{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T17:56:43Z","timestamp":1780941403412,"version":"3.54.1"},"reference-count":73,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry of Education and Research (BMBF)","award":["FKZ 01ZZ2301A"],"award-info":[{"award-number":["FKZ 01ZZ2301A"]}]},{"name":"German Federal Ministry of Education and Research (BMBF)","award":["FKZ 01KX2121"],"award-info":[{"award-number":["FKZ 01KX2121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: The objective of this IRB-approved retrospective monocentric study was to identify risk factors for mortality after surgery for congenital heart defects (CHDs) in pediatric patients using machine learning (ML). CHD belongs to the most common congenital malformations, and remains the leading mortality cause from birth defects. Methods: The most recent available hospital encounter for each patient with an age &lt;18 years hospitalized for CHD-related cardiac surgery between the years 2011 and 2020 was included in this study. The cohort consisted of 1302 eligible patients (mean age [SD]: 402.92 [\u00b1562.31] days), who were categorized into four disease groups. A random survival forest (RSF) and the \u2018eXtreme Gradient Boosting\u2019 algorithm (XGB) were applied to model mortality (incidence: 5.6% [n = 73 events]). All models were then applied to predict the outcome in an independent holdout test dataset (40% of the cohort). Results: RSF and XGB achieved average C-indices of 0.85 (\u00b10.01) and 0.79 (\u00b10.03), respectively. Feature importance was assessed with \u2018SHapley Additive exPlanations\u2019 (SHAP) and \u2018Time-dependent explanations of machine learning survival models\u2019 (SurvSHAP(t)), both of which revealed high importance of the maximum values of serum creatinine observed within 72 h post-surgery for both ML methods. Conclusions: ML methods, along with model explainability tools, can reveal interesting insights into mortality risk after surgery for CHD. The proposed analytical workflow can serve as a blueprint for translating the analysis into a federated setting that builds upon the infrastructure of the German Medical Informatics Initiative.<\/jats:p>","DOI":"10.3390\/diagnostics14222587","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T07:45:27Z","timestamp":1731915927000},"page":"2587","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Using Machine Learning and Feature Importance to Identify Risk Factors for Mortality in Pediatric Heart Surgery"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1866-860X","authenticated-orcid":false,"given":"Lorenz","family":"Kapsner","sequence":"first","affiliation":[{"name":"Medical Informatics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91058 Erlangen, Germany"},{"name":"Institute of Radiology, Universit\u00e4tsklinikum Erlangen, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Fei\u00dft","sequence":"additional","affiliation":[{"name":"Institute of Medical Biometry, University of Heidelberg, 69117 Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5242-2630","authenticated-orcid":false,"given":"Ariawan","family":"Purbojo","sequence":"additional","affiliation":[{"name":"Department of Paediatric Cardiac Surgery, Universit\u00e4tsklinikum Erlangen, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans-Ulrich","family":"Prokosch","sequence":"additional","affiliation":[{"name":"Medical Informatics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91058 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6864-8936","authenticated-orcid":false,"given":"Thomas","family":"Ganslandt","sequence":"additional","affiliation":[{"name":"Medical Informatics, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91058 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8455-3416","authenticated-orcid":false,"given":"Sven","family":"Dittrich","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology, Universit\u00e4tsklinikum Erlangen, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0518-4710","authenticated-orcid":false,"given":"Jonathan","family":"Mang","sequence":"additional","affiliation":[{"name":"Medical Center for Information and Communication Technology, Universit\u00e4tsklinikum Erlangen, 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1788-233X","authenticated-orcid":false,"given":"Wolfgang","family":"W\u00e4llisch","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology, Universit\u00e4tsklinikum Erlangen, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91054 Erlangen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1055\/s-0030-1254155","article-title":"Prevalence of congenital heart defects in newborns in germany: Results of the first registration year of the PAN study (July 2006 to June 2007)","volume":"222","author":"Lindinger","year":"2010","journal-title":"Klin. 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