Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters
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 neurotrau...
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The Japan Neurosurgical Society
2025-02-01
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| Series: | Neurologia Medico-Chirurgica |
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| Online Access: | https://www.jstage.jst.go.jp/article/nmc/65/2/65_2024-0066/_pdf/-char/en |
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| author | Gaku FUJIWARA Yohei OKADA Eiichi SUEHIRO Hiroshi YATSUSHIGE Shin HIROTA Shu HASEGAWA Hiroshi KARIBE Akihiro MIYATA Kenya KAWAKITA Kohei HAJI Hideo AIHARA Shoji YOKOBORI Motoki INAJI Takeshi MAEDA Takahiro ONUKI Kotaro OSHIO Nobukazu KOMORIBAYASHI Michiyasu SUZUKI Naoto SHIOMI |
| author_facet | Gaku FUJIWARA Yohei OKADA Eiichi SUEHIRO Hiroshi YATSUSHIGE Shin HIROTA Shu HASEGAWA Hiroshi KARIBE Akihiro MIYATA Kenya KAWAKITA Kohei HAJI Hideo AIHARA Shoji YOKOBORI Motoki INAJI Takeshi MAEDA Takahiro ONUKI Kotaro OSHIO Nobukazu KOMORIBAYASHI Michiyasu SUZUKI Naoto SHIOMI |
| author_sort | Gaku FUJIWARA |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7061a1ea36ef4b0abbdc3103df95c86b |
| institution | DOAJ |
| issn | 1349-8029 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | The Japan Neurosurgical Society |
| record_format | Article |
| series | Neurologia Medico-Chirurgica |
| spelling | doaj-art-7061a1ea36ef4b0abbdc3103df95c86b2025-08-20T02:48:03ZengThe Japan Neurosurgical SocietyNeurologia Medico-Chirurgica1349-80292025-02-01652617010.2176/jns-nmc.2024-00662024-0066Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation ParametersGaku FUJIWARA0Yohei OKADA1Eiichi SUEHIRO2Hiroshi YATSUSHIGE3Shin HIROTA4Shu HASEGAWA5Hiroshi KARIBE6Akihiro MIYATA7Kenya KAWAKITA8Kohei HAJI9Hideo AIHARA10Shoji YOKOBORI11Motoki INAJI12Takeshi MAEDA13Takahiro ONUKI14Kotaro OSHIO15Nobukazu KOMORIBAYASHI16Michiyasu SUZUKI17Naoto SHIOMI18Department of Neurosurgery, Saiseikai Shiga Hospital, Imperial Gift Foundation Inc.Department of Preventive Services, School of Public Health, Kyoto UniversityDepartment of Neurosurgery, International University of Health and Welfare School of MedicineDepartment of Neurosurgery, National Hospital Organization Disaster Medical CenterDepartment of Neurosurgery, Tsuchiura Kyodo General HospitalDepartment of Neurosurgery, Kumamoto Red Cross HospitalDepartment of Neurosurgery, Sendai City HospitalDepartment of Neurosurgery, Chiba Emergency Medical CenterEmergency Medical Center, Kagawa University HospitalDepartment of Neurosurgery, Yamaguchi University School of MedicineDepartment of Neurosurgery, Hyogo Prefectural Kakogawa Medical CenterDepartment of Emergency and Critical Care Medicine, Graduate School of Medicine, Nippon Medical SchoolDepartment of Neurosurgery, Institute of Science TokyoDepartment of Neurological Surgery, Nihon University School of MedicineDepartment of Emergency Medicine, Teikyo University School of MedicineDepartment of Neurosurgery, St. Marianna University School of MedicineIwate Prefectural Advanced Critical Care and Emergency Center, Iwate Medical UniversityDepartment of Neurosurgery, Yamaguchi University School of MedicineDepartment of Critical and Intensive Care Medicine, Shiga University of Medical ScienceThis 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.https://www.jstage.jst.go.jp/article/nmc/65/2/65_2024-0066/_pdf/-char/enmachine learninganticoagulant therapyvitamin k antagonistdirect oral anticoagulantstraumatic brain injury |
| spellingShingle | Gaku FUJIWARA Yohei OKADA Eiichi SUEHIRO Hiroshi YATSUSHIGE Shin HIROTA Shu HASEGAWA Hiroshi KARIBE Akihiro MIYATA Kenya KAWAKITA Kohei HAJI Hideo AIHARA Shoji YOKOBORI Motoki INAJI Takeshi MAEDA Takahiro ONUKI Kotaro OSHIO Nobukazu KOMORIBAYASHI Michiyasu SUZUKI Naoto SHIOMI Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters Neurologia Medico-Chirurgica machine learning anticoagulant therapy vitamin k antagonist direct oral anticoagulants traumatic brain injury |
| title | Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters |
| title_full | Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters |
| title_fullStr | Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters |
| title_full_unstemmed | Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters |
| title_short | Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters |
| title_sort | development of machine learning model to predict anticoagulant use and type in geriatric traumatic brain injury using coagulation parameters |
| topic | machine learning anticoagulant therapy vitamin k antagonist direct oral anticoagulants traumatic brain injury |
| url | https://www.jstage.jst.go.jp/article/nmc/65/2/65_2024-0066/_pdf/-char/en |
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