Predicting kidney injury after cardiac surgery with cardio-pulmonary bypass using machine learning
Risk stratification with an ML-based AI approach based solely on EHR data provides a low-effort and high-yield screening method for identifying patients at risk of developing CSA-AKI and AKD. These findings indicate that an EHR (Electronic Health Record)-only model, trained on routine hospital data, provides clinically actionable discrimination in a real-world cardiac surgery cohort, supporting its use for early screening and targeted mitigation.
Authors
- M. Dahlweid
- Janis Fliegenschmidt (ORCID: https://orcid.org/0000-0002-7396-5677)
- Patricia Cabanillas Silva (ORCID: https://orcid.org/0000-0002-7509-721X)
- Nikolai Hulde (ORCID: https://orcid.org/0000-0001-5070-0249)
- Laurent Meesseman (ORCID: https://orcid.org/0000-0002-3761-9822)
- Astrid Bergmann
- Gunung Sasono
- Mohamed Rezk
- Vera von Dossow
Institutions
- Fidal (FR)
- Bielefeld University (DE)
- Heart and Diabetes Center North Rhine-Westphalia (DE)
- Jiaxing University (CN)
- Enceladus Pharmaceuticals (Netherlands) (NL)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2026-02-26
- DOI
- https://doi.org/10.3389/fdgth.2026.1695494
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00