Decrease of haemoconcentration reliably detects hydrostatic pulmonary oedema in dyspnoeic patients in the emergency department – a machine learning approach
ΔHb<sub>T1-T0</sub> in patients admitted for acute dyspnoea reliably identifies HPO in the ED setting. The machine learning predictive tool may represent a performing and clinically handy tool for confirming HPO.
Authors
- Filippo Patrucco (ORCID: https://orcid.org/0000-0002-4794-8734)
- Simona Priora
- Matteo Bertoli (ORCID: https://orcid.org/0000-0001-6936-4249)
- Mattia Bellan (ORCID: https://orcid.org/0000-0003-1488-8736)
- Pier Paolo Sainaghi (ORCID: https://orcid.org/0000-0001-8322-9158)
- Nello De Vita (ORCID: https://orcid.org/0000-0003-3218-8653)
- Ilaria Nerici (ORCID: https://orcid.org/0000-0002-3713-4550)
- Jean–Louis Teboul (ORCID: https://orcid.org/0000-0002-5748-7820)
- Michela Beltrame (ORCID: https://orcid.org/0000-0002-1656-5482)
- Valentina Giai Via (ORCID: https://orcid.org/0000-0003-0357-8011)
- Luigi Mario Castello (ORCID: https://orcid.org/0000-0001-8248-1976)
- Xavier Monnet (ORCID: https://orcid.org/0000-0001-6820-2678)
- Gian Carlo Avanzi (ORCID: https://orcid.org/0000-0001-7611-8345)
- Danila Azzolina (ORCID: https://orcid.org/0000-0002-8185-5742)
- Claudia Foieni
- Francesco Gavelli
Institutions
- Università degli Studi del Piemonte Orientale “Amedeo Avogadro” (IT)
- University of Ferrara (IT)
- Assistance Publique – Hôpitaux de Paris (FR)
- Azienda Ospedaliera Ospedale Maggiore (IT)
- Bicêtre Hospital (FR)
Publication Details
- Journal
- International Journal of Emergency Medicine
- Published
- 2024-09-05
- DOI
- https://doi.org/10.1186/s12245-024-00698-y
- Citations
- 1
- Primary Topic
- Ultrasound in Clinical Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.42
Funders
- European Society of Intensive Care Medicine