26-A-11796-ACC HEALTH PERCEPTIONS AND NEURODEVELOPMENTAL FUNCTIONING ARE KEY TARGETS TO IMPROVE HEALTH-RELATED QUALITY OF LIFE IN ADULT CONGENITAL HEART DISEASE PATIENTS: INSIGHTS FROM THE PROJECT TO UNDERSTAND LIFELONG SURVIVOR EXPERIENCE (CHD PULSE)

Journal of the American College of Cardiology · 2026-03-27

26-A-10949-ACC LIVING WITH TETRALOGY OF FALLOT: A REPORT FROM THE CONGENITAL HEART DISEASE PROJECT TO UNDERSTAND LIFELONG SURVIVOR EXPERIENCE (CHD PULSE)

Journal of the American College of Cardiology · 2026-03-27

Reproductive health outcomes of congenital heart disease survivors: A report from the congenital heart disease project to understand lifelong survivor experience (CHD PULSE study)

American Heart Journal · 2026-01-06

Shocking Practices of Pediatric Electrophysiologists: Contemporary Defibrillation Testing Data From a Multicenter National CIED Registry

Circulation Arrhythmia and Electrophysiology · 2026-03-01

Supervised Machine Learning Models Predicting Postoperative Low Cardiac Output Syndrome In Neonates

Critical Care Explorations · 2025-10-01 · 1 citations

OBJECTIVE: To train and test supervised machine learning (ML) models to predict low cardiac output syndrome (LCOS) within the first 48 postoperative hours in neonates undergoing cardiothoracic surgery. DESIGN: Retrospective observational study. An efficient tree-based gradient-boosting algorithm (LightGBM) ML models were developed to predict LCOS occurrence at 2-, 4-, 6-, and 12-hour forecasting horizons, incorporating data from the prediction time and the two preceding hours. SHapley Additive exPlanations (SHAP) analyses were used for feature importance analyses. SETTING: Single center, January 2012 to April 2023. PATIENTS: Neonates 28 days old or younger who underwent cardiothoracic surgery. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 181 neonates were included, with 14.9% experiencing LCOS. A multivariate time-series dataset was constructed using hourly clinical and laboratory variables recorded during the first 48 postoperative hours. The LightGBM ML models achieved area under the receiver operating characteristic curve values ranging from 0.91 to 0.98 and area under the precision-recall curve values ranging from 0.60 to 0.80 for LCOS prediction across 2-, 4-, 6-, and 12-hour forecasting horizons. SHAP analyses identified higher vasoactive inotrope score, lower urine output, and higher serum lactate as the most influential predictors. CONCLUSIONS: This study demonstrates that the supervised machine learning models can accurately predict LCOS in neonates, offering high interpretability. The findings support further validation in multicenter settings and integration into clinical workflows to enhance postoperative critical cardiac care neonates.

Performance of Supervised Machine Learning Models for Cardiac Surgery-Associated Acute Kidney Injury in Children: Multicenter Retrospective Cohort Study, 2019–2022

Pediatric Critical Care Medicine · 2025-11-10 · 4 citations

OBJECTIVES: To derive and externally validate supervised machine learning (ML) models predictive of cardiac surgery-associated acute kidney injury (CS-AKI). DESIGN: Retrospective cohort analysis. SETTING: Multicenter (4), cardiac surgical centers from January 2019 to February 2022. PATIENTS: Seven days to 18 years old who had undergone cardiac surgery. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: CS-AKI was defined using Kidney Disease: Improving Global Outcomes criteria, with stages 2/3 classified as severe, during the first 7 postoperative days. Data analysis followed two approaches: 1) combining three centers for derivation and using a fourth for external validation and 2) randomly dividing the entire dataset into derivation and validation cohorts in a 4:1 ratio. Forty ML models were developed across five derivation-validation pairs using four ML algorithms (light gradient-boosting machine, extreme gradient boosting, categorical boosting, and histogram gradient boosting) to predict two outcomes (any and severe CS-AKI) utilizing preoperative, intraoperative, and immediate postoperative variables. SHapley Additive exPlanations was used for input variable importance analysis. A cohort of 1100 patients was analyzed. Any CS-AKI and severe CS-AKI occurred in 49.1% and 23.1% patients, respectively. Wide range of variations in external validation of model performance were observed among all 40 ML models. For any CS-AKI, the range in metrics were: area under the receiver operating characteristic curve (AUROC) 0.64-0.83, sensitivity 0.29-0.86, specificity 0.46-0.95, positive predictive value (PPV) 0.50-0.85, and negative predictive value (NPV) 0.60-0.86. For severe CS-AKI, we found the range in metrics with AUROC 0.65-0.77, sensitivity 0.04-0.58, specificity 0.77-0.99, PPV 0.32-0.75, and NPV 0.78-0.90. Preoperative serum creatinine, cardiopulmonary bypass, aortic cross-clamp duration, weight, and age at surgery were the most important predictors associated with CS-AKI. CONCLUSIONS: This analysis of a retrospective multicenter dataset shows that external performance of ML models vary, highlighting challenges in generalizability, which may be due to center-based differences in practice.

