Optimising coronary imaging decisions with machine learning: an external validation study.

Journal: Open heart
PMID: 40280592

Abstract

BACKGROUND: Exclusion of coronary stenosis in individuals with suggestive symptoms is challenging. Cardiac CT or coronary angiography is often used but is inefficient and costly and involves risks. Sex-stratified algorithms based on electronic health records (EHRs) could be a non-invasive alternative for excluding coronary stenosis, yet their performance may vary by healthcare settings. Thus, external validation is crucial for determining their generalisability. This study aimed to externally validate sex-stratified machine learning algorithms based on EHR data to predict the absence of coronary stenosis, evaluated in diverse clinical settings.

Authors

  • L Malin Overmars
    Central Diagnostic Laboratory, University Medical Centre Utrecht, Utrecht, The Netherlands l.m.overmars-2@umcutrecht.nl.
  • Bram van Es
    Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. b.vanes-3@umcutrecht.nl.
  • Floor Groepenhoff
    Experimental Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands.
  • Mark C H De Groot
    Central Diagnostic Laboratory, University Medical Centre Utrecht, Utrecht, The Netherlands.
  • G Aernout Somsen
    Cardiology, Cardiology Centers of the Netherlands, Utrecht, The Netherlands.
  • Sophie Heleen Bots
    Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht University Utrecht Institute for Pharmaceutical Sciences, Utrecht, The Netherlands.
  • I Igor Tulevski
    Cardiology, Cardiology Centers of the Netherlands, Amsterdam, The Netherlands.
  • Leonard Hofstra
    Cardiology, Cardiology Centers of the Netherlands, Amsterdam, The Netherlands.
  • Hester M den Ruijter
    Laboratory for Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
  • Wouter W van Solinge
    Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
  • Imo Hoefer
    Central Diagnostic Laboratory, University Medical Centre Utrecht, Utrecht, The Netherlands.
  • Saskia Haitjema
    Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.

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