Predicting arterial pressure without prejudice: towards effective hypotension prediction models.

Journal: British journal of anaesthesia
Published Date: Jul 19, 2025

Abstract

Selection bias has been identified in hypotension prediction models, but its impact on an algorithm's ability to learn relevant information from the arterial waveform remains unclear. The recent study by Yang and colleagues sheds considerable light on this by training and evaluating a deep learning prediction model with biased and unbiased data selections. Unbiased training data allowed an algorithm to learn modestly more than just current blood pressure and the bias significantly distorted and inflated the positive predictive value. We discuss these findings and offer suggestions for further developing effective hypotension prediction algorithms.

Authors

  • Simon Tilma Vistisen
    From the Institute of Clinical Medicine, Aarhus University (STV), Department of Anaesthesiology & Intensive Care, Aarhus University Hospital, Denmark (STV), Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA (TJP), Department of Critical Care, University College London Hospital and Institute of Health Informatics, University College London, UK (SH) and Enversion A/S, Denmark (SML).
  • Paul Elbers
    Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence (LCCI), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals