Anjewierden, Scott

Background Mortality in adults with congenital heart disease (CHD) is strongly driven by disease complexity. Social determinants of health (SDOH) influence adult CHD (ACHD) health outcomes but their impact on mortality is unclear. This study examined the influence of clinical factors and SDOH on all‐cause mortality in patients with ACHD. Methods Single‐center retrospective cohort study of patients with ACHD in the Mayo ACHD Registry who had a verifiable residential address between January 1, 2004 and December 31, 2023. Clinical and SDOH indicators were extracted, including insurance type, language, rural–urban commuting area code, and a housing‐based socioeconomic index, an individual‐level measure of socioeconomic status derived from 4 objective housing characteristics and categorized into quartiles (Q1–Q4). Cox proportional‐hazards models were fitted to evaluate associations with mortality, incorporating CHD severity, arrhythmias, SDOH indicators, and other comorbidities. County of residence was included as a random effect, and model discrimination was assessed using the concordance statistic. Results Among 6134 patients with ACHD, the mean age was 38±16 years; 51% were male; 78.7% were White and 44.9% had rural residence. Independent predictors of mortality included cyanotic CHD (adjusted hazard ratio [aHR], 5.70 [95% CI, 2.90–11.23]), Fontan palliation (aHR, 4.98 [95% CI, 2.56–9.70]), ventricular tachycardia (aHR, 1.71 [95% CI, 1.16–2.52]), older age at first visit (aHR, 1.05 per year [95% CI, 1.04–1.05]), male sex (aHR, 1.19 [95% CI, 1.03–1.39]), public insurance (aHR, 1.60 [95% CI, 1.35–1.90]), rural residence (aHR, 1.25 [95%, CI 1.06–1.47]), and non‐English language (aHR 1.75 [95% CI 1.38–2.23]). The housing‐based socioeconomic index quartile was not associated with mortality. The final model achieved a concordance statistic of 0.77. Conclusions Mortality in patients with ACHD is strongly influenced by both clinical factors and SDOH, independent of socioeconomic status. Addressing barriers to care such as language, insurance, and geography is essential for improving ACHD survival particularly in those at increased mortality risk.

Background: Artificial intelligence (AI) can be used to predict a person’s age from the electrocardiogram (ECG) (AI-ECG age) and has been proposed as a measurement of biological age. The difference between AI-ECG age and chronological age (defined as delta-age) is an independent predictor of mortality in the general population. This study assessed the relationship between AI ECG estimated age and mortality among adults living with congenital heart disease (CHD). Methods: A previously validated neural network to estimate AI-ECG age was used to analyze standard digital 12-lead ECGs in a cohort of subjects aged >18 years seen in the adult CHD (ACHD) clinic between 1992 to 2023. A single ECG, collected during the year of the first visit to the ACHD clinic was analyzed to compute the delta-age.. A positive delta-age indicates an AI ECG estimated age higher than the subject’s chronological age. The relationship between the delta-age and mortality was evaluated using Cox proportional hazard models adjusting for influential clinical factors including chronologic age, sex, ACHD severity, NYHA class and history of arrhythmia. Results: Of 5,780 subjects tested (50% females), the mean chronological age was 39.1 ± 15.0 years. AI ECG estimated age was 52.3 ± 16.6 years. Complexity of CHD was classified as mild, moderate, and severe in 7.4%, 73.9% and 18.7% of patients respectively. Patients with severe CHD had the highest delta-age of 17.1 (±18.2) years followed by moderate 12.7 (±13.6) y and simple 7.7 (±11.5) y. Patients with single ventricle had the highest deviation in AI estimated age [median delta-age 21.0 (IQR 28.6) y] followed by those with a systemic right ventricle [median delta-age of 16.3 (IQR 29.3) y]. During a median follow-up of 6.4 y (25th – 75th centile 1.58 – 13.7 y), mortality occurred in 839 (14.5%) patients. After adjusting for chronological age, CHD severity, and other clinical variables, delta-age gap was associated with increased mortality risk (HR 1.06 (1.03 – 1.09) per 5-year increment in delta-age, p<0.005), Table 1. Conclusion: The difference between AI ECG and chronological age is an independent predictor of all-cause mortality in ACHD patients.

Background Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12‐lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence–enabled ECG algorithms for LVSD and RVSD detection in pediatric patients. Methods and Results We identified 10 142 unique pediatric patients (age≤18) with a 10‐second, 12‐lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF <50% achieved excellent test areas under the curve of 0.93 (95% CI, 0.89–0.98) and 0.88 (95% CI, 0.83–0.94) respectively. The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. In comparison, the previously validated adult data‐derived model for LVEF <35% achieved an area under the curve of 0.87 (95% CI, 0.84–0.90) for LVEF ≤35% in children. A novel pediatric model for any RVSD detection reached a test area under the curve of 0.90 (0.87–0.94). Conclusions An artificial intelligence–enabled ECG demonstrates accurate detection of both LVSD and RVSD in pediatric patients. While adult‐trained models offer good performance, improvements are seen when training pediatric‐specific models.

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.

Radiofrequency ablation (RFA) is a common treatment of atrial fibrillation. However, current treatment is associated with a greater than 20% recurrence rate, in part due to inadequate monitoring of tissue viability during ablation. Spectral parameters, in particular cyclic variation of integrated backscatter (CVIB), have shown promise as early indicators of myocardial recovery from ischemia. Our aim was to demonstrate the use of spectral parameters to differentiate atrial myocardium before and after radiofrequency ablation. An AcuNav 10 F catheter was used to collect radiofrequency signals from the posterior wall of the left atrium of patients before and immediately after RFA for AF. The normalized power spectrum was obtained and three spectral parameters (integrated backscatter [IB], slope, and intercept) were extracted across two continuous heart cycles. Parameters were gated for ventricular end-diastole and compared before and after ablation. Additionally, the cyclic variation of each of these three parameters was generated as an average of the variation across the two recorded heart cycles. Data from 14 patients before and after ablation demonstrated a significant difference in the magnitude of the cyclic variation of integrated backscatter (9.0 vs. 6.0 dB, p < .001) and cyclic variation of the intercept (14.0 vs. 11.5 dB, p = .04). No significant difference was noted in the magnitude of the cyclic variation of the slope. Among spectral parameters gated for end-diastole, significant differences were noted in the slope (−4.39 vs. −3.73 dB/MHz, p = .002) and intercept (16.8 vs. 11.9 dB, p = .002). No significant difference was noted in the integrated backscatter. Spectral parameters are able to differentiate atrial myocardium before and immediately following ablation and may be useful in monitoring atrial ablations.