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4 "Se Heon Kim"
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Original Article
Predicting 30-day mortality in severely injured elderly patients with trauma in Korea using machine learning algorithms: a retrospective study
Jonghee Han, Su Young Yoon, Junepill Seok, Jin Young Lee, Jin Suk Lee, Jin Bong Ye, Younghoon Sul, Se Heon Kim, Hong Rye Kim
J Trauma Inj. 2024;37(3):201-208.   Published online August 8, 2024
DOI: https://doi.org/10.20408/jti.2024.0024
  • 786 View
  • 24 Download
AbstractAbstract PDF
Purpose
The number of elderly patients with trauma is increasing; therefore, precise models are necessary to estimate the mortality risk of elderly patients with trauma for informed clinical decision-making. This study aimed to develop machine learning based predictive models that predict 30-day mortality in severely injured elderly patients with trauma and to compare the predictive performance of various machine learning models. Methods: This study targeted patients aged ≥65 years with an Injury Severity Score of ≥15 who visited the regional trauma center at Chungbuk National University Hospital between 2016 and 2022. Four machine learning models—logistic regression, decision tree, random forest, and eXtreme Gradient Boosting (XGBoost)—were developed to predict 30-day mortality. The models’ performance was compared using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, specificity, F1 score, as well as Shapley Additive Explanations (SHAP) values and learning curves. Results: The performance evaluation of the machine learning models for predicting mortality in severely injured elderly patients with trauma showed AUC values for logistic regression, decision tree, random forest, and XGBoost of 0.938, 0.863, 0.919, and 0.934, respectively. Among the four models, XGBoost demonstrated superior accuracy, precision, recall, specificity, and F1 score of 0.91, 0.72, 0.86, 0.92, and 0.78, respectively. Analysis of important features of XGBoost using SHAP revealed associations such as a high Glasgow Coma Scale negatively impacting mortality probability, while higher counts of transfused red blood cells were positively correlated with mortality probability. The learning curves indicated increased generalization and robustness as training examples increased. Conclusions: We showed that machine learning models, especially XGBoost, can be used to predict 30-day mortality in severely injured elderly patients with trauma. Prognostic tools utilizing these models are helpful for physicians to evaluate the risk of mortality in elderly patients with severe trauma.
Summary
Case Report
Visual Disturbance Caused by a Nail Gun-Induced Penetrating Brain Injury
Jin Bong Ye, Young Hoon Sul, Se Heon Kim, Jin Young Lee, Jin Suk Lee, Hong Rye Kim, Soo Young Yoon, Jung Hee Choi
J Trauma Inj. 2021;34(3):203-207.   Published online September 30, 2021
DOI: https://doi.org/10.20408/jti.2021.0030
  • 2,807 View
  • 74 Download
AbstractAbstract PDF

Penetrating brain injury caused by a nail gun is an uncommon clinical scenario reported in the literature. A 36-year-old male presented with a nail that had penetrated through the occipital bone. He was alert and neurologically intact except for visual disturbance. Computed tomography (CT) of the brain showed the nail lodged at the occipital lobe and the parietal lobe, with minimal intracerebral hemorrhage. The nail was placed in the occipital lobe close to the superior sagittal sinus. We removed the nail with craniotomy since the entrance of the nail was close to the superior sagittal sinus. There were no newly developed neurological deficits postoperatively. Immediate postoperative CT showed no newly developed lesions. The patient recovered well without any significant complications. Two weeks postoperatively, magnetic resonance imaging showed no remarkable lesions. The visual disturbance was followed up at the outpatient department. To summarize, we report a rare case of penetrating head injury by a nail gun and discuss relevant aspects of the clinical management.

Summary
Original Article
The Influence of Seasons and Weather on the Volume of Trauma Patients: 4 Years of Experience at a Single Regional Trauma Center
Se Heon Kim, Young Hoon Sul, Jin Young Lee, Joong Suck Kim
J Trauma Inj. 2021;34(1):21-30.   Published online March 23, 2021
DOI: https://doi.org/10.20408/jti.2020.0027
  • 4,176 View
  • 105 Download
  • 2 Citations
AbstractAbstract PDF
Purpose

The purpose of this study was to determine the influence of seasons and weather on the volume of trauma patients in central Korea.

 Methods

The records of 4,665 patients treated at Chungbuk National Hospital Regional Trauma Center from January 2016 to December 2019 were retrospectively reviewed. Meteorological data including hourly temperature (°C), precipitation (mm), humidity (%), and wind speed (m/s) for each district were collected retrospectively. Statistical analysis was done using the independent <i>t</i>-test, one-way analysis of variance (ANOVA), and linear regression analysis.

 Results

Patients’ average age was 53.66 years, with a significant difference between men (49.92 years) and women (60.48 years) (p<0.001). Rolling/slipping down was a prominent cause of injury in winter (28.4%, n=283), with statistical significance (p<0.001). Trauma occurred least frequently in winter (p=0.005). Linear regression analysis revealed an increasing number of patients as the temperature increased (p<0.05), the humidity increased (p<0.001), and the wind speed decreased (p<0.001). Precipitation did not affect patient volume (p=0.562). One-way ANOVA revealed a decreased incidence of trauma when the temperature exceeded 30°C (p<0.001), and when the humidity was more than 75%, compared to 25–50% and 50–75%.

 Conclusions

At the regional trauma center of Chungbuk National University Hospital, in central Korea, the number of trauma patients was lowest in winter, and patient volume was affected by temperature, humidity, and wind speed.

Summary

Citations

Citations to this article as recorded by  
  • Climate change and mental health in Korea: A scoping review
    Jiyoung Shin, Juha Baek, Sumi Chae
    Journal of Climate Change Research.2023; 14(6-2): 989.     CrossRef
  • The impact of COVID-19 on trauma patients and orthopedic trauma operations at a single focused training center for trauma in Korea
    Wonseok Choi, Hanju Kim, Whee Sung Son, Seungyeob Sakong, Jun-Min Cho, Nak-Jun Choi, Tae-Wook Noh, Namryeol Kim, Jae-Woo Cho, Jong-Keon Oh
    Journal of Trauma and Injury.2022; 35(3): 195.     CrossRef
Case Report
Chronic Traumatic Glass Foreign Body Removal from the Lung through a Direct Parenchymal Incision
Su Young Yoon, Si Wook Kim, Jin Suk Lee, Jin Young Lee, Jin Bong Ye, Se Heon Kim, Young Hoon Sul
J Trauma Inj. 2019;32(4):248-251.   Published online December 30, 2019
DOI: https://doi.org/10.20408/jti.2019.031
  • 4,581 View
  • 63 Download
  • 1 Citations
AbstractAbstract PDF

Traumatic intrapulmonary glass foreign bodies that are missed on an initial examination can migrate and lead to severe complications. Here, we present a rare case of a traumatic intrapulmonary glass foreign body surgically removed by a direct pulmonary incision, which preserved the pulmonary parenchyma and avoided severe complications caused by migration.

Summary

Citations

Citations to this article as recorded by  
  • Thoracoscopic retrieval of an intrapulmonary sewing needle: A case report
    Houssem Messaoudi, Imen Ben Ismail, Wafa Ragmoun, Hatem Lahdhili, Saber Hachicha, Slim Chenik
    Clinical Case Reports.2020; 8(12): 2494.     CrossRef
J Trauma Inj : Journal of Trauma and Injury
© The Korean Society of Traumatology.
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