Emergency department use of a high-sensitivity point-of-care troponin assay reduces length of stay: an implementation study preliminary report.

Aims: Point-of-care (POC) high-sensitivity troponin (hs-cTn) assays within a clinical pathway may safely reduce length of stay (LoS) for patients presenting to the emergency department (ED) with possible acute myocardial infarction (AMI). In this early report, we present the first evaluation of a POC hs-cTn in real-life care.

Methods And Results: In adult patients presenting to ED investigated for possible AMI, we compared the LoS in patients assessed with a troponin in the 8 weeks before (usual-care phase) and the 8 weeks following introduction of the Siemens Atellica VTLi POC hs-cTnI for decision-making (intervention phase).

Analytical verification of the Atellica VTLi point of care high sensitivity troponin I assay.

Objectives: The Siemens Point-of-Care Testing (POC) Atellica VTLi high-sensitivity troponin I (hsTnI) device has been previously validated. Verification independently provides evidence that an analytical procedure fulfils concordance with laboratory assays, imprecision, and hemolysis interference requirements.

Methods: Five whole blood samples spanning the measuring interval were analysed 20 times in succession.

Connectivity for point-of-care testing results: a call for change.

This article hopes to minimise challenges such as result transcription errors, undocumented tests and results, delays in management and over-testing, by making seven recommendations in support of implementing nation-wide connectivity for point-of-care testing. The ultimate goal is to facilitate safer and equitable point-of-care medical laboratory testing throughout the patient journey and in all geographical locations of New Zealand. The recommendations have been endorsed by the New Zealand Society for Pathologists and The New Zealand Institute of Medical Laboratory Science.

Effective resource management using machine learning in medicine: an applied example.

Background: The field of medicine is rapidly becoming digitised, and in the process passively amassing large volumes of healthcare data. Machine learning and data analytics are advancing rapidly, but these have been slow to be taken up in the day-to-day delivery of healthcare. We present an application of machine learning to optimise a laboratory testing programme as an example of benefiting from these tools.

Relative recovery of haematopoietic stem cell products after cryogenic storage of up to 19 years.

There is an increasing trend towards long-term frozen storage of haematopoietic stem cells. For such stem cells, harvested from peripheral blood (PB) or BM, it is not known if stem cell viability decreases with time. In this study, 31 separate bags of stem cell product (SCP) stored for 11-19 years (median 15 years) were assessed for total nucleated cell (TNC) count, colony forming unit-granulocyte/macrophage (CFU-GM), CD34⁺ cell count and cell viability.