Decrease of haemoconcentration reliably
detects hydrostatic pulmonary oedema
in dyspnoeic patients in the emergency
department – a machine learning approach

Decrease of haemoconcentration reliably
detects hydrostatic pulmonary oedema
in dyspnoeic patients in the emergency
department – a machine learning approach

Pulmonary edema, Lung water, Haemoconcentration, Haemodilution, Augmented intelligence

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.
Keywords Pulmonary edema, Lung water, Haemoconcentration, Haemodilution, Augmented intelligence

Francesco Gavelli1,2,3*, Luigi Mario Castello1,2, Xavier Monnet3

, Danila Azzolina4

, Ilaria Nerici1,2, Simona Priora1,2,

Valentina Giai Via1,2, Matteo Bertoli1,2, Claudia Foieni1,2, Michela Beltrame1,2, Mattia Bellan1

, Pier Paolo Sainaghi1
,

Nello De Vita1

, Filippo Patrucco1

, Jean-Louis Teboul3

and Gian Carlo Avanzi1,2

https://doi.org/10.1186/s12245-024-00698-y

2024

Peri Irawan

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