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Decrease of haemoconcentration reliably detects hydrostatic pulmonary oedema in dyspnoeic patients in the emergency

Francesco Gavelli1,2,3, Luigi Mario Castello4,5, Xavier Monnet6

  • 1Department of Translational Medicine, Università degli Studi del Piemonte Orientale, Via Solaroli 17, Novara, 28100, Italy. francesco.gavelli@uniupo.it.

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Haemoglobin variation (ΔHb) effectively identifies hydrostatic pulmonary oedema (HPO) in emergency departments. A machine learning tool using ΔHb shows promise for diagnosing HPO in acute dyspnea patients.

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Area of Science:

  • Emergency Medicine
  • Pulmonary Medicine
  • Medical Diagnostics

Background:

  • Hydrostatic pulmonary oedema (HPO) diagnosis can be challenging in emergency settings.
  • Haemoglobin variation (ΔHb) due to fluid shifts is a proposed indicator for HPO.
  • The utility of ΔHb in the emergency department (ED) requires further investigation.

Purpose of the Study:

  • To evaluate the effectiveness of ΔHb in detecting HPO in ED patients with acute dyspnoea.
  • To develop and assess a machine learning model for HPO prediction using ΔHb and patient characteristics.

Main Methods:

  • Observational retrospective study of 706 ED patients with acute dyspnoea.
  • Recorded haemoglobin (Hb) at presentation (T0) and 4-8 hours later (T1).
  • Calculated ΔHb (absolute and relative) and used machine learning (Gradient Boosting Machine) for HPO prediction.

Main Results:

  • Significant differences in Hb levels and ΔHb between HPO and non-HPO groups (p < 0.001).
  • Relative ΔHb of -5% showed high accuracy (AUROC 0.901) in detecting HPO.
  • Machine learning model, particularly Gradient Boosting, demonstrated excellent HPO predictive ability, with ΔHb as a key covariate.

Conclusions:

  • ΔHb is a reliable marker for identifying HPO in ED patients with acute dyspnoea.
  • A machine learning-based predictive tool incorporating ΔHb offers a clinically useful method for HPO confirmation.