2025-12-12 東京科学大学
<関連情報>
- https://www.isct.ac.jp/ja/news/vj289gd4l6zn
- https://www.isct.ac.jp/plugins/cms/component_download_file.php?type=2&pageId=&contentsId=1&contentsDataId=2708&prevId=&key=9850d593d218852af53c42ab9bdbf39a.pdf
- https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2793225
機械学習アルゴリズムを用いた外傷性頭蓋内出血を検出する病院前トリアージシステム A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms
Daisu Abe, MD; Motoki Inaji, MD, PhD; Takeshi Hase, PhD;et al
JAMA Network Open Published:June 10, 2022
DOI:10.1001/jamanetworkopen.2022.16393
Key Points
Question Can machine learning algorithms be used to triage patients with head trauma according to their severity before transportation?
Findings In this cohort study of 2123 patients with head trauma, a machine learning–based prediction model detected traumatic intracranial hemorrhage with a sensitivity of 74% and a specificity of 75% by using only the patient’s prehospital information.
Meaning This study suggests that a machine learning algorithm can accurately stratify patients with head trauma according to severity in prehospital settings and may improve the prognosis of patients with severe traumatic head injury.
Abstract
Importance An adequate system for triaging patients with head trauma in prehospital settings and choosing optimal medical institutions is essential for improving the prognosis of these patients. To our knowledge, there has been no established way to stratify these patients based on their head trauma severity that can be used by ambulance crews at an injury site.
Objectives To develop a prehospital triage system to stratify patients with head trauma according to trauma severity by using several machine learning techniques and to evaluate the predictive accuracy of these techniques.
Design, Setting, and Participants This single-center retrospective cohort study was conducted by reviewing the electronic medical records of consecutive patients who were transported to Tokyo Medical and Dental University Hospital in Japan from April 1, 2018, to March 31, 2021. Patients younger than 16 years with cardiopulmonary arrest on arrival or with a significant amount of missing data were excluded.
Main Outcomes and Measures Machine learning–based prediction models to detect the presence of traumatic intracranial hemorrhage were constructed. The predictive accuracy of the models was evaluated with the area under the receiver operating curve (ROC-AUC), area under the precision recall curve (PR-AUC), sensitivity, specificity, and other representative statistics.
Results A total of 2123 patients (1527 male patients [71.9%]; mean [SD] age, 57.6 [19.8] years) with head trauma were enrolled in this study. Traumatic intracranial hemorrhage was detected in 258 patients (12.2%). Among several machine learning algorithms, extreme gradient boosting (XGBoost) achieved the mean (SD) highest ROC-AUC (0.78 [0.02]) and PR-AUC (0.46 [0.01]) in cross-validation studies. In the testing set, the ROC-AUC was 0.80, the sensitivity was 74.0% (95% CI, 59.7%-85.4%), and the specificity was 74.9% (95% CI, 70.2%-79.3%). The prediction model using the National Institute for Health and Care Excellence (NICE) guidelines, which was calculated after consultation with physicians, had a sensitivity of 72.0% (95% CI, 57.5%-83.8%) and a specificity of 73.3% (95% CI, 68.7%-77.7%). The McNemar test revealed no statistically significant differences between the XGBoost algorithm and the NICE guidelines for sensitivity or specificity (P = .80 and P = .55, respectively).
Conclusions and Relevance In this cohort study, the prediction model achieved a comparatively accurate performance in detecting traumatic intracranial hemorrhage using only the simple pretransportation information from the patient. Further validation with a prospective multicenter data set is needed.
