Abstract/Summary

Background The objective of this study was to undertake an early economic evaluation to analyse the potential costs and benefits associated with adopting a high sensitivity troponin (hs-cTn) at the Point of Care (POC) in the Emergency Department (ED) diagnostic pathway for suspected Acute Coronary Syndrome (ACS) patients in line with National Institute for Health and Care Excellence (NICE) Diagnostics Guidance (DG15) and NICE Clinical Guideline (CG95) as practised in the NHS in England. Methods A decision tree analysis was undertaken to compare the current 60 to 90 minutes turnaround time for the standard laboratory hs-cTn test with an expected 20-minute turnaround time for a POC hs-cTn test. Three routes through the chest pain pathway were modelled based on the hs-cTn pathway used in Oxford University Hospitals (OUH) NHS Foundation Trust. Sensitivity analysis was performed. Results The results indicate that if a hs-cTn POC test is used to diagnose patients in routes 1 to 3 of the diagnostic pathway for suspected ACS patients at ED, it potentially saves per patient costs of £33 in Routes 1 & 3 and £42 in Route 2. Moreover, it can also help in easing the pressure at ED as it enables diagnosis to be made between 55 to 70 minutes earlier across the 3 pathway routes. A hs-cTn POC test also has potential in achieving a ‘rule-in’ diagnosis for patients to speed up the treatment pathway for improved prognosis. The sensitivity analysis indicates that savings per patient increase as the nursing time for patient monitoring is varied between 70% and 90% . Furthermore, there is savings per patient even when the cost of the hs-cTn biomarker is varied by ~£10. Conclusions Use of a hs-cTn test at POC can save between £33 and £42 per patient in ED when compared to the standard laboratory test. When such a POC test is developed, an evaluation validating the accuracy of the device will be needed together with a study of its clinical performance in a health care setting. The study should include a formal economic evaluation with real-world data alongside an efficacy/effectiveness study.