Re-engineering the clinical approach to suspected cardiac chest pain assessment in the emergency department by expediting research evidence to practice using artificial intelligence. (RAPIDx AI)-a cluster randomized study design.

Journal: American heart journal
PMID: 39993551

Abstract

BACKGROUND: Clinical work-up for suspected cardiac chest pain is resource intensive. Despite expectations, high-sensitivity cardiac troponin assays have not made decision making easier. The impact of recently validated rapid triage protocols including the 0-hour/1-hour hs-cTn protocols on care and outcomes may be limited by the heterogeneity in interpretation of troponin profiles by clinicians. We have developed machine learning (ML) models which digitally phenotype myocardial injury and infarction with a high predictive performance and provide accurate risk assessment among patients presenting to EDs with suspected cardiac symptoms. The use of these models may support clinical decision-making and allow the synthesis of an evidence base particularly in non-T1MI patients however prospective validation is required.

Authors

  • Ehsan Khan
    College of Medicine and Public Health, Flinders University of South Australia, Adelaide, Australia; Department of Health, SA Health, South Australian, Adelaide, Australia.
  • Kristina Lambrakis
    Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia; MonashHeart, Monash Health, Melbourne, Victoria, Australia; College of Medicine and Public Health, Flinders University, Adelaide, South, Australia.
  • Tom Briffa
    Cardiovascular Epidemiology Research Centre, School of Population and Global Health, The University of Western Australia, Crawley, Western Australia, Australia.
  • Louise A Cullen
    Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia; School of Public Health, Queensland University of Technology, Brisbane, Australia; School of Medicine, University of Queensland, Brisbane, Australia.
  • Jonathon Karnon
    College of Medicine and Public Health, Flinders University of South Australia, Adelaide, Australia.
  • Cynthia Papendick
    Department of Health, SA Health, South Australian, Adelaide, Australia.
  • Stephen Quinn
    Department of Statistics, Department of Health Science and Biostatistics, Swinburne University of Technology, Melbourne, Australia.
  • Phil Tideman
    College of Medicine and Public Health, Flinders University of South Australia, Adelaide, Australia; Department of Health, SA Health, South Australian, Adelaide, Australia.
  • Anton van den Hengel
  • Johan Verjans
  • Derek P Chew
    Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia; MonashHeart, Monash Health, Melbourne, Victoria, Australia; College of Medicine and Public Health, Flinders University, Adelaide, South, Australia.

Keywords

External Resources

Popular Topics
Recent Journals