Citations

Background: Atrial septal defects (ASD) and ventricular septal defects (VSD) are among the most common forms of congenital heart disease (CHD). Early detection and intervention for these defects are crucial to prevent complications. Given advancements in AI technology, the development of AI-enabled ECG models for detecting these defects is of significant interest. Aims: This study aimed to create AI models using pediatric ECG data for the detection of ASD, VSD, and a combined model detecting either ASD or VSD. Methods: We analyzed a dataset of 7,795 pediatric patients from Mayo Clinic who had undergone echocardiography between 2002 and 2022, with corresponding 10-second, 12-lead surface ECGs taken within 14 days of echocardiography. This dataset included 259 patients with ASDs and 139 patients with VSDs (single or multiple). The control group was comprised of 7,397 patients without significant or complex CHD. For cases and controls, we included patients with all forms of electrocardiographic abnormalities and those with valvular lesions who had less than moderate stenosis or regurgitation. The dataset was divided into training, validation, and testing subsets to develop convolutional neural network (CNN) models for ASD, VSD, and either ASD or VSD detection. Results: Our ASD and VSD detection models achieved test AUCs of 0.82 (95% CI 0.76, 0.88) and 0.80 (95% CI 0.70, 0.90), respectively. Meanwhile, a combined model that detected either ASD or VSD demonstrated a test AUC of 0.81 (95% CI, 0.76, 0.86), sensitivity of 0.73, specificity of 0.76, PPV of 0.14, and NPV of 0.98 (Figure 1). Conclusions: AI-enabled ECG models demonstrate promising performance in detecting ASD and VSD in pediatric patients. A combined model that detects either ASD or VSD also shows robust results. Future research should focus on multi-center validation of these models, refining their diagnostic capacities, and investigating their potential for early intervention in CHDs.

Introduction: Left and right ventricular systolic dysfunction (LVSD and RVSD respectively) contribute to considerable pediatric morbidity. Both LVSD and RVSD can occur in children before symptom onset, emphasizing the need for early detection. Existing deep-learning algorithms can identify LVSD in adults using 12-lead ECG; however, their efficacy in children is uncertain. Aims: We aimed to create a deep-learning model for LVSD and RVSD detection in pediatric patients using an AI-enabled ECG and compare novel pediatric models to a previously validated adult LVSD model. Methods: We identified 10,214 pediatric patients at the Mayo Clinic with a transthoracic echocardiogram (TTE) and a 10-second, 12-lead surface ECG within 14 days of TTE, performed from 2002 -2022. Our cohort comprised the first TTE-ECG pair from each patient, totaling 138 with EF ≤ 35%, 312 with EF ≤ 50%, 482 with RVSD, and 9,567 without any LVSD or RVSD. The cohort was split into training, validation, and testing datasets to develop neural networks for systolic dysfunction detection. Results: The cohort was 52.3% male, with an average age of 10.3 years. Novel models generated in the pediatric cohort demonstrated excellent performance in systolic dysfunction detection (Figure 1). Pediatric models for EF ≤ 35% and EF ≤ 50% achieved test AUCs of 0.92 (95% CI 0.89, 0.95) and 0.89 (95% CI 0.85, 0.94) respectively. The RVSD model reached a test AUC of 0.91 (95% CI 0.88, 0.94). A combined model for EF ≤ 35% or RVSD exhibited a test AUC of 0.90 (95% CI 0.87, 0.93), sensitivity 0.88, specificity 0.82, PPV 0.20, and NPV 0.95. A combined model for EF ≤ 50% or RVSD exhibited a test AUC of 0.88 (95% CI 0.85, 0.91). The previously validated adult data-derived model achieved an AUC of 0.86 (95% CI 0.83, 0.89) for EF ≤ 50% in children. Conclusions: AI-enabled ECGs demonstrate efficient detection of both LVSD and RVSD in pediatric patients. Future multi-site validation is needed to further establish the reliability of these models.

Introduction: Biologic sex and hormonal concentrations shape ECG parameters due to sex hormone effects on cardiac function. AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. Aims: We aimed to test the ability of AI-enabled ECGs to predict pediatric patient sex and explore the influence of pubertal development. Methods: Two AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead surface ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 89,063 pediatric patients (aged <19 years) recorded from 1988-2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0-7 years), pubertal (8-14 years), and post-pubertal (15-18 years) patients. Results: The cohort consisted of 46.6% males, with 21,434 prepubertal, 26,300 pubertal, and 41,329 post-pubertal children. No significant performance difference was observed between the two models. Both models demonstrated 81% accuracy and an overall AUC of 0.91 in the testing cohorts, with high discriminatory ability in post-pubertal teenagers (AUC = 0.98). (Figure 1) Conclusions: AI-ECG predicts pediatric patient sex with varying performance across pubertal stages. The highest discriminatory ability is observed post-puberty, decreasing in pubertal and prepubertal children. The results suggest hormonal and physiological changes during puberty may influence cardiac electrophysiology measurably. More research is required to interpret CNN-predicted sex discordance with actual sex and its implications for future cardiovascular risk.

