were included. After a median follow-up of 24 months, 253 patients (27.5%) experienced AF recurrence. A single deep learning enabled ECG signals identified AF recurrence with an AUROC of 0.769, sensitivity of 75.5%, specificity of 61.1%, F1 score of 55.6% and overall accuracy of 65.2%. Combining ECG signals and clinical features increased the AUROC to 0.899, sensitivity to 81.1%, specificity to 81.7%, F1 score to 71.7%, and overall accuracy to 81.5%. CONCLUSIONS: The Transformer algorithm demonstrated excellent performance in predicting AF recurrence. Integrating ECG and clinical features enhanced the models' performance and may help identify patients at low risk for AF recurrence after index ablation.

Taurine and enzymatically modified isoquercitrin protected against methotrexate-induced deteriorations in the conductivity and rhythmicity of the heart in rats: Antioxidant, anti-inflammatory, and histological architecture approach.

Cardiotoxicity is one of the most devastating complications of cancer treatment by methotrexate (MTX). The present study aimed to investigate the potential anti-cardiotoxic efficacy of taurine (Tau) and enzymatically modified isoquercitrin (EMIQ) alone or combined against MTX-induced cardiotoxicity in adult male rats. A total of 36 rats were randomly divided into six groups (six animals each): control, MTX (a single i.p. dose of 20 mg/kg), EMIQ + MTX (26 mg/kg of EMIQ, p.o. for 16 days), Tau + MTX (500 mg/kg of Tau, p.o. for 16 days), EMIQ + Tau + MTX at the same previous doses, and (EMIQ + Tau)½ + MTX. MTX reduced the percentage of body weight change, the expression of dihydrofolate reductase (DHFR) and folypolyglutamyl synthetase (FPGS), the cleaved tumor necrosis factor alpha (TNF-α) level in the cardiac tissue, and the elevated serum TNF-α level. MTX extensively deteriorated the electrocardiography (ECG), inducing tachycardia with shortening of the time intervals between successive heartbeats (R-R interval), associated with elongation of ventricular depolarization (QRS interval), and the corrected total time for ventricular de- and repolarization (QTc) duration. Treatment with MTX resulted in a significant reduction in atrial depolarization (P amplitude) and rapid repolarization (T amplitude) and a significant elevation in plateau phase (ST height). MTX treatment resulted in swelling of cardiomyocytes with extensive vacuolization of sarcoplasm with numerous variably sized vacuoles in addition to apoptotic cells. Tau and EMIQ protected against MTX-induced deteriorations in the conductivity and rhythmicity of the heart through antioxidative, anti-inflammatory, and antiapoptotic activities. Treatment with tau and EMIQ combined at high or low doses offered superior protection to the heart than using each agent alone.

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. Artificial Intelligence (AI) has demonstrated the capability to identify ePAP and its association with hospitalization due to heart failure when analyzing chest X-rays (CXR). An AI model based on electrocardiograms (ECG) has shown promise in not only detecting ePAP but also in predicting future risks related to cardiovascular mortality. We aimed to develop an AI model integrating ECG and CXR to detect ePAP and evaluate their performance. We developed a deep-learning model (DLM) using paired ECG and CXR to detect ePAP (systolic pulmonary artery pressure > 50 mmHg in transthoracic echocardiography). This model was further validated in a community hospital. Additionally, our DLM was evaluated for its ability to predict future occurrences of left ventricular dysfunction (LVD, ejection fraction < 35%) and cardiovascular mortality. The AUCs for detecting ePAP were as follows: 0.8261 with ECG (sensitivity 76.6%, specificity 74.5%), 0.8525 with CXR (sensitivity 82.8%, specificity 72.7%), and 0.8644 with a combination of both (sensitivity 78.6%, specificity 79.2%) in the internal dataset. In the external validation dataset, the AUCs for ePAP detection were 0.8348 with ECG, 0.8605 with CXR, and 0.8734 with the combination. Furthermore, using the combination of ECGs and CXR, the negative predictive value (NPV) was 98% in the internal dataset and 98.1% in the external dataset. Patients with ePAP detected by the DLM using combination had a higher risk of new-onset LVD with a hazard ratio (HR) of 4.51 (95% CI: 3.54-5.76) in the internal dataset and cardiovascular mortality with a HR of 6.08 (95% CI: 4.66-7.95). Similar results were seen in the external validation dataset. The DLM, integrating ECG and CXR, effectively detected ePAP with a strong NPV and forecasted future risks of developing LVD and cardiovascular mortality. This model has the potential to expedite the early identification of pulmonary hypertension in patients, prompting further evaluation through echocardiography and, when necessary, right heart catheterization (RHC), potentially resulting in enhanced cardiovascular outcomes.

Apical Acute Myocardial Infarction Due to Occluded Posterior Descending Branch of Right Coronary Artery Concomitant With Short Left Anterior Descending Artery: Multi-imaging Modality Assessment.

Regional wall motion abnormality in the left ventricular (LV) apex detected on transthoracic echocardiography is commonly interpreted as the presence of a distal left anterior descending (LAD) artery lesion in clinical practice. Herein, we reported a rare case of apical acute myocardial infarction (AMI) caused by an occluded posterior descending branch of the right coronary artery (RCA), in which the correspondence between coronary arterial anatomy and supplied LV apex was evaluated by multi-imaging modalities. Despite the presence of regional wall motion abnormality in the LV apex, left coronary angiography showed no significant coronary artery diseases. It was of note that LAD terminated before the LV apex. Right coronary angiography showed total occlusion of the posterior descending branch. Cardiac computed tomography (CT) clearly demonstrated that the spontaneously recanalized posterior descending branch extended toward the LV apex. Cardiac magnetic resonance imaging (MRI) clearly revealed regional wall motion abnormality corresponding to myocardial edema in the LV apex. Cardiac CT and MRI were powerful tools in clarifying the correspondence between coronary arterial anatomy and supplied LV apex. Clinicians should be aware that localized apical AMI can occur under the condition of occluded posterior descending branch of RCA concomitant with short LAD.