Citations

Background: The impact of computer ECG interpretation (CEI) on medical professionals’ performance is understudied. This study aims to assess the interpretation proficiency of diverse medical professionals with and without access to CEI reports. Methods: Medical professionals from diverse backgrounds interpreted 60 12-lead ECGs with urgent and non-urgent findings. The interpretation process involved two interpretation phases: (i) 30 ECGs with clinical statements and (ii) the same 30 ECGs and clinical statements in random order along with a CEI report. Diagnostic performance was evaluated based on interpretation accuracy, time per ECG, and self-reported confidence (rated 0 [not confident], 1 [somewhat confident], or 2 [confident]). Results: Among 892 participants, there were 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 significantly improved interpretation accuracy by 15.1% (95% CI, 14.3 to 16.0; P<0.001), reduced interpretation time by 52 seconds (95% CI, -56 to -48; P<0.001), and increased confidence by 0.06 (95% CI, 0.03 to 0.09; P=0.003) (Table 1) . Conclusions: CEI integration improves ECG interpretation accuracy, efficiency, and confidence across medical professionals. Nevertheless, even with CEI, significant gaps in ECG interpretation proficiency remain.

Background: Accurate ECG interpretation is vital, but variations in skills exist among healthcare professionals. This study aims to identify factors contributing to ECG interpretation proficiency. Methods: Survey data and ECG test scores from participants in the EDUCATE Trial were analyzed. The dependent variable was the test score for interpreting 30 12-lead ECGs. Independent variables included non-modifiable factors (physician status, clinical experience, patient care impact) and modifiable factors (weekly interpretations, training hours, expert supervision frequency). Bivariate and multivariate analyses generated the Comprehensive Model (all factors) and Actionable Model (modifiable factors only). Results: Among 1206 participants, there were 72 (6.0%) primary care physicians, 146 (12.1%) cardiology fellows-in-training, 353 (29.3%) resident physicians, 182 (15.1%) medical students, 84 (7.0%) advanced practice providers, 120 (9.9%) nurses, and 249 (20.7%) allied health professionals. Physicians accounted for 571 (47.3%) of participants, while 453 (37.6%) were non-physicians. Bivariate analysis showed associations between test scores and multiple variables (Table 1) . In the Comprehensive Model, scores were independently associated with weekly interpretations (9.9 score increase; 95% CI 7.9-11.8; P<0.001), physician status (9.0 score increase; 95% CI 7.2-10.8; P<0.001), and training hours (5.7 score increase; 95% CI 3.7-7.6; P<0.001). In the Actionable Model, scores were independently associated with weekly interpretations (12.0 score increase; 95% CI, 10.0-14.0; P<0.001) and training hours (4.7 score increase; 95% CI 2.6-6.7; P<0.001). The Comprehensive and Actionable Models accounted for 18.7% and 12.3% of the variance in test scores, respectively. Conclusions: Being a physician is a non-modifiable predictor, while training and regular practice are modifiable predictors of ECG interpretation performance.

Background: Gaps in ECG interpretation competency among medical professionals exist, with a need for practical, evidence-based, and accessible learning solutions. We conducted an international, prospective, randomized controlled trial to assess the effectiveness of web-based, self-directed ECG learning. Methods: A total of 1206 diverse medical learners and professionals underwent an initial test, followed by random assignment to one of four groups: (i) online question-bank (questions), (ii) online lectures (lectures), (iii) online, question-bank and lectures (hybrid), or (iv) no resources (control). After 4 months, a post-test was administered to evaluate the overall change in performance. Results: Of all participants, 863 (72%) completed the trial. After the 4-month follow-up, all three online, self-directed intervention groups (questions, lectures, and hybrid) showed significant improvement in overall performance (P<0.001), while the control group did not (P=0.544). The questions, lectures, and hybrid interventions demonstrated score improvements of +11.4% (95% CI, 9.1-13.7; P<0.001), +9.8% (95% CI, 7.8-11.9; P<0.001), and +11.0% (95% CI, 9.2-12.9; P<0.001) respectively, compared to +0.8% (95% CI, -1.2-2.8; P=0.544) for the control group. These improvements were consistent across medical professional groups (Figure 1) . Conclusions: Web-based, self-directed learning resources effectively improved ECG interpretation proficiency for diverse medical learners and professionals.

