Re-engineering the clinical approach to suspected cardiac chest pain assessment in the emergency department by expediting research evidence to practice using artficial intelligence. (RAPIDx AI) - a cluster randomised study design
Date
2025
Authors
Khan, E.
Lambrakis, K.
Briffa, T.
Cullen, L.A.
Karnon, J.
Papendick, C.
Quinn, S.
Tideman, P.
Hengel, A.V.D.
Verjans, J.
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Journal article
Citation
American Heart Journal, 2025; 285:106-118
Statement of Responsibility
Ehsan Khan, Kristina Lambrakis, Tom Briffa, Louise A Cullen, Jonathon Karnon, Cynthia Papendick, Stephen Quinn, Phil Tideman, Anton Van Den Hengel, Johan Verjans, and Derek P. Chew
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Abstract
Background: Clinical work-up for suspected cardiac chest pain is resource intensive. Despite expectations, highsensitivity 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 inter- pretation 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. Objective: We propose that integrating validated real-time artificial intelligence (AI) methods into clinical care may better support clinical decision-making and establish the foundation for a self-learning health system. Design: This prospective, multicenter, open-label, cluster-randomized clinical trial within blinded endpoint adjudication across 12 hospitals (n = 20,000) will randomize sites to the clinical decision-support tool or continue current standard of care. The clinical decision support tool will utilize ML models to provide objective patient-specific diagnostic probabilities (ie, likelihood for Type 1 myocardial infarction [MI] versus Type 2 MI/Acute Myocardial Injury versus Chronic Myocardial Injury etc.) and prognostic assessments. The primary outcome is the composite of cardiovascular mortality, new or recurrent MI and unplanned hospital re-admission at 12 months post index presentation. Summary: Supporting clinicians with a decision support tool that utilizes AI has the potential to provide better diagnostic and prognostic assessment thereby improving clinical efficiency and establish a self-learning health system continually improving risk assessment, quality and safety. Trial registration: ANZCTR, Registration Number: ACTRN12620001319965, https://www.anzctr.org.au/.
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© 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)