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Related Experiment Video

Updated: Jun 4, 2025

Author Spotlight: Deciphering Coagulation Disorders in Traumatic Brain Injury Patients
04:56

Author Spotlight: Deciphering Coagulation Disorders in Traumatic Brain Injury Patients

Published on: August 4, 2023

688

Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using

Gaku Fujiwara1, Yohei Okada2,3, Eiichi Suehiro4

  • 1Department of Neurosurgery, Saiseikai Shiga Hospital, Imperial Gift Foundation Inc.

Neurologia Medico-Chirurgica
|December 25, 2024
PubMed
Summary

This study analyzed coagulation patterns in elderly traumatic brain injury patients on anticoagulants. A machine learning model was developed to predict anticoagulant type using PT-INR and APTT, showing characteristic patterns.

Keywords:
anticoagulant therapydirect oral anticoagulantsmachine learningtraumatic brain injuryvitamin K antagonist

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Area of Science:

  • Neurology
  • Pharmacology
  • Biostatistics

Background:

  • Geriatric patients with traumatic brain injury (TBI) often require anticoagulation therapy.
  • Understanding coagulation parameter patterns associated with different anticoagulants is crucial for managing TBI patients.

Purpose of the Study:

  • To investigate anticoagulation therapy patterns and coagulation parameters in elderly TBI patients.
  • To develop a predictive model for anticoagulant type using coagulation parameters.

Main Methods:

  • Retrospective analysis of a nationwide neurotrauma database.
  • Categorization of patients into no anticoagulant, direct oral anticoagulant (DOAC), and vitamin K antagonist (VKA) groups.
  • Machine learning model development using prothrombin time-international normalized ratio (PT-INR) and activated partial thromboplastin time (APTT) for prediction.

Main Results:

  • Significant differences in PT-INR and APTT were observed between groups receiving no anticoagulant, DOACs, and VKAs.
  • A machine learning model demonstrated acceptable predictive ability for anticoagulant type based on PT-INR and APTT.
  • Heat map visualization revealed characteristic patterns between coagulation parameters and anticoagulant use.

Conclusions:

  • Coagulation parameters exhibit distinct patterns related to anticoagulant use in elderly TBI patients.
  • A pilot model using PT-INR and APTT can predict anticoagulant type, aiding clinical decision-making.