Background: ECG interpretation is crucial in medical practice, but professionals struggle with achieving and maintaining competency. Identifying proficiency gaps aids in designing educational interventions. Methods: Medical professionals from various disciplines interpreted 30 12-lead ECGs containing commonly taught urgent and non-urgent findings. Performance metrics evaluated were overall adjusted score (% of correctly identified findings using a point-value score based on clinical relevance), unadjusted score (% of correctly identified findings), interpretation time per ECG, and self-reported confidence (rated on an ordinal scale of 0 [not confident], 1 [somewhat confident], or 2 [confident]). Results: Among 1206 participants, there were 72 (6%) primary care physicians (PCPs), 146 (12%) cardiology fellows-in-training (FIT), 353 (29%) resident physicians, 182 (15%) medical students, 84 (7%) advanced practice providers (APPs), 120 (10%) nurses, 249 (21%) allied health professionals, 571 (47%) physicians, and 453 (38%) non-physicians. Participants had a mean adjusted score of 46.9% (± 15.9%), unadjusted score of 56.4% (± 17.2%), interpretation time of 142 seconds (± 67 seconds), and confidence of 0.83 (± 0.53) (Table 1) . Performance varied across groups; cardiology FIT had superior performance in all metrics. Physicians generally outperformed non-physicians, with higher overall adjusted score (52% vs. 43%; p<0.01), unadjusted score (62% vs. 52%; p<0.01), and confidence (0.91 vs. 0.80; p<0.01). Unadjusted score for PCPs was higher than nurses and APPs (58% vs. 47% and 51%; p<0.01) but lower than resident physicians (58% vs. 60%; p<0.01). Allied health professionals outperformed nurses and APPs and closely matched resident physicians and PCPs. Conclusions: Significant gaps in ECG interpretation proficiency exist, emphasizing the need for comprehensive, scalable, and accessible educational tools.

Introduction: Aging and inflammation are associated with risk of atrial fibrillation (AF), to which clonal hematopoiesis of indeterminate potential (CHIP) may contribute. We investigated whether CHIP was associated with AF, inflammatory and cardiac biomarkers and cardiac structural changes. Methods: We ascertained CHIP (variant allele frequency, VAF ≥2%) in the Atherosclerosis Risk in Community Study (ARIC visit-5, n = 4,131) and the UK Biobank (UKBB, n = 195,851) participants. Using multivariable Cox regression models, we studied the association of CHIP and the top three implicated genes (i.e., DNMT3A , TET2 , ASXL1 ) with incident AF and conducted a meta-analysis on both cohorts. We also ascertained high-sensitivity C-reactive protein (hs-CRP), interleukin 6 (IL6), IL18, high-sensitivity troponin T and I (hs-TnT and hs-TnI), NT-proBNP, and echocardiography indices in ARIC. We first standardized the natural log of all markers (st-ln) and used the adjusted quantile regression to compare the median in those with and without CHIP. Results: In ARIC, the mean ± SD age was 75.8 ± 5.2 years, 41% were men, 23% were Black, 25% had CHIP, and 11.6% had large CHIP (VAF ≥10%). These measures in UKBB were 56.4 ± 8.1 years, 45%, 2%, 5.8%, and 2.6%, respectively. In the meta-analysis, compared to those without CHIP, the hazard ratios (95% confidence interval) for AF were 1.12 (1.01 - 1.25, P =0.035), 1.31 (1.07 - 1.60, P =0.009), and 1.48 (1.05 - 2.08, P =0.026) for Large-CHIP, Large- TET2, and Large- ASXL1 , respectively. hs-CRP and IL18 levels were similar in those with and without CHIP as well as gene-specific subgroups. Large- TET2 and ASXL1 were associated with higher IL6, which was separately associated with AF. Additionally, Large- ASXL1 was associated with increased left ventricle mass index and NT-proBNP (coefficients in Figure 1 ). Conclusion: Large TET2 and ASXL1 but not DNMT3A CHIP is associated with higher IL6 levels, indices of cardiac remodeling, and an increased risk of AF.

