{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T15:09:20Z","timestamp":1789657760227,"version":"4.0.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686875","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,17]]},"abstract":"<jats:p>Introduction: Acute kidney injury (AKI) is a frequent complication after pediatric cardiac surgery. This study aimed to identify literature-informed perioperative variables associated with AKI within 0\u20137 days after surgery and to assess whether the resulting risk profile remained visible in an extended routine-data feature set. Methods: We performed a retrospective secondary analysis of anonymized routine clinical data comprising 1,354 surgery episodes from 1,135 patients. Feature selection was informed by a structured literature review and resulted in two analytical datasets from the same cohort: Dataset A, a 26-feature core set, and Dataset B, a broader 72-variable feature space used for domain-level robustness analysis. The primary endpoint was AKI within 0\u20137 days after surgery. Results: AKI within 0\u20137 days occurred in 530 of 1,354 episodes (39.14%), and overall postoperative AKI occurred in 566 episodes (41.8%). Episodes with AKI showed longer procedure, cardiopulmonary bypass (CPB), and aortic cross-clamp times and higher mean Vasoactive Inotropic Score (VIS) during the first 24 postoperative hours. The median time from surgery end to the first linked rule-based AKI event was 7.2 hours; 395 of 530 events (74.5%) occurred within 24 hours and 462 (87.2%) within 48 hours. Conclusion: The findings identify a clinically relevant early postoperative risk window characterized by patient vulnerability, intraoperative burden, and early hemodynamic\/perfusion instability. Dataset B provided domain-level robustness support, while ehrapy and AnnData enabled reproducible data representation and exploration.<\/jats:p>","DOI":"10.3233\/shti260992","type":"book-chapter","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T14:47:58Z","timestamp":1789656478000},"source":"Crossref","is-referenced-by-count":0,"title":["Early Postoperative AKI Risk Factors After Pediatric Cardiac Surgery: A Literature-Informed Analysis of Integrated Routine Clinical Data"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5088-1258","authenticated-orcid":false,"given":"Fatemeh","family":"Ali-Sadeh","sequence":"first","affiliation":[{"name":"Hannover University of Applied Sciences and Arts, Faculty III \u2013 Media, Information and Design, Hannover, Germany"},{"name":"Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6478-2230","authenticated-orcid":false,"given":"Marcel","family":"Schack","sequence":"additional","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Hannover, Germany"},{"name":"CAIMed \u2013 Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine, Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7068-1508","authenticated-orcid":false,"given":"Johanna Natalie","family":"Apfel-Starke","sequence":"additional","affiliation":[{"name":"Hannover University of Applied Sciences and Arts, Faculty III \u2013 Media, Information and Design, Hannover, Germany"},{"name":"Hannover Medical School, MHH Information Technology, Hannover, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2026: BIG DATA, small data, Your Data; Transitions in the age of biomedical AI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260992","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T14:47:59Z","timestamp":1789656479000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260992"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,17]]},"ISBN":["9781643686875"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260992","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,17]]}}}