Widespread Introduction of a High-Sensitivity Troponin Assay: Assessing the Impact on Patients and Health Services.
Jaimi H GreensladeWilliam ParsonageLaura ForanLouise McCormackSarah AshoverTanya MilburnSara BerndtMartin ThanDavid C BrainLouise Ann CullenPublished in: Journal of clinical medicine (2020)
Adoption of High-sensitivity troponin (hs-cTn) assays by hospitals worldwide is increasing. We sought to determine the effects of a simultaneous state-wide hs-cTn assay introduction on the implementing health service. A quasi-experimental pre-post design was used. Participants included all adult patients presenting to 21 Australian hospitals who had troponin testing commenced within the Emergency Department (ED). Data were collected for 124,357 episodes of care between 30 April 2018 and 23 April 2019; six months pre- and six months post-implementation of the assay. The primary outcome was hospital length of stay (LOS). Secondary outcomes included ED LOS, 90-day cardiovascular mortality, elevated troponin, diagnosis of acute myocardial infarction (AMI), admission to a cardiology ward, invasive cardiac procedures, and total hospital costs. Following hs-cTn implementation, there was a 1.9-h (95% CI: -2.9 to -1.0 h) reduction in overall LOS. This equated to a cost saving of over 9 million Australian dollars per year. There was no increase in diagnosis of AMI, invasive cardiac procedures or ward admissions. The use of hs-cTn assays facilitates important benefits for health services by enabling more rapid evaluation protocols within the ED. This benefit may be considerable given the large cohort of emergency patients with possible ACS.
Keyphrases
- emergency department
- healthcare
- acute myocardial infarction
- high throughput
- quality improvement
- left ventricular
- adverse drug
- end stage renal disease
- percutaneous coronary intervention
- primary care
- newly diagnosed
- electronic health record
- ejection fraction
- acute coronary syndrome
- acute care
- single cell
- type diabetes
- cardiovascular events
- prognostic factors
- machine learning
- peritoneal dialysis
- patient reported outcomes
- pain management
- acute kidney injury
- artificial intelligence
- chronic pain