Predicting haemoglobin deferral using machine learning models: Can we use the same prediction model across countries?

Journal: Vox sanguinis
PMID: 38637123

Abstract

BACKGROUND AND OBJECTIVES: Personalized donation strategies based on haemoglobin (Hb) prediction models may reduce Hb deferrals and hence costs of donation, meanwhile improving commitment of donors. We previously found that prediction models perform better in validation data with a high Hb deferral rate. We therefore investigate how Hb deferral prediction models perform when exchanged with other blood establishments.

Authors

  • Amber Meulenbeld
    Donor Medicine Research, Sanquin Research, Amsterdam, The Netherlands.
  • Jarkko Toivonen
    Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland.
  • Marieke Vinkenoog
    Donor Medicine Research, Sanquin Research, Amsterdam, The Netherlands.
  • Tinus Brits
    Business Intelligence, South African National Blood Service, Johannesburg, South Africa.
  • Ronel Swanevelder
    Business Intelligence, South African National Blood Service, Johannesburg, South Africa.
  • Dorien de Clippel
    Dienst voor het Bloed, Belgian Red Cross Ugent, Ghent, Belgium.
  • Veerle Compernolle
    Dienst voor het Bloed, Belgian Red Cross Ugent, Ghent, Belgium.
  • Surendra Karki
    Research and Development, Australian Red Cross Lifeblood, Sydney, Australia.
  • Marijke Welvaert
    Research and Development, Australian Red Cross Lifeblood, Sydney, Australia.
  • Katja van den Hurk
    Donor Medicine Research, Sanquin Research, Amsterdam, The Netherlands.
  • Joost van Rosmalen
    Department of Epidemiology, Erasmus MC.
  • Emmanuel Lesaffre
    L-Biostat, KU Leuven, Leuven, Belgium.
  • Mart Janssen
    Donor Medicine Research, Sanquin Research, Amsterdam, The Netherlands.
  • Mikko Arvas
    VTT Technical Research Centre of Finland, Espoo, Finland.

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