Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F₁-Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F₁-Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F₁-Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources.

• Mentees are more satisfied with their mentorship experience when they have had more than 3 mentors or a mentor from outside of their practice/institution. • Satisfaction with the mentoring relationship is significantly associated with perceived satisfaction in achieving professional goals. • Sex and race/ethnicity concordance in mentoring relationships is associated with positive outcomes. • Characteristics that mentees desire in a mentor tend to change with time/career stage.

Objective: Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. Methods: We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai East Hospital. The dataset is partitioned into training (80%), validation (10%), and test (10%) cohorts for comprehensive evaluation. Each cohort contains ECGs from distinct patients, considering some patients took repeated ECG measurements. The proposed algorithm is evaluated in two levels, self-performance measurement and comparison with the residual neural network classification model. Two scores are used for self-performance measurement, including Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE). To compare the performance of the proposed model with the residual neural network model, we assessed the F1 score and area under the receiver operating characteristic curve (AUC). Results: We have observed promising performance metrics across multiple evaluation criteria through an extensive evaluation of a large 12-lead ECG database comprising 1,128,553 ECG readings from 754,920 patients. The CardioGPT model exhibited high BLEU and ROUGE scores with 0.68 (95% CI: 0.66, 0.71) and 0.81 (95% CI: 0.79, 0.84). Furthermore, in the classification performance measurement setting, the CardioGPT achieved an average F1-score of 0.91(95% CI: 0.89, 0.93) and AUC of 0.82(95% CI: 0.79, 0.84) and has higher scores than that of the convolutional neural network model, indicating its proficiency in accurately classifying ECG recordings. Conclusion: By leveraging the power of transformer structure model and natural language processing, the GPT model addresses the challenge of imbalanced learning commonly encountered in ECG classification tasks. The results indicate that the GPT model can accurately interpret ECG using natural language, providing valuable insights into the underlying patterns and abnormalities present in the data. Significance: The pioneering application of the GPT model for interpreting ECGs with natural language demonstrates its potential to address ECG classification challenges and offer valuable insights into cardiac health.

The demands and hazards of working in the cardiac catheterization laboratory (CCL) are well-represented in the literature and include radiation exposure, orthopedic injuries, and physical and psychological stress. This analysis aimed to compare occupational hazards in the CCL between 2014 and 2023, focusing on orthopedic injuries, cancer diagnoses, and pregnancy-related issues. Society for Cardiovascular Angiography & Interventions members were invited to complete an online survey regarding occupational hazards in the CCL from May to August 2023. This survey included questions about demographics, caseload, self-reported injuries, malignancies, radiation and orthopedic protection, and pregnancy. The 2023 survey had an 8% response rate and included 296 respondents (91% interventional cardiologists, 7% fellows-in-training, and 2% interventional echocardiographers or invasive noninterventional cardiologists). The majority of respondents were men (83%), 30 to 50 years old (61%), and in practice for 5 to 15 years (39%). The 2023 survey reported orthopedic injuries and lifetime cancer in 59.8% and 6.1% of respondents, respectively, vs 49.4% and 4.5% reported in the 2014 survey. Among women respondents, 28% reported being discouraged from working in the CCL due to pregnancy or considering pregnancy, and 71% wanted the option to step away from the CCL during pregnancy. A high prevalence of orthopedic injuries and cancer diagnoses was reported in a 2023 survey of Society for Cardiovascular Angiography & Interventions members working in the CCL. It is imperative to promote the widespread use of radiation protective gear to decrease radiation exposure and encourage utilization of newer radiation protection systems that can minimize the use of lead aprons, a major contributor to orthopedic injuries. Attention to effective measures for the safety of CCL personnel is a critically important goal in interventional cardiology.