Undiagnosed phenomena such as long QT syndrome can have devastating effects on patients. Our case, involving a woman in her 30s, highlights the serious effects of undiagnosed long QT and how antiemetic medications can precipitate cardiac events that can lead to fatalities. Various medications are known to prolong QT intervals, and clinicians must be aware of the side effects of some of these commonly used medications. While survival was achieved in this case, education and reflection can act as a tool to help improve global standards of care in this subgroup of the population.

BACKGROUND: Our investigation aimed to determine how the diverse backgrounds and medical specialties of emergency physicians (Eps) influence the accuracy of diagnoses and the subsequent treatment pathways for patients presenting preclinically with MI symptoms. By scrutinizing the relationships between EPs\' specialties and their approaches to patient care, we aimed to unveil potential variances in diagnostic accuracy and treatment choices.
METHODS: In this retrospective, monocenter cohort study, we leveraged machine learning techniques to analyze a comprehensive dataset of 2328 patients with suspected MI, encompassing preclinical diagnoses, electrocardiogram (ECG) interpretations, and subsequent treatment strategies by attending EPs.
RESULTS: We demonstrated that diagnosis and treatment patterns of different specialties were distinct enough, that machine learning (ML) was able to differentiate between specialties (maximum area under the receiver operating characteristic = 0.80 for general medicine and 0.80 for surgery). In our study, internist demonstrated the highest accuracy for preclinical identification of STEMI (0.96) whereas surgeons showed the highest accuracy for identifying NSTEMI. Our findings highlight significant correlations between EP specialties and the accuracy of both preclinical diagnoses and subsequent treatment pathways for patients with suspected MI.
CONCLUSIONS: Our results offer valuable insights into how the diverse backgrounds and specialties of EPs can influence the optimization of patient care in emergency settings. Understanding these patterns can help in the development of tailored training programs and protocols to enhance diagnostic accuracy and treatment efficacy in emergency cardiac care, ultimately optimizing patient treatment and improving outcomes.

Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI\'s significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

This study evaluated the capabilities of the newly released ChatGPT-4V, a large language model with visual recognition abilities, in interpreting electrocardiogram waveforms and answering related multiple-choice questions for assisting with cardiovascular care.

Emergency Medicine physicians experience a significant number of interruptions throughout their work day. One common cause of interruptions is the immediate interpretation of triage electrocardiograms (ECGs). Recent studies have suggested that ECGs interpreted as normal via automated analysis by the ECG machine rarely require urgent cardiac intervention and suggested that providers may not have to be interrupted to interpret these \"normal\" ECGs. We describe the case of a patient who presented to the Emergency Department (ED) with chest pain and an ECG interpreted as normal by an automated reading from the ECG machine, despite having acute coronary syndrome requiring emergent intervention.

OBJECTIVE: Interpreting an electrocardiogram (ECG) is a vital skill for nurses in cardiology. This study aimed to evaluate the efficacy of the bridge-in, objective, preassessment, participatory learning, post-assessment, and summary (BOPPPS) model, when combined with case-based learning (CBL), in enhancing nursing students\' ECG interpretation capabilities.
METHODS: Nursing students were randomly divided into two groups: one utilizing the BOPPPS model combined with CBL (BOPPPS-CBL), and the other employing a traditional lecture-based learning (LBL) model. All participants underwent training and completed pre- and post-course quizzes.
RESULTS: The BOPPPS-CBL model significantly improved nursing students\' abilities in ECG interpretation compared to the traditional LBL model group. The BOPPPS-CBL model proved to be a comprehensive and effective method for enhancing students\' attitudes towards teaching and learning.
CONCLUSIONS: Our study demonstrated for the first time that the BOPPPS-CBL model is an innovative and effective method for promoting nurses\' accuracy in ECG interpretation. It highlights the potential of this approach as a superior alternative to traditional learning methods.

BACKGROUND: Learning by concordance (LbC) is a recent approach that introduces learners to the complexity and uncertainty of clinical practice. Some data on LbC suggest that it stimulates reflection in future clinicians. We developed an online LbC training program on electrocardiogram (ECG) interpretation in general practice at the University of Strasbourg, France, and conducted an exploratory qualitative study to document the impact of this ECG learning-by-concordance training program on reflection in participants.
METHODS: We created 18 clinical vignettes on ECG interpretation based on a review of the literature on frequent and serious cardiovascular diseases that can be identified using an ECG in general practice. The training program was delivered online to postgraduate general practice students in two faculties of medicine. We conducted a qualitative study based on thematic analysis of two focus groups and six individual interviews. Inductive and deductive coding were performed. The five major components of reflection in the Nguyen model were used in the deductive coding: (i) thoughts and actions, (ii) attentive, critical, exploratory, and iterative processes (ACEI), (iii) underlying conceptual frame, (iv) change and (v) self.
RESULTS: Two focus groups and six individual interviews were conducted. The qualitative analysis indicated 203 codes in the focus groups and 206 codes in the individual interviews, which were divided into five groups based on the components of reflection in the Nguyen model: (i) the self; (ii) attentive, critical, exploratory, and iterative interactions with (iii) one\'s thoughts and actions; and (iv) a view on both the change itself and (v) the underlying conceptual frame. Inductive coding revealed interesting insights into the impact of the identity of the panel members, the absence of a scoring system and the question of uncertainty in ECG reading.
CONCLUSIONS: This study supports the claim that the use of LbC in the context of ECG interpretation could foster reflection in future general practitioners. We discuss future research avenues on instructional design of LbC and reflection.

UNASSIGNED: The series of electrocardiograms (ECGs) can help track cardiac abnormalities in patients\' conditions and make an earlier clinical decision. It is crucial for nurses working in critical care environments to acquire ECG knowledge for effective ECG monitoring and act accordingly in case of a change in patient condition. This study aimed at investigating intensive care unit (ICU) nurses\' knowledge and attitude towards ECG interpretation in Fujian province, China. The study also analyzed the relationship between participants\' demographic characteristics and level of ECG knowledge.
UNASSIGNED: This study was done online at twenty-one hospitals in Fujian province using a quantitative cross-sectional design involving 357 registered nurses working in the ICU between October and December 2021. The selection of hospitals and potential participants involved purposive and convenient sampling methods, respectively. Binary logistic regression was carried out to determine factors that predict ICU nurses\' knowledge of ECG interpretation, and a p-value <0.05 was deemed statistically significant.
UNASSIGNED: The majority of nurses (70.9%) demonstrated a low level of ECG knowledge. The mean score for ECG knowledge was 5.95 (SD = 2.14), with only 0.8% of ICU nurses answering all questions correctly. The majority portrayed positive attitude towards ECG interpretation; however, more than half (61.6%) believed that nurses should rely on a doctor\'s opinion about ECG interpretation. Previous ECG training (AOR = 3.98, 95% CI: 2.12-7.45); frequency of ECG interpretation in comparison with no frequency of ECG interpretation (1-3 times per day: AOR = 15.55, 95% CI: 6.33-38.18; 1-3 times per week: AOR = 18.10, 95% CI: 6.38-51.34); and current working unit in comparison to those working in cardiac ICU (general ICU: AOR = 0.45, 95% CI: 0.21-0.94; medical ICU; AOR = 0.28, 95% CI: 0.12-0.67; and surgical ICU; AOR = 0.05, 95% CI: 0.01-0.43) remained statistically significant after adjusting for confounders.
UNASSIGNED: The present study revealed a low level of knowledge about ECG interpretation among ICU nurses. Although the participants demonstrated positive attitudes toward ECG interpretation, the negative attitude still existed. Nurses should acknowledge ECG interpretation as part of their duties and responsibilities in nursing care instead of merely relying on doctors\' opinions.

