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Background: Despite improvements in survival over time, the mortality rate for infants with single-ventricle heart disease remains high. Infants of low socioeconomic status (SES) are particularly vulnerable. We sought to determine whether use of a novel remote monitoring program, the Cardiac High Acuity Monitoring Program, mitigates differences in outcomes by SES., Methods and Results: Within the Cardiac High Acuity Monitoring Program, we identified 610 infants across 11 centers from 2014 to 2021. All enrolled families had access to a mobile application allowing for near-instantaneous transfer of patient information to the care team. Patients were divided into SES tertiles on the basis of 6 variables relating to SES. Hierarchical logistic regression, adjusted for potential confounding characteristics, was used to determine the association between SES and death or transplant listing during the interstage period. Of 610 infants, 39 (6.4%) died or were listed for transplant. In unadjusted analysis, the rate of reaching the primary outcome between SES tertiles was similar ( P =0.24). Even after multivariable adjustment, the odds of death or transplant listing were no different for those in the middle (odds ratio, 1.7 [95% CI, 0.73-3.94) or highest (odds ratio, 0.997 [95% CI, 0.30, 3.36]) SES tertile compared with patients in the lowest (overall P value 0.4)., Conclusions: In a large multicenter cohort of infants with single-ventricle heart disease enrolled in a digital remote monitoring program during the interstage period, we found no difference in outcomes based on SES. Our study suggests that this novel technology could help mitigate differences in outcomes for this fragile population of patients.

Background: Mortality in cohorts with a single ventricle remains high with multiple associated factors. The effect of heart block during stage I palliation remains unclear., Objective: The purpose of this study was to study patient and surgical risks of heart block and its effect on 12-month transplant-free survival in patients with a single ventricle., Methods: Patient, surgical, outcome data and heart block status (transient and permanent) were obtained from the National Pediatric Cardiology Quality Improvement Collaborative single ventricle database. Bivariate analysis was performed comparing patients with and without heart block, and multivariate modeling was used to identify variables associated with block. One-year outcomes were analyzed to identify variables associated with lower 12-month transplant-free survival., Results: In total, 1423 patients were identified, of whom 28 (2%) developed heart block (second degree or complete) during their surgical admission. Associated risk factors for block included heterotaxy syndrome (odds ratio [OR] 6.4) and atrial flutter/fibrillation (OR 3.8). Patients with heart block had lower 12-month survival, though only in patients with complete heart block as opposed to second degree block. At 12 months of age, 43% (12/28) of patients with heart block died and were more likely to experience mortality at 12 months than patients without block (OR 4.9; 95% confidence interval 1.4-17.5; P = .01)., Conclusion: Although rare, complete heart block after stage I palliation represents an additional risk of poor outcomes in this high-risk patient population. Heterotaxy syndrome was the most significant risk factor for the development of heart block after stage I palliation. The role of transient block in outcomes and potential rescue with long-term pacing remains unknown and requires additional study., (Copyright © 2021 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)

There is high risk of mortality between stage I and stage II palliation of single ventricle heart disease. This study aimed to leverage advanced machine learning algorithms to optimize risk-prediction models and identify features most predictive of interstage mortality. This study utilized retrospective data from the National Pediatric Cardiology Quality Improvement Collaborative and included all patients who underwent stage I palliation and survived to hospital discharge (2008–2019). Multiple machine learning models were evaluated, including logistic regression, random forest, gradient boosting trees, extreme gradient boost trees, and light gradient boosting machines. A total of 3267 patients were included with 208 (6.4%) interstage deaths. Machine learning models were trained on 180 clinical features. Digoxin use at discharge was the most influential factor resulting in a lower risk of interstage mortality (p < 0.0001). Stage I surgery with Blalock-Taussig-Thomas shunt portended higher risk than Sano conduit (7.8% vs 4.4%, p = 0.0002). Non-modifiable risk factors identified with increased risk of interstage mortality included female sex, lower gestational age, and lower birth weight. Post-operative risk factors included the requirement of unplanned catheterization and more severe atrioventricular valve insufficiency at discharge. Light gradient boosting machines demonstrated the best performance with an area under the receiver operative characteristic curve of 0.642. Advanced machine learning algorithms highlight a number of modifiable and non-modifiable risk factors for interstage mortality following stage I palliation. However, model performance remains modest, suggesting the presence of unmeasured confounders that contribute to interstage risk. [ABSTRACT FROM AUTHOR]