Introduction: Previous studies have shown inconsistent changes in the rates of adverse cardiovascular events after daylight savings time (DST) transitions. In this large-scale, U.S.-based nationwide study, we sought to investigate the association of DST transitions with the rates of adverse cardiovascular events. Methods: The study cohort included 36,116,951 unique individuals from de-identified administrative claims data of the OptumLabs Data Warehouse®. There were 74,722 total adverse cardiovascular events during the week of DST transition and the control weeks (two weeks prior and after) in spring and autumn of 2015-2019. We used Bayesian hierarchical Poisson regression models to estimate the event rate ratios (ERR) representing the ratio of composite adverse cardiovascular event rates (acute myocardial infarction, stroke, cardiogenic shock, cardiac arrest, and sudden death) between DST transition weeks and control weeks. Results: Our results showed an average increase of 3% [95% uncertainty interval; -3% to 10%] and 4% [-2% to 12%] in the rate of adverse cardiovascular events during Monday and Friday of the spring DST transition week, respectively. The probability of spring DST transition being associated with a moderate-to-large increase in the event rates (ERR>1.10) was estimated to be <6% for Monday and Friday, and <1% for the remaining days. During autumn DST transition, the probability of any decrease in adverse cardiovascular event rates was estimated to be <46% and a moderate-to-large decrease in the event rates to be <4% across all days of the week. Conclusions: Spring DST transition week had a suggestive association with a minor increase in adverse cardiovascular event rates on Monday and Friday. The probability of this association to be of clinically meaningful magnitude was estimated to be very low. We did not observe any evidence suggestive of a change in adverse cardiovascular event rates immediately following autumn DST transition. Taken together, our findings, based on a large, diverse cohort from across the United States, suggest that DST transitions are unlikely to significantly impact the rate of cardiovascular events.

Introduction: Biomarkers can facilitate the prediction of events in primary prevention. Few studies have been conducted in the community setting with analyses that incorporate many biomarkers. Hypothesis: We tested whether a multi biomarker model would outperform clinical factors and individual biomarkers for predicting MACE and Stroke/MI in subjects without known CAD. Methods: A retrospective cohort study assessed composite outcomes MACE (MI, CABG, PCI, stroke, and death) and Stroke/MI. Cox proportional hazard models were fit for individual biomarkers, adjusted for age, gender, BMI, hypertension, CAD, and GFR. Multi biomarker models were developed based on AIC values from the single-biomarker models. Bootstrap tests compared survival C-statistics and relative Integrated Discrimination Index (IDI). Results: 1131 patients in Olmsted County, MN were studied. Median age was 63.1 years, 97.5% were Caucasian, and 52.2% were female. Median BMI was 27.8. Kaplan Meier rates of MACE and Stroke/MI were 29.2% (CI = 26.5-31.8%) and 12.3% (CI = 10.3% - 14.3%), at 10 years. Adjusted NT proBNP models showed a 31% increased risk of MACE/death (HR = 1.31; CI = 1.17-1.46) and a 31% increased risk for stroke/MI (HR = 1.31; CI = 1.08-1.58). Adjusted Ceramide score models showed a 13% increased risk of MACE/death (HR = 1.13; CI = 1.04-1.24) and a 29% increased risk for stroke/MI (HR = 1.29; CI = 1.11-1.51). NT proBNP and the Ceramide score with clinical factors was better than clinical factors alone for MACE/Death (Relative IDI = 29.7%; p = 0.003) and Stroke/MI (Relative IDI = 36.1%; p = 0.034). This combined model did not outperform the strongest single biomarker model for Stroke/MI or MACE/Death. Other biomarkers were not significant when added to the combined model. Discussion: Ceramide score and NT proBNP alone and together improve the prediction of MACE and Stroke/MI in a community primary prevention cohort.

Although there is consensus on the management of patients with Brugada Syndrome with high risk for sudden cardiac arrest, asymptomatic or intermediate-risk patients present clinical management challenges. This document explores the management opinions of experts throughout the world for patients with Brugada Syndrome who do not fit guideline recommendations. Four real-world clinical scenarios were presented with commentary from small expert groups for each case. All authors voted on case-specific questions to evaluate the level of consensus among the entire group in nuanced diagnostic and management decisions relevant to each case. Points of agreement, points of controversy, and gaps in knowledge are highlighted.