Mortality associated with the perioperative management of patients with proximal femur fractures: A machine learning prediction model
Our results provide interesting elements to optimize the perioperative management of these patients. The future perspectives include validating the predictive model on external cohorts and integrating new variables to improve its accuracy.
Authors
- Carole Bechis
- Marion Poirier
- Sylvain Takerkart (ORCID: https://orcid.org/0000-0001-8410-0962)
- Stéphanie Durieux
- Pierre Simeone
- B. Lassale
- Xavier Flécher (ORCID: https://orcid.org/0000-0002-2229-3620)
- Luigi Natale
- Marie Le Baron (ORCID: https://orcid.org/0000-0002-8833-7454)
- Solène Prost (ORCID: https://orcid.org/0000-0002-3500-0655)
- Lionel Velly (ORCID: https://orcid.org/0000-0001-5009-1711)
- Bruno Pastène (ORCID: https://orcid.org/0000-0002-6836-3903)
- Damien Lami
- Geoffray Agard (ORCID: https://orcid.org/0000-0002-5095-6268)
- Marc Léone (ORCID: https://orcid.org/0000-0002-3097-758X)
- Laura Kubacsi
- Arthur Malet
- Benjamin Blondel (ORCID: https://orcid.org/0000-0001-8896-5277)
- Alisée Delbrel
- Romain Ambrosino
- Thomas A. Rocco
- François Antonini
- Christophe Jacquet
- Thibaut Florant
- Olivier Sanzeri
Institutions
- Centre National de la Recherche Scientifique (FR)
- Aix-Marseille Université (FR)
- Assistance Publique Hôpitaux de Marseille (FR)
- Hôpital Sainte-Marguerite (FR)
- Délégation Provence et Corse (FR)
- Institut de Neurosciences de la Timone (FR)
Publication Details
- Journal
- Anaesthesia Critical Care & Pain Medicine
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1016/j.accpm.2025.101707
- Primary Topic
- Hip and Femur Fractures
- Type
- article
- Field-Weighted Citation Impact
- 0.00