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https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/148
2024-03-28T14:00:48ZAssociation of prehospital advanced airway and epinephrine with survival in patients with out-of-hospital cardiac arrest
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/64406
Title: Association of prehospital advanced airway and epinephrine with survival in patients with out-of-hospital cardiac arrest
Authors: Ahn, Sejoong; Jin, Bo-Yeong; Cho, Hanjin; Moon, Sungwoo; Cho, Young-Duck; Park, Jong-Hak
Abstract: Survival benefits of prehospital advanced airway and epinephrine in out-of-hospital cardiac arrest (OHCA) patients are controversial, but few studies evaluated this together. This study evaluated association of prehospital advanced airway and epinephrine with survival outcomes in OHCA patients. This was observational study using a prospective multicentre KoCARC registry. Adult OHCA patients between October 2015 and December 2021 were included. The variables of interest were prehospital managements, which was classified into basic life support (BLS)-only, BLS + advanced airway, and BLS + advanced airway + epinephrine. In total, 8217 patients were included in analysis. Survival to discharge and good neurological outcomes were lowest in the BLS + advanced airway + epinephrine group (22.1% in BLS-only vs 13.2% in BLS + advanced airway vs 7.5% in BLS + advanced airway + epinephrine, P < 0.001 and 17.1% in BLS-only vs 9.2% in BLS + advanced airway vs 4.3% in BLS + advanced airway + epinephrine, P < 0.001, respectively). BLS + advanced airway + epinephrine group was less likely to survive to discharge and have good neurological outcomes (aOR 0.39, 95% CI 0.28-0.55, P < 0.001 and aOR 0.33, 95% CI 0.21-0.51, P < 0.001, respectively) than BLS-only group after adjusting for potential confounders. In prehospital settings with intermediate EMS providers and prehospital advanced airway insertion is performed followed by epinephrine administration, prehospital management with BLS + advanced airway + epinephrine in OHCA patients was associated with lower survival to discharge rate compared to BLS-only.2023-10-01T00:00:00ZPrognostic accuracy of initial and 24-h maximum SOFA scores of septic shock patients in the emergency department
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/64262
Title: Prognostic accuracy of initial and 24-h maximum SOFA scores of septic shock patients in the emergency department
Authors: Kim, Tae Han; Jeong, Daun; Park, Jong Eun; Hwang, Sung Yeon; Suh, Gil Joon; Choi, Sung-Hyuk; Chung, Sung Phil; Kim, Won Young; Lee, Gun Tak; Shin, T. G.
Abstract: Background: We compared the prognostic accuracy of in-hospital mortality of the initial Sequential Organ Failure Assessment (SOFAini) score at the time of sepsis recognition and resuscitation and the maximum SOFA score (SOFAmax) using the worst variables in the 24 h after the initial score measurement in emergency department (ED) patients with septic shock.Methods: This was a retrospective observational study using a multicenter prospective registry of septic shock patients in the ED between October 2015 and December 2019. The primary outcome was in-hospital mortality. The prognostic accuracies of SOFAini and SOFAmax were evaluated using the area under the receiver operating characteristic (AUC) curve.Results: A total of 4860 patients was included, and the in-hospital mortality was 22.1%. In 59.7% of patients, SOFAmax increased compared with SOFAini, and the mean change of total SOFA score was 2.0 (standard deviation, 2.3). There was a significant difference in in-hospital mortality according to total SOFA score and the SOFA component scores, except cardiovascular SOFA score. The AUC of SOFAmax (0.71; 95% confidence interval [CI], 0.69-0.72) was significantly higher than that of SOFAini (AUC, 0.67; 95% CI, 0.66-0.69) in predicting in-hospital mortality. The AUCs of all scores of the six components were higher for the maximum values.Conclusion: The prognostic accuracy of the initial SOFA score at the time of sepsis recognition was lower than the 24-h maximal SOFA score in ED patients with septic shock. Follow-up assessments of organ failure may improve discrimination of the SOFA score for predicting mortality.2023-09-01T00:00:00ZImproving triage accuracy of hospitalization and discharge decisions in the emergency department
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/64318
Title: Improving triage accuracy of hospitalization and discharge decisions in the emergency department
Authors: Park, Sung-Joon; Choi, Sung-Hyuk; Song, Dae-Jin; Park, Jong-Hak; Song, Ju-Hyun; Cho, Han-Jin; Lee, Sun-Hong; Ko, Byung-Chul; Ahn, Kyu-Hwan; Kim, Gil-Gon; Choi, Won-Seok; Kim, Kyung-Nam
Abstract: The initial severity triage of patients in the emergency department (ED) is implemented differently worldwide, according to the characteristics of each country. However, better classification methods are being studied due to various problems with the current system. Therefore, the aim of this study was to determine the usefulness of patients' severity assessment in a new way that gives appropriate values to factors that can be obtained in the ED. We collected data from 158,246 patients who visited the ED from 01 January 2016 to 31 December 2020. Using the appropriate values of various factors obtained using the Rasch analysis method, the cut-off values for predicting hospitalization and discharge at the ED of patients were determined. Furthermore, using artificial intelligence, the patients who were hospitalized and discharged from the ED were classified and compared with the results of the Rasch analysis. The accuracy of the algorithms was analyzed as a combination of factors that could be obtained during the initial stage of the patient's visits. The area under the curve (AUC) value for the prediction of hospitalization and discharge by a combination of factors immediately obtained from the visit was 0.611. In addition, using the factors that could be obtained after a certain period, the AUC value of hospitalization and discharge prediction was 0.767. The results of analysis using artificial intelligence were similar to or slightly higher than the AUC value of the Rasch analysis. The prediction of hospitalization and discharge in the ED using clinical parameters with Rasch analysis can be used for medical assistance, which is expected to help in the efficient operation of the ED.2023-09-01T00:00:00ZMortality prediction of patients with sepsis in the emergency department using machine learning models: a retrospective cohort study according to the Sepsis-3 definitions
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/64330
Title: Mortality prediction of patients with sepsis in the emergency department using machine learning models: a retrospective cohort study according to the Sepsis-3 definitions
Authors: Jeon, Eun-Tae; Song, Juhyun; Park, Dae Won; Lee, Ki-Sun; Ahn, Sejoong; Kim, Joo Yeong; Park, Jong-Hak; Moon, Sungwoo; Cho, Han-Jin
Abstract: Although clinical scoring systems and biomarkers have been used to predict outcomes in sepsis, their prognostic value is limited. Therefore, machine learning (ML) models have been proposed to predict the outcomes of sepsis. This study aims to propose ML algorithms that create robust models for predicting mortality in patients with sepsis diagnosed using the Sepsis-3 definitions in the emergency department. This study was performed using a prospectively collected registry of adult patients with sepsis between January 2016 and February 2020. Among the 810 patients, 607 (75%) and 203 (25%) patients were assigned to the training and test sets, respectively. The primary outcome was 30-day mortality. Using the values of the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), balanced accuracy, and Brier score, we compared the performances of different ML algorithms with that of the logistic regression models and clinical scoring systems. The ML models' performance was superior to that of the clinical scoring systems. A light gradient boosting machine achieved the highest AUROC among the ML models in predicting 30-day mortality. Most of the ML models had significantly higher AUROC and balanced accuracy than the logistic regression models. All the ML models exhibited higher AUPRC and lower Brier scores compared to the scoring systems and logistic regression model. The ML models can be used as supportive tools for predicting mortality in sepsis patients. In future studies, the performance of the proposed models will be validated using more data from different hospitals or departments.2023-09-01T00:00:00Z