None listed
Conditions
Brief summary
Within Australia, suspected cardiac chest pain represents nearly 1 million emergency department (ED) presentations every year. Most patients are eventually not diagnosed with acute coronary syndromes (ACS). Further, the clinical work-up of patients with suspected ACS is laborious and complex, often leading to unnecessary invasive tests. In an effort to improve decision-making, and thereby reduce unnecessary risk to patients and associated health-economic impacts, this study will elucidate the pivotal role of artificial intelligence (AI) as an aid in ACS diagnosis. This study will implement and evaluate the system-level intervention of AI-based decision support for clinical assessment of suspected cardiac chest pain in the reduction of death, myocardial infarction and 12-month readmissions. Further, the study will also provide the cost-effectiveness of embedding AI-based decision support in routine clinical assessment of suspected chest-pain and ACS.
Interventions
This study will use AI-based support for clinical decision in a system-wide approach. Real-time health data will be assimilated within the clinical decision-support tool which will be deployed in South Australian emergency departments. This AI clinical support aid will provide clinicians with objective patient-specific diagnostic probabilities and prognostic assessments alongside recommended evidence-based clinical management.
The AI-based support will be assimilated systems wide thus obtainin
This study will use AI-based support for clinical decision in a system-wide approach. Real-time health data will be assimilated within the clinical decision-support tool which will be deployed in South Australian emergency departments. This AI clinical support aid will provide clinicians with objective patient-specific diagnostic probabilities and prognostic assessments alongside recommended evidence-based clinical management. The AI-based support will be assimilated systems wide thus obtaining an opt-in participant consent form will be operationally unfeasible. However, all patients will be able to opt-out of providing their data at any point of the study. To enable informed patient participation, posters and information sheets will be accessible to patients, their clinical team/clinician and/or their next-of-kin. In the hospitals allocated to the intervention arm, real-time health data will be assimilated within the electronic decision-support tool being developed in partnership with Siemens Healthineers. This will be a web-based tool, accessible on any computer or mobile device, and will require login credentials. Data assimilation will occur in an automated manner where possible, with manual entry occurring only as required (determined by electronic data system maturity at site). The decision-support tool will then provide clinicians with objective patient-specific diagnostic probabilities (i.e. the likelihood for Type 1 MI, vs Type 2 MI, vs cardiac injury etc.) and prognostic assessments alongside recommended clinical management. Specifically, the AI algorithm will report the probabilities for the various types of myocardial infarction and myocardial injury. Importantly, since a therapeutic evidence base exists only for Type 1 myocardial infarction, treatment recommendations will only be presented when the probability for this condition exceeds 90%. In all other scenarios, only ongoing diagnostic recommendations will be made. This intervention does not mandate any clinical procedures. However if any procedures are required, the participant will undergo the standard medical procedure consent process, conducted by appropriately qualified medical personnel in line with SA Health policies, which is independent of this study. This AI-based support will be trialed in South Australian Hospital Emergency Departments for a period of 12 months post-index presentation for the last randomised participant. Adherence to the study will be monitored through data linkage of health system data and interrogation of medical records as required.
Sponsors
Flinders University of South Australia
Study design
Allocation
Randomised controlled trial
Intervention model
Parallel
Primary purpose
Diagnosis
Masking
Blinded (masking used) (Investigator, Outcomes Assessor)
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Patients presenting to the emergency department will be considered eligible for analysis if they meet all of the following: a) Clinical features of chest pain or suspected ACS as the principal cause; and b) At least one high-sensitivity troponin T assay is drawn; and c) Age of 18 years or older
Exclusion criteria
Patients presenting to the ED will be considered ineligible for analysis if they meet any of the following: a) Are re-presenting with suspected cardiac chest pain within 30 days of last presentation for suspected cardiac chest pain; or b) Arrive as a transfer after initial assessment within another hospital ED; or c) Reside interstate or overseas; or d) Wish to opt-out.
Outcome results
None listed