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Review
. 2023 Sep 25;59(10):1714.
doi: 10.3390/medicina59101714.

Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention

Affiliations
Review

Artificial Intelligence and Neurosurgery: Tracking Antiplatelet Response Patterns for Endovascular Intervention

Khushi Saigal et al. Medicina (Kaunas). .

Abstract

Platelets play a critical role in blood clotting and the development of arterial blockages. Antiplatelet therapy is vital for preventing recurring events in conditions like coronary artery disease and strokes. However, there is a lack of comprehensive guidelines for using antiplatelet agents in elective neurosurgery. Continuing therapy during surgery poses a bleeding risk, while discontinuing it before surgery increases the risk of thrombosis. Discontinuation is recommended in neurosurgical settings but carries an elevated risk of ischemic events. Conversely, maintaining antithrombotic therapy may increase bleeding and the need for transfusions, leading to a poor prognosis. Artificial intelligence (AI) holds promise in making difficult decisions regarding antiplatelet therapy. This paper discusses current clinical guidelines and supported regimens for antiplatelet therapy in neurosurgery. It also explores methodologies like P2Y12 reaction units (PRU) monitoring and thromboelastography (TEG) mapping for monitoring the use of antiplatelet regimens as well as their limitations. The paper explores the potential of AI to overcome such limitations associated with PRU monitoring and TEG mapping. It highlights various studies in the field of cardiovascular and neuroendovascular surgery which use AI prediction models to forecast adverse outcomes such as ischemia and bleeding, offering assistance in decision-making for antiplatelet therapy. In addition, the use of AI to improve patient adherence to antiplatelet regimens is also considered. Overall, this research aims to provide insights into the use of antiplatelet therapy and the role of AI in optimizing treatment plans in neurosurgical settings.

Keywords: antiplatelet therapy; artificial intelligence; endovascular intervention.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Antiplatelet and anticoagulant regimens for stent and WEB Embolization (A). Antiplatelet and anticoagulant regimen in the pre-, intra-, and post-operative stages when utilizing stent or web-embolization for unruptured aneurysm that extends into the feeding vessel; (B) antiplatelet and anticoagulant regimen in the pre-, intra-, and post-operative stages for WEB embolization for ruptured aneurysm; (C) antiplatelet and anticoagulant regimen in the pre-, intra-, and post-operative stages for WEB embolization for aneurysm that does not extend into the feeding vessel.
Figure 2
Figure 2
Summary of Properties of Common Antiplatelet Regiments. Mechanisms of action, time of onset, and duration of action of common antiplatelet regimens including aspirin, clopidogrel, ticagrelor, prasugrel, cilostazol, cangrelor, abciximab, eptifibatide, and tirofiban [11].
Figure 3
Figure 3
VerifyNow Test Protocol to Adjust Antiplatelet Therapy. There are different options for antiplatelet therapy depending on the PRU measurement prior to the procedure, and these are summarized in this figure. Once the PRU measurement is between 60 and 240, the procedure can be conducted. Following the procedure, there are various post-procedure antiplatelet regimens that can be followed depending on the initial PRU measurement prior to the procedure which are summarized in the figure.
Figure 4
Figure 4
TEG Mapping. This figure shows the activation of platelets based on the conditions amplified at various timepoints. The distance between MACK and MAactivator represents total platelet activation with the distance between MACK and MAADP/AA representing platelet inhibition.

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