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Oral Anti Coagulation Reversal
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Improving diagnosis and treatment decisions?

Declaration of sponsorship CSL Behring
Read time: 15 mins
Last updated:21st Nov 2020
Published:21st Nov 2020

How is technology improving diagnosis and informing treatment decisions?

The 21st century is seeing the rise of the machine with the development of applications and technology that can help inform patient diagnosis and treatment.

Technologies that enhance the abilities of healthcare professionals are already making waves in research across the world, such as the heightened interest in the utility of mHealth tools in shared decision making between atrial fibrillation (AF) patients and their clinicians when balancing the risks of venous thromboembolism12.

But can new technologies, like AI, replace doctors?

To answer this question, here’s another quick rundown of technologies that could transform healthcare; this time in the diagnosis of patients needing anticoagulation therapy.

In This Section

The race to apply new technologies that improve and miniaturise bedside diagnostic testing is rapidly accelerating.

Recently, Maji et al. described the design, fabrication, and testing of a microfluidic sensor for dielectric spectroscopy of human whole blood during coagulation3. This ‘ClotChip’ measures a patient’s bleeding-risk profile, such as the bleeding risks associated with vitamin K antagonists, at the bedside from a drop of blood.

This new hand-held device offers the potential of a point-of-care platform to assess blood coagulation and platelet activity using <10 μL of human whole blood. The bedside assessment of human whole blood could allow for the rapid reversal of oral anticoagulants with medications, such as prothrombin complex concentrates.

Similar advancements have also been made by Tripathi et al. using the iCoagLab device for blood coagulation profiling of patients with elevated risk for bleeding4 and Luna et al. using a tortuosity-powered microfluidic device for assessment of thrombosis and antithrombotic therapy in whole blood5.

As technology becomes ever smaller and more efficient, more advanced tests will be able to be performed at the bedside, both saving time and improving patient care. Speaking of small technology…

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References

  1. Zeballos-Palacios CL, Hargraves IG, Noseworthy PA, Branda ME, Kunneman M, Burnett B, et al. Developing a Conversation Aid to Support Shared Decision Making: Reflections on Designing Anticoagulation Choice. Mayo Clin Proc. 2019;94(4):686–696.
  2. Kunneman M, Branda ME, Hargraves IG, Sivly AL, Lee AT, Gorr H, et al. Assessment of Shared Decision-making for Stroke Prevention in Patients with Atrial Fibrillation: A Randomized Clinical Trial. JAMA Intern Med. 2020. doi:10.1001/jamainternmed.2020.2908.
  3. Maji D, Suster MA, Kucukal E, Sekhon UDS, Gupta A Sen, Gurkan UA, et al. ClotChip: A Microfluidic Dielectric Sensor for Point-of-Care Assessment of Hemostasis. IEEE Trans Biomed Circuits Syst. 2017;11(6):1459–1469.
  4. Tripathi MM, Tshikudi DM, Hajjarian Z, Hack DC, Van Cott EM, Nadkarni SK. Comprehensive Blood Coagulation Profiling in Patients Using iCoagLab: Comparison against Thromboelastography. Thromb Haemost. 2020;120(7):1116–1127.
  5. David L, Navaneeth P, Tanmay M, Justin B, Pranav G, Vadim K, et al. Tortuosity-powered microfluidic device for assessment of thrombosis and antithrombotic therapy in whole blood. Sci Rep. 2020;10(5742). doi:https://doi.org/10.1038/s41598-020-62768-4.
  6. Szydzik C, Brazilek RJ, Akbaridoust F, de Silva C, Moon M, Marusic I, et al. Active Micropump-Mixer for Rapid Antiplatelet Drug Screening in Whole Blood. Anal Chem. 2019;91(16):10830–10839.
  7. Zhou Y, Yasumoto A, Lei C, Huang CJ, Kobayashi H, Wu Y, et al. Intelligent classification of platelet aggregates by agonist type. Elife. 2020;9:1–15.