Machine learning and coagulation testing: the next big thing in hemostasis investigations?

Emmanuel J Favaloro, Davide Negrini

Research output: Contribution to journalArticlepeer-review

Abstract

Change has been a continuum in the field of hemostasis since the field began. The elder of us has personal recollections of starting in the field performing manual coagulation tests. He thought the workload was high, performing up to 40 prothrombin times (PTs) and activated partial thromboplastin times (APTTs) in a day. Specialist testing was also introduced, and again, many tests such as factor assays and lupus anticoagulant (LA) were also performed using manual testing – namely the manual tilt tube method (in duplicate of course) using a water bath and stop watches. To cope with the high workload, we introduced various cutting-edge innovations, such as a metal clip to hold up to eight test-tubes at a time for water bath immersion, and withdrawal. He thought he had hit the jackpot with his first automated analyzer, the Coag-A-Mate, which used a large plastic carousel with wells for the reaction and clot detection, and peristatic pumps and tubing that could do perhaps accomplish 20 coagulation tests an hour. The instrument required some time and trial and error to alter the set-up for specialised tests, so this was rarely done. The Coag-A-Mate was eventually replaced with an ACL-300R, followed by ever larger and higher throughput analyzers to cope with the growth in test number and variety, including an MDA-180 and more recently Stago Star Evolutions. Fast forwarding to 2021, and the large networks of laboratories, where Westmead is leading the introduction of 75 ACL-TOP analyzers into 60 laboratories of NSW Health [1], the Network now performs over one million PTs and one million APTTs a year. Indeed, the Westmead laboratory alone now performs around 400 PTs and 400 APTTs a day. In another setting, halfway across the world, the Verona Hospital laboratory also performs around 750 PTs and 600 APTTs per day.
Original languageEnglish
Pages (from-to)1177-1179
Number of pages3
JournalClinical Chemistry and Laboratory Medicine
Volume59
Issue number7
Early online date02 Mar 2021
DOIs
Publication statusPublished - 25 Jun 2021

Fingerprint

Dive into the research topics of 'Machine learning and coagulation testing: the next big thing in hemostasis investigations?'. Together they form a unique fingerprint.

Cite this