Abstract 4372933: Hybrid NLP Model Accurately Extracts Data from Tetralogy of Fallot Cardiac MR Reports

Circulation · 2025-11-03

Introduction: Utilization of large language models (LLMs) for named entity recognition from free-text medical reports is rapidly expanding. However, concerns about protected health information (PHI) restrict the adoption of commercial LLMs. While simpler natural language processing methods, such as regular expressions (RegEx), offer an accessible solution, they often fail when linguistic variability increases. Particularly in rare conditions with limited labeled datasets, advanced machine learning models like Bidirectional Encoder Representations from Transformers (BERT) are difficult to train. Thus, there is a need for secure, efficient, and accurate data extraction methods. Research Questions: We hypothesized that a hybrid approach combining simple RegEx with few-shot prompts on an on-premises LLM would maximize accuracy and efficiency while maintaining PHI compliance. Methods: We retrospectively analyzed cardiovascular magnetic resonance (CMR) reports from 183 patients. Custom RegEx rules and few-shot LLM prompts were independently applied across all reports. A hybrid extraction approach integrated both methods by selectively using LLM results in areas of poor RegEx performance. Ground truths were manually verified by a clinical expert. Performance was evaluated using Coverage, Precision, Recall, and F 1 -score metrics. Results: A manual review of 430 CMR reports (3/2005-12/2024) identified a median proportion of missing values of 3.95% (IQR 2.79–5.12) across 13 clinical metrics. The baseline RegEx extraction alone achieved a completeness of 90.7%, whereas the standalone few-shot LLM approach reached 91.9%. Combining RegEx with targeted few-shot LLM prompts, the hybrid method significantly improved data completeness to 99.8%. In terms of accuracy, the hybrid approach attained an F 1 score of 97.5%±3.6, clearly outperforming RegEx alone (85.2%±22.2) and the standalone LLM (86.0%±15.1). Pairwise comparisons confirmed differences were significant (p<0.001) with large effect sizes (Cohen’s d >1.0). Additionally, the hybrid approach reduced computational time by approximately 75% compared to the LLM-only method. Conclusion: A hybrid NLP method combining deterministic RegEx and targeted LLM prompts significantly enhances data extraction accuracy from legacy clinical free-text reports. This approach addresses PHI security concerns and effectively reallocates annotation resources toward predictive modeling, thereby advancing clinical research and quality improvement.

Arrhythmia burden in congenitally corrected transposition of the great arteries: Does treatment pathway matter?

Heart Rhythm · 2025-01-17 · 4 citations

Quality of life, neurodevelopmental and psychosocial outcomes in pediatric heart failure

Elsevier eBooks · 2025-10-17

Dual-Phase Increase in Atrial Arrhythmia Prevalence Following Percutaneous Atrial Septal Defect Closure

Journal of the Society for Cardiovascular Angiography & Interventions · 2025-06-12 · 1 citations

Background: Conflicting data surround the relationship between age at percutaneous atrial septal defect (ASD) closure and subsequent burden of atrial arrhythmias (AA), particularly in adults. This study aimed to determine the effect of age at ASD closure and other predisposing patient-specific factors on the burden of AA postpercutaneous ASD closure. Methods: All patients who underwent percutaneous ASD closure at Cleveland Clinic from January 2010 to July 2022 were included. A nonlinear logistic temporal decomposition mixed-effects model was used to analyze the longitudinal AA. Results: Among 197 patients, 63% (125) were female, with a mean age of 40 years (SD, 24 years) at ASD closure. A total of 177 patients (89% of the cohort) had 687 rhythm records. Postclosure AA exhibited a dual-phase pattern: early peaking phase followed by a late rise up to 6 years postclosure. Age 60 years or older was associated with higher likelihood of early (≤6 months) and late (>6 months) AA prevalence. Older age, lower E/A ratio, and lower left ventricular ejection fraction were associated with a higher likelihood of AA post-ASD closure. Greater than moderate tricuspid regurgitation was associated with a higher likelihood of early AA. Mild right ventricular dysfunction and more than moderate right ventricular dilation were associated with a higher likelihood of late AA. Conclusions: Older patients have an ongoing dual-phase risk for AA postpercutaneous ASD closure. Our findings underscore the need for routine rhythm monitoring in patients aged 60 years or older.