ArticleNeurologia medico-chirurgica2025
Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters.
Article in Neurologia medico-chirurgica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Development and Validation of a Machine Learning Model to Predict Oral Anticoagulant Use in Stroke From Prothrombin Time-International Normalized Ratio and Activated Partial Thromboplastin Time.Journal of the American Heart Association · 2026Article
- Minimally Invasive and Proactive Approaches for Treatment of Acute Traumatic Brain Injury in Elderly Patients.Journal of clinical medicine · 2025Review
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
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19 authors.
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Abstract
This study aimed to investigate the patterns of anticoagulation therapy and coagulation parameters and to develop a prediction model to predict the type of anticoagulation therapy in geriatric patients with traumatic brain injury. A retrospective analysis was performed using the nationwide neurotrauma database of Japan. Elderly patients (≥65 years) with traumatic brain injury. Patients were divided into 3 groups based on their daily anticoagulant medication (none, direct oral anticoagulant [DOAC], and vitamin K antagonist [VKA]), and coagulation parameters were compared in each group. We then developed a machine-learning model to predict the anticoagulant using coagulation parameters and visualized the pattern using a heat map. A total of 495 patients were enrolled and divided into 3 groups: none (n = 439), DOACs (n = 37), and VKA (n = 19). Comparing none to DOAC and DOAC to VKA for prothrombin time-international normalized ratio (PT-INR), the mean difference and 95% confidence intervals (CIs) were 0.38 (95% CI: 0.59-0.17) and 1.56 (95% CI: 1.21-1.90), and for activated partial thromboplastin time (APTT), the mean difference between none to DOAC and DOAC to VKA was 3.46 (95% CI: 0.98-5.94) and 95% CI was 7.39 (95% CI: 3.29-11.48). A prediction model for the type of anticoagulant used by PT-INR and APTT was developed using machine-learning methods, and a heat map visually revealed their relationship with acceptable predictive ability. This study revealed the characteristic patterns of coagulation parameters in anticoagulants and a pilot model to predict anticoagulant use.
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