Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters - Gaku FUJIWARA; Yohei OKADA; Eiichi SUEHIRO; Hiroshi YATSUSHIGE; Shin HIROTA; Shu HASEGAWA; Hiroshi KARIBE; Akihiro MIYATA; Kenya KAWAKITA; Kohei HAJI; Hideo AIHARA; Shoji YOKOBORI; et al. (Neurologia Medico-Chirurgica)
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.