Our objective was to determine characteristics of electrocardiograms (ECG) that predict ventricular fibrillation (VF) among prehospital patients with suspected ST-segment elevation myocardial infarction (STEMI) in Québec.
We performed a matched case-control study of prehospital adult suspected with STEMI. Patients in case group (STEMI/VF+) were matched with controls (STEMI/VF-) for age and sex and then compared for ECG characteristics, including ST-segment elevations (STE) and depressions (STD), duration of interval complexes, general characteristics, and several calculated variables. Logistic regression was used to measure the association between ECG characteristics and VF development.
Overall, 310 prehospital patients with suspected STEMI were included in the analysis (case group, n = 155; control group, n = 155). We confirmed that the presence of TW-pattern complex (OR 7.0, 95% CI 1.55-31.58), premature ventricular contraction (PVC) (OR 5.5, 95% CI 2.04-14.82), and STE in V2-V6 (OR 3.8, 95% CI 1.21-11.74) were electrocardiographic predictors of VF. We also observed that STD in V3-V5 (OR 6.5, 95% CI 1.42-29.39), atrial fibrillation (AF) ≥ 100 beats per minute (bpm) (OR 6.3, 95% CI 1.80-21.90), the combination of STE in V4 and V5, and STD in II, III and aVF (OR 4.8, 95% CI 1.01-22.35), and the presence of STD in ≥ 6 leads (OR 4.2, 95% CI 1.33-13.13) were also associated with VF development. Finally, simultaneous association of 2 (OR 2.3, 95% CI 1.13-4.06) and 3 (OR 11.6, 95% CI 3.22-41.66) predictors showed significant association with VF.
In addition to some already known predictors, we have identified several ECG findings associated with the development of VF in patients with suspected STEMI. Early identification of patients with STEMI at increased risk of VF should help EMS providers anticipate adverse events and encourage use of defibrillation pads.
RéSUMé: OBJECTIF: Notre objectif était de déterminer les caractéristiques des électrocardiogrammes (ECG) qui prédisent la fibrillation ventriculaire (FV) chez les patients préhospitaliers suspectés d’infarctus du myocarde à élévation du segment ST (STEMI) au Québec. MéTHODES: Nous avons effectué une étude cas-témoin appariée de l’adulte préhospitalier suspecté avec STEMI. Les patients du groupe de cas (STEMI/VF+) ont été appariés avec les témoins (STEMI/VF-) pour l’âge et le sexe, puis comparés pour les caractéristiques ECG, y compris les élévations du segment ST (STE) et les dépressions (STD), la durée des complexes d’intervalles, les caractéristiques générales et plusieurs variables calculées. La régression logistique a été utilisée pour mesurer l’association entre les caractéristiques de l’ECG et le développement de la FV. RéSULTATS: Dans l’ensemble, 310 patients préhospitaliers présentant un STEMI suspecté ont été inclus dans l’analyse (groupe de cas, n = 155; groupe témoin, n = 155). Nous avons confirmé que la présence de complexes TW (OR 7,0, IC à 95% 1,55–31,58), de contraction ventriculaire prématurée (PVC) (OR 5,5, IC à 95% 2,04–14,82) et de STE dans V2–V6 (OR 3,8, IC à 95% 1,21–11,74) étaient des prédicteurs électrocardiographiques de la FV. Nous avons également observé que STD dans V3-V5 (OR 6,5, IC à 95% 1,42–29,39), fibrillation auriculaire (AF) 100 battements par minute (bpm) (OR 6,3, IC à 95% 1,80–21,90), la combinaison de STE dans V4 et V5, et STD dans II, III et aVF (OR 4,8, IC à 95% 1,01–22,35) et la présence de STD dans 6 dérivations (OR 4.2, IC à 95% 1.33–13.13) ont également été associés au développement de la FV. Enfin, l’association simultanée de 2 (OR 2,3, IC à 95% 1,13–4,06) et 3 (OR 11,6, IC à 95% 3,22–41,66) prédicteurs a montré une association significative avec la FV. CONCLUSIONS: En plus de certains prédicteurs déjà connus, nous avons identifié plusieurs résultats d’ECG associés au développement de la FV chez des patients présentant une STEMI suspectée. L’identification précoce des patients atteints de STEMI à risque accru de FV devrait aider les fournisseurs de soins médicaux d’urgence à anticiper les événements indésirables et à encourager l’utilisation de tampons de défibrillation.

The interpretation of electrocardiograms (ECGs) involves a dynamic interplay between computerized ECG interpretation (CEI) software and human overread. However, the impact of computer ECG interpretation on the performance of healthcare professionals remains largely unexplored. The aim of this study was to evaluate the interpretation proficiency of various medical professional groups, with and without access to the CEI report. Healthcare professionals from diverse disciplines, training levels, and countries sequentially interpreted 60 standard 12-lead ECGs, demonstrating both urgent and nonurgent findings. The interpretation process consisted of 2 phases. In the first phase, participants interpreted 30 ECGs with clinical statements. In the second phase, the same 30 ECGs and clinical statements were randomized and accompanied by a CEI report. Diagnostic performance was evaluated based on interpretation accuracy, time per ECG (in seconds [s]), and self-reported confidence (rated 0 [not confident], 1 [somewhat confident], or 2 [confident]). A total of 892 participants from various medical professional groups participated in the study. This cohort included 44 (4.9%) primary care physicians, 123 (13.8%) cardiology fellows-in-training, 259 (29.0%) resident physicians, 137 (15.4%) medical students, 56 (6.3%) advanced practice providers, 82 (9.2%) nurses, and 191 (21.4%) allied health professionals. The inclusion of the CEI was associated with a significant improvement in interpretation accuracy by 15.1% (95% confidence interval, 14.3-16.0; P < 0.001), decrease in interpretation time by 52 s (-56 to -48; P < 0.001), and increase in confidence by 0.06 (0.03-0.09; P = 0.003). Improvement in interpretation accuracy was seen across all professional subgroups, including primary care physicians by 12.9% (9.4-16.3; P = 0.003), cardiology fellows-in-training by 10.9% (9.1-12.7; P < 0.001), resident physicians by 14.4% (13.0-15.8; P < 0.001), medical students by 19.9% (16.8-23.0; P < 0.001), advanced practice providers by 17.1% (13.3-21.0; P < 0.001), nurses by 16.2% (13.4-18.9; P < 0.001), allied health professionals by 15% (13.4-16.6; P < 0.001), physicians by 13.2% (12.2-14.3; P < 0.001), and nonphysicians by 15.6% (14.3-17.0; P < 0.001).CEI integration improves ECG interpretation accuracy, efficiency, and confidence among healthcare professionals.