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- Title
Decrease of haemoconcentration reliably detects hydrostatic pulmonary oedema in dyspnoeic patients in the emergency department – a machine learning approach.
- Authors
Gavelli, Francesco; Castello, Luigi Mario; Monnet, Xavier; Azzolina, Danila; Nerici, Ilaria; Priora, Simona; Via, Valentina Giai; Bertoli, Matteo; Foieni, Claudia; Beltrame, Michela; Bellan, Mattia; Sainaghi, Pier Paolo; De Vita, Nello; Patrucco, Filippo; Teboul, Jean-Louis; Avanzi, Gian Carlo
- 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 < 0.001). ΔHbT1-T0 were more pronounced in HPO than non-HPO patients, both as relative (-8.2 [-11.2 to -5.6] vs. 0.6 [-2.1 to 3.3] %) and absolute (-1.0 [-1.4 to -0.8] vs. 0.1 [-0.3 to 0.4] g/dL) values (p < 0.001). A relative ΔHbT1-T0 of -5% detected HPO with an area under the receiver operating characteristic curve (AUROC) of 0.901 [0.896–0.906]. Among the considered models, Gradient Boosting Machine showed excellent predictive ability in identifying HPO patients and was used to create a web-based application. ΔHbT1-T0 was confirmed as the most important covariate for HPO prediction. Conclusions: ΔHbT1-T0 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.
- Subjects
PREDICTIVE tests; PATIENTS; RECEIVER operating characteristic curves; ACADEMIC medical centers; PREDICTION models; HEMOGLOBINS; PULMONARY edema; SCIENTIFIC observation; HOSPITAL emergency services; RETROSPECTIVE studies; EMERGENCY medical services; DESCRIPTIVE statistics; DYSPNEA; MACHINE learning
- Publication
International Journal of Emergency Medicine, 2024, Vol 17, Issue 1, p1
- ISSN
1865-1372
- Publication type
Article
- DOI
10.1186/s12245-024-00698-y