https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/413 Fri, 24 Jul 2026 07:43:10 GMT 2026-07-24T07:43:10Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/212888 Title: Early prediction of renal replacement therapy within 24 hours after septic shock recognition in the emergency department using machine learning: a retrospective analysis of a prospectively collected multicenter registry Authors: Nah, Sangun; Lim, Tae Ho; Chung, Sung Phil; Suh, Gil Joon; Choi, Sung-Hyuk; Kwon, Woon Yong; Kim, Won Young; Kim, Kyuseok; Choi, Sangchun; You, Je Sung; Choi, Han Sung; Shin, Tae Gun; Han, Sangsoo Abstract: Background: Early identification of patients with septic shock who may soon require renal replacement therapy (RRT) is clinically important but challenging in the emergency department (ED), where definitive indications for RRT often have not yet developed at the time of presentation. Recognizing these patients in advance is important for timely planning of RRT initiation, including coordination of equipment and personnel at the hospital level. This study aimed to develop and validate machine learning (ML) models that predict the need for RRT within 24 h of septic shock recognition in the ED. Methods: We analyzed data from the Korean Shock Society septic shock registry collected from October 2015 to December 2023. Feature selection was performed using least absolute shrinkage and selection operator regression, and five ML models were trained. The best-performing model was selected based on the area under the receiver operating characteristic curve (AUROC). Shapley additive explanations were used to interpret the contribution of each feature. Results: In total, 5361 patients were included in the analysis, of whom 728 (13.6%) required RRT within 24 h. Among the evaluated models, categorical boosting (CatBoost) demonstrated the best discrimination with an AUROC of 0.86 (95% CI, 0.833–0.887), outperforming conventional severity scores such as the Sequential Organ Failure Assessment (AUROC, 0.673 [95% CI, 0.628–0.717]) and the Acute Physiology and Chronic Health Evaluation (AUROC, 0.672 [95% CI, 0.623–0.719]). Conclusions: The CatBoost model demonstrated moderate discriminative performance for predicting early RRT requirement within 24 h of ED septic shock recognition. Tue, 01 Dec 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/212888 2026-12-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/210818 Title: Emerging electronic deodorization technologies for human odor management Authors: Lee, Solpa; Diwe, Pratiksha; Lim, Tae Ho; Jang, Yongwoo Abstract: [No abstract available] Tue, 01 Sep 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/210818 2026-09-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/218680 Title: Deep Learning-Based Anatomical Segmentation of the Foot and Ankle: A Multi-View Radiograph Approach Authors: Lim, Hyojin; Oh, Jaehoon; Kim, Tae Hyun; Lee, Juncheol; Chung, Jae Ho; Lee, Dong Keon Abstract: Purpose: We aimed to develop deep learning models that can identify and separate the shapes of 14 bones in the foot and ankle using multi-view radiographs and predict their masks. Materials and Methods: We retrospectively collected 273 radiographs from 99 patients with anatomically normal feet, including anteroposterior (AP), oblique (OBL), and lateral (LAT) views, obtained between January 2020 and December 2021. In each view, 14 bones were segmented using AP and OBL radiographs and 6 bones using LAT radiographs. Ground truth masks were manually annotated by two radiology technologists and reviewed by an emergency medicine physician. Two deep learning models, a fully convolutional network (FCN) with ResNet-50 and DeepLabv3 with ResNet-50, were independently fine-tuned for semantic segmentation and evaluated using five-fold cross-validation. Results: In the AP and OBL views, both models attained mean intersection over union (mIoU) values ranging from 0.899 to 0.975 and from 0.875 to 0.978, respectively. In the LAT view, mIoU values varied from 0.926 to 0.976 for FCN-ResNet-50 and from 0.872 to 0.961 for DeepLabv3. FCN-ResNet-50 achieved slightly higher mIoU values than DeepLabv3, with statistically significant differences identified between the two models in the overall OBL view and across the 14 specific bones (p<0.05). Conclusion: The FCN-ResNet-50 and DeepLabv3 models could be effective in automatically segmenting foot and ankle bone structures using multi-view radiographs. Wed, 01 Jul 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/218680 2026-07-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/212531 Title: Comparison of prognosis in emergency department elderly septic shock patients with initial hypotension versus delayed hypotension Authors: Lee, Chaeeun; Suh, Gil Joon; Choi, Sung-Hyuk; Chung, Sung Phil; Kim, Won Young; Lim, Tae Ho; Choi, Sangchun; Shin, Tae Gun; Nah, Sangun; Han, Sangsoo Abstract: Background and importance – Hypotension and advanced-age are significant risk factors for increased sepsis-related mortality. However, the relationship between the timing of hypotension in the emergency department (ED) and the prognosis of elderly patients with septic shock is little understood. Objective – To determine the effect of hypotension on arrival at the ED with the prognosis of elderly patients with septic shock. Design, settings, and participants – A retrospective analysis of a multicenter registry that was prospectively collected from 12 EDs. Patients aged older than 65 years who were diagnosed with septic shock requiring vasopressor support from October 2015 to December 2022 were included. Hypotension was defined as a systolic blood pressure less than 90 mmHg or a mean arterial pressure less than 65 mmHg. Based on the timing of the first hypotension episode, patients were divided into two groups: the initial hypotension group (hypotension on arrival at the ED) and the delayed hypotension group (developed hypotension while staying in the ED). Outcome measures and analysis – The primary outcome was 28-day mortality of elderly patients with septic shock, and the secondary outcomes were ICU admission, mechanical ventilation within 24 h, and renal replacement therapy (RRT) within 24 h. A multivariable Cox proportional hazards model was used to analyze the association between initial hypotension and the outcomes. A Kaplan–Meier curve was constructed to investigate the survival probabilities of the patients. Main results – This study included 1444 patients [868 (60.1%) with initial hypotension and 576 (39.9%) with delayed hypotension]. Initial hypotension was significantly associated with 28-day mortality [hazard ratio: 1.20, 95% confidence interval (CI): 1.00–1.45, P = 0.049]. However, initial hypotension was not associated with ICU admission (hazard ratio: 1.13, 95% CI: 0.96–1.33, P = 0.154), mechanical ventilation within 24 h (hazard ratio: 0.85, 95% CI: 0.69–1.06, P = 0.147), or RRT within 24 h (hazard ratio: 1.05, 95% CI: 0.76–1.46, P = 0.775). Conclusion – This study highlights the prognostic value of initial hypotension in elderly patients with septic shock, showing its association with a high risk of 28-day mortality. Mon, 01 Jun 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/212531 2026-06-01T00:00:00Z