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Decrease of haemoconcentration reliably detects hydrostatic pulmonary oedema in dyspnoeic patients in the emergency department - a machine learning approach.
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.
Afiliación
  • Gavelli F; Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Via Solaroli 17, Novara, 28100, Italy. francesco.gavelli@uniupo.it.
  • Castello LM; Emergency Medicine Department, AOU Maggiore della Carità di Novara, C.so Mazzini 18, Novara, 28100, Italy. francesco.gavelli@uniupo.it.
  • Monnet X; Service de médecine intensive-réanimation, Hôpital de Bicêtre, Le Kremlin-Bicêtre, Hôpitaux universitaires Paris- Saclay, APHP, rue du Général Leclerc, Paris, France. francesco.gavelli@uniupo.it.
  • Azzolina D; Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Via Solaroli 17, Novara, 28100, Italy.
  • Nerici I; Emergency Medicine Department, AOU Maggiore della Carità di Novara, C.so Mazzini 18, Novara, 28100, Italy.
  • Priora S; Service de médecine intensive-réanimation, Hôpital de Bicêtre, Le Kremlin-Bicêtre, Hôpitaux universitaires Paris- Saclay, APHP, rue du Général Leclerc, Paris, France.
  • Via VG; Department of Environmental and Preventive Science, University of Ferrara, Ferrara, Italy.
  • Bertoli M; Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Via Solaroli 17, Novara, 28100, Italy.
  • Foieni C; Emergency Medicine Department, AOU Maggiore della Carità di Novara, C.so Mazzini 18, Novara, 28100, Italy.
  • Beltrame M; Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Via Solaroli 17, Novara, 28100, Italy.
  • Bellan M; Emergency Medicine Department, AOU Maggiore della Carità di Novara, C.so Mazzini 18, Novara, 28100, Italy.
  • Sainaghi PP; Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Via Solaroli 17, Novara, 28100, Italy.
  • De Vita N; Emergency Medicine Department, AOU Maggiore della Carità di Novara, C.so Mazzini 18, Novara, 28100, Italy.
  • Patrucco F; Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Via Solaroli 17, Novara, 28100, Italy.
  • Teboul JL; Emergency Medicine Department, AOU Maggiore della Carità di Novara, C.so Mazzini 18, Novara, 28100, Italy.
  • Avanzi GC; Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Via Solaroli 17, Novara, 28100, Italy.
Int J Emerg Med ; 17(1): 114, 2024 Sep 05.
Article en En | MEDLINE | ID: mdl-39237860
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Int J Emerg Med Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Int J Emerg Med Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido