1. Decrease of haemoconcentration reliably detects hydrostatic pulmonary oedema in dyspnoeic patients in the emergency department – a machine learning approach
- Author
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Francesco Gavelli, Luigi Mario Castello, Xavier Monnet, Danila Azzolina, Ilaria Nerici, Simona Priora, Valentina Giai Via, Matteo Bertoli, Claudia Foieni, Michela Beltrame, Mattia Bellan, Pier Paolo Sainaghi, Nello De Vita, Filippo Patrucco, Jean-Louis Teboul, and Gian Carlo Avanzi
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Pulmonary edema ,Lung water ,Haemoconcentration ,Haemodilution ,Augmented intelligence ,Medical emergencies. Critical care. Intensive care. First aid ,RC86-88.9 - Abstract
Abstract Background Haemoglobin variation (ΔHb) induced by fluid transfer through the intestitium has been proposed as a useful tool for detecting hydrostatic pulmonary oedema (HPO). However, its use in the emergency department (ED) setting still needs to be determined. Methods In this observational retrospective monocentric study, ED patients admitted for acute dyspnoea were enrolled. Hb values were recorded both at ED presentation (T0) and after 4 to 8 h (T1). ΔHb between T1 and T0 (ΔHbT1-T0) was calculated as absolute and relative value. Two investigators, unaware of Hb values, defined the cause of dyspnoea as HPO and non-HPO. ΔHbT1-T0 ability to detect HPO was evaluated. A machine learning approach was used to develop a predictive tool for HPO, by considering the ability of ΔHb as covariate, together with baseline patient characteristics. Results Seven-hundred-and-six dyspnoeic patients (203 HPO and 503 non-HPO) were enrolled over 19 months. Hb levels were significantly different between HPO and non-HPO patients both at T0 and T1 (p
- Published
- 2024
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