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Machine Learning to Predict Interstage Mortality Following Single Ventricle Palliation: A NPC-QIC Database Analysis

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Abstract

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.

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Abbreviations

AUROC:

Area under the receiver operating characteristic

BTTs:

Blalock-Taussig-Thomas shunt

CART:

Classification and regression tree analysis

DKS:

Damus Kaye Stansel

ECMO:

Extracorporeal membrane oxygenation

GBT:

Gradient boosting trees

HLHS:

Hypoplastic left heart syndrome

LightGBM:

Light gradient boosting model

ML:

Machine learning

NPC-QIC:

National Pediatric Cardiology Quality Improvement Collaborative

NPV:

Negative predictive value

PPV:

Positive predictive value

REDcap:

Research Electronic Data Capture

RF:

Random forest

SHAP:

Shapley additive explanations

XGBoost:

Extreme gradient boosting model

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Acknowledgements

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Funding

Dr. Sunthankar received funding from NIH T32 5T32HL105334-10 and Project Heart. Dr. Jayaram is supported by a Career Development Award (K23HL153895) from the NHLBI.

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Author notes

  1. Sudeep D. Sunthankar and Juan Zhao are co-first authors.

Authors and Affiliations

  1. Division of Pediatric Cardiology, Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA

    Sudeep D. Sunthankar, David A. Parra, Karen Kohl & Justin Godown

  2. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

    Juan Zhao, Wei-Qi Wei & Allison McCoy

  3. Division of Pediatric Cardiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Garick D. Hill

  4. Division of Pediatric Cardiology, Children’s Mercy Hospital, Kansas City, MO, USA

    Natalie M. Jayaram

  5. Thomas P. Graham Jr Division of Pediatric Cardiology, Department of Pediatrics, Monroe Carell Jr Children’s Hospital at Vanderbilt, 2220 Children’s Way, Suite 5230, Nashville, TN, 37232, USA

    Sudeep D. Sunthankar

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Contributions

All authors have contributed in a significant and meaningful way deserving of authorship. SDS, JZ, WQW, and JG designed the study. SDS, GDH, DAP, KK, NMJ, JG all have expertise in single ventricle congenital heart disease. GDH, AM, NMJ are intimately involved with the NPC-QIC registry and served as resources for data acquisition. JZ and WQW have expertise in machine learning models and completed the statistical analysis. SDS, JZ, and JG were involved in clinical interpretation of machine learning models and manuscript preparation. All authors have read and approved the submitted version and no authors have financial or other relationships that might lead to a conflict of interest.

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Correspondence to Sudeep D. Sunthankar.

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Sunthankar, S.D., Zhao, J., Wei, WQ. et al. Machine Learning to Predict Interstage Mortality Following Single Ventricle Palliation: A NPC-QIC Database Analysis. Pediatr Cardiol 44, 1242–1250 (2023). https://doi.org/10.1007/s00246-023-03130-z

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