90-day mortality prediction in elective visceral surgery using machine learning: a retrospective multicenter development, validation, and comparison study
ML offers robust preoperative risk stratification for 90DM in elective visceral surgery. Leveraging training across multi-organ cohorts may improve accuracy and robustness compared to organ-specific models. Prospective studies are needed to confirm the potential of ML in surgical outcome prediction.
Authors
- Bjarne Pfitzner (ORCID: https://orcid.org/0000-0001-7824-8872)
- Robert Ahlborn (ORCID: https://orcid.org/0000-0002-9371-0452)
- Robin P. van de Water (ORCID: https://orcid.org/0000-0002-2895-4872)
- Bert Arnrich (ORCID: https://orcid.org/0000-0001-8380-7667)
- Axel Winter (ORCID: https://orcid.org/0000-0001-7435-4993)
- Igor M. Sauer (ORCID: https://orcid.org/0000-0001-9355-937X)
- Christian Hans Wolfgang Schineis (ORCID: https://orcid.org/0000-0001-9189-6751)
- Johann Pratschke (ORCID: https://orcid.org/0000-0001-9839-1369)
- Christoph Riepe (ORCID: https://orcid.org/0009-0003-3763-404X)
- Maximilian Schulze (ORCID: https://orcid.org/0000-0001-5996-948X)
- Katharina Beyer (ORCID: https://orcid.org/0000-0001-7578-280X)
- Max M. Maurer
- Lara Faraj (ORCID: https://orcid.org/0009-0006-0790-5226)
- Daniela Zuluaga
Institutions
- Hasso Plattner Institute (DE)
- University of Potsdam (DE)
- Humboldt-Universität zu Berlin (DE)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Einstein Center for Neurosciences Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1097/js9.0000000000002372
- Citations
- 4
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 5.55