Introduction: Data on use of ICE guidance in TEER procedure is limited to case reports and small case series. Our study aims to assess the feasibility, safety, utilization patterns and clinical outcomes of mitral TEER procedure with ICE guidance using a nationally representative real-world cohort of patients.Hypothesis: There is no difference between ICE-guided TEER and TEE-guided TEER in the clinical outcomes and complications. Methods: This study used the National Inpatient Sample database from quarter 4 of 2015 to 2020. We used a propensity-matched analysis and adjusted odds ratios for in-hospital outcomes/complications. A P value of <0.05 was considered significant. Results: A total of 38,770 weighted cases of mitral TEER were identified. Of the included patients 665 patients underwent ICE-guided TEER while 38,105 had TEE-guided TEER. There were no differences in the in-hospital mortality between both groups (2.5% vs. 3.0%, p=0.58). Adjusted odds of in-hospital mortality (aOR 0.83, 95%CI [0.42-1.64]) were not significantly different. There were no differences in periprocedural complications including cardiac (aOR 0.85, 95%CI [0.54-1.35]), bleeding (aOR 1.45, 95%CI [0.93-2.33]), respiratory (aOR 0.88, 95%CI [0.61-1.25]), and renal (aOR 0.89, 95%CI [0.66-1.20]) complications between patients undergoing ICE-guided versus TEE-guided TEER. There was no difference in GI complications between both groups (aOR 1.11, 95%CI [0.46-2.70]). The adjusted length of stay was less among ICE-guided TEER (median: 1 vs. 2, p<0.01) with no differences in costs of hospitalization ($137,312 vs. $153,773, p=0.06), Conclusions: In conclusion, ICE-guided mitral TEER is safe when compared with TEE guided TEER with no significant differences in in-hospital mortality, cardiac, bleeding, respiratory, and renal complications. The length of stay in ICE group is shorter as compared with the TEE group.

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: Data on clinical outcomes of percutaneous closure of iatrogenic atrial septal defect (PC-ASD) in patients undergoing transcatheter edge-to-edge repair (TEER) or mitral valve replacement (TMVR) remains limited. We used a real-world USA database to evaluate the differences in the in-hospital outcomes between both groups. Hypothesis: Is there a clinical significance for PC-ASD in patients undergoing TEER or TMVR? Methods: We used the National Inpatient Sample (2015-2020) by utilizing ICD-10 codes to identify all patients who underwent TEER or TMVR with or without PC-ASD. Multivariate logistic regression was used for adjusted outcomes (adjusted odds ratio [aOR]). Results: A total of 44,065 eligible weighted hospitalizations were included: 39,625 (89.9%) of the patients underwent TEER and 4,440 (10.1%) underwent TMVR. Rates of PC-ASD were higher in TMVR group compared to TEER group (10.7% vs. 2.0%, p<0.01). In patients who underwent TEER without PC-ASD, PC-ASD was more associated with acute heart failure (AHF) (aOR 1.42 [1.19-1.68], p<0.01) and longer hospitalizations (aOR 1.64 [1.41-1.89], p<0.01), without significant difference in inpatient mortality (aOR 0.97 [0.61-1.54], p<0.01), cardiogenic shock (aOR 1.13 [0.85-1.51], p=0.21), need for mechanical circulatory support (MCS) (aOR 0.98 [0.61-1.54], p=0.18), and stroke. Compared to patients who underwent TMVR without PC-ASD, PC-ASD was associated with higher inpatient morality (aOR 1.65 [1.13-2.4], p<0.01), need for MCS (aOR 1.91 [1.33-2.73], p<0.01) without significant difference in AHF (aOR 1.19 [0.94-1.51], p=0.18] and cardiogenic shock (aOR 0.83 [0.56-1.22], p=0.43). Remarkably, PC-ASD was less associated with stroke (aOR 0.27 [0.10-0.72], p<0.01). Conclusions: Rates of PC-ASD after TEER were lower than after TMVR and were associated with worse in-hospital mortality and need for MCS in TMVR but not in TEER. Also, PC-ASD was less associated with stroke after TMVR.

Introduction: Data on clinical outcomes of early transcatheter aortic valve replacement (TAVR) (<48 hours) compared to late (≥48 hours) TAVR in patients hospitalized with acute heart failure (AHF) and aortic stenosis (AS) remains limited. We aim to evaluate the differences between early vs. late TAVR regarding major clinical outcomes using a real-world US database. Hypothesis: There is no differences in the clinical outcomes based on the timing of TAVR procedural intervention in patients with acute heart failure. Methods: We used the National Inpatient Sample database by utilizing ICD-10 codes to identify all hospitalizations with a diagnosis of AHF and TAVR (2015-2020). The associations between the time of TAVR and outcomes were examined using multivariable logistic regression. Propensity-score matched (PSM) analysis was utilized to determine the adjusted odds ratio (aOR) of major clinical outcomes. Results: A total of 25,285 weighted hospitalizations of AHF were identified, of which 6,855 patients (27.1%) underwent early TAVR and 18,430 (72.9%) had late TAVR. Compared with early TAVR, Late TAVR was associated with less need for mechanical circulatory support (MCS) (aOR 0.97 [0.96-0.98], p<0.01) and acute hypoxic respiratory failure (aOR 0.99 [0.98-0.99], p<0.01), but higher odds of developing septic shock (aOR 2.06 [1.52-2.80], p <0.01). However, there was no significant difference between both procedures regarding in-hospital mortality (aOR 1.01 [1.00-1.02], p=0.14), cardiac arrest (aOR 0.99 [0.98-1.00], p=0.21), and stroke (aOR 1.01 [1.00-1.02], p=0.18). Conclusions: Most patients with AHF and AS undergo TAVR after 48 hours of their presentation. After PSM, there was no difference in inpatient mortality, cardiac arrest, and stroke between both procedures. Late TAVR was associated with lower odds for MCS and acute hypoxic respiratory failure. The optimal timing of TAVR in patients with AHF and AS needs further study.