• Journal of Korean Neurosurgical Society
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• v.48 no.2
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• pp.99-104
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• 2010

Objective : We conducted this study to evaluate the clinical impact of early enteral nutrition (EN) on in-hospital mortality and outcome in patients with critical hypertensive intracerebral hemorrhage (ICH). Methods : We retrospectively analyzed 123 ICH patients with Glasgow Coma Scale (GCS) score of 3-12. We divided the subjects into two groups : early EN group (< 48 hours, n = 89) and delayed EN group ($\geq$ 48 hours, n = 34). Body weight, total intake and output, serum albumin, Creactive protein, infectious complications, morbidity at discharge and in-hospital mortality were compared with statistical analysis. Results : The incidence of nosocomial pneumonia and length of intensive care unit stay were significantly lower in the early EN group than in the delayed EN group (p < 0.05). In-hospital mortality was less in the early EN group than in the delayed EN group (10.1% vs. 35.3%, respectively; p = 0.001). By multivariate analysis, early EN [odds ratio (OR) 0.229, 95% CI : 0.066-0.793], nosocomial pneumonia (OR = 5.381, 95% CI : 1.621-17.865) and initial GCS score (OR = 1.482 95% CI : 1.160-1.893) were independent predictors of in-hospital mortality in patients with critical hypertensive ICH. Conclusion : These findings indicate that early EN is an important predictor of outcome in patients with critical hypertensive ICH.

• The Korean journal of internal medicine
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• v.39 no.4
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• pp.625-639
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• 2024

Background/Aims: Intensive care unit (ICU) quality is largely determined by the mortality rate. Therefore, we aimed to develop and validate a novel prognostic model for predicting mortality in Korean ICUs, using national insurance claims data. Methods: Data were obtained from the health insurance claims database maintained by the Health Insurance Review and Assessment Service of South Korea. From patients who underwent the third ICU adequacy evaluation, 42,489 cases were enrolled and randomly divided into the derivation and validation cohorts. Using the models derived from the derivation cohort, we analyzed whether they accurately predicted death in the validation cohort. The models were verified using data from one general and two tertiary hospitals. Results: Two severity correction models were created from the derivation cohort data, by applying variables selected through statistical analysis, through clinical consensus, and from performing multiple logistic regression analysis. Model 1 included six categorical variables (age, sex, Charlson comorbidity index, ventilator use, hemodialysis or continuous renal replacement therapy, and vasopressor use). Model 2 additionally included presence/absence of ICU specialists and nursing grades. In external validation, the performance of models 1 and 2 for predicting in-hospital and ICU mortality was not inferior to that of pre-existing scoring systems. Conclusions: The novel and simple models could predict in-hospital and ICU mortality and were not inferior compared to the pre-existing scoring systems.

• The Korean Journal of Health Service Management
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• v.9 no.2
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• pp.81-88
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• 2015

The purpose of this study was to compare the risk-adjusted in-hospital mortality for craniotomies between logistic regression and multilevel analysis. By using patient sample data from the Health Insurance Review & Assessment Service, in-patients with a craniotomy were selected as the survey target. The sample data were collected from a total number of 2,335 patients from 90 hospitals. The sample data were analyzed with SAS 9.3. From the results of the existing logistic regression analysis and multilevel analysis, the values from the multilevel analysis represented a better model than that of logistic regression. The intra-class correlation (ICC) was 18.0%. It was found that risk-adjusted in-hospital mortality for craniotomies may vary in every hospital. The agreement by kappa coefficient between the two methods was good for the risk-adjusted in-hospital mortality for craniotomies, but the factors influencing the outcome for that were different.

• Journal of Trauma and Injury
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• v.35 no.1
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• pp.25-33
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• 2022

Purpose: The scoring system for traumatic liver injury (SSTLI) was developed in 2015 to predict mortality in patients with polytraumatic liver injury. This study aimed to validate the SSTLI as a prognostic factor in patients with polytrauma and liver injury through a generalized estimating equation analysis. Methods: The medical records of 521 patients with traumatic liver injury from January 2015 to December 2019 were reviewed. The primary outcome variable was in-hospital mortality. All the risk factors were analyzed using multivariate logistic regression analysis. The SSTLI has five clinical measures (age, Injury Severity Score, serum total bilirubin level, prothrombin time, and creatinine level) chosen based on their predictive power. Each measure is scored as 0-1 (age and Injury Severity Score) or 0-3 (serum total bilirubin level, prothrombin time, and creatinine level). The SSTLI score corresponds to the total points for each item (0-11 points). Results: The areas under the curve of the SSTLI to predict mortality on post-traumatic days 0, 1, 3, and 5 were 0.736, 0.783, 0.830, and 0.824, respectively. A very good to excellent positive correlation was observed between the probability of mortality and the SSTLI score (γ=0.997, P<0.001). A value of 5 points was used as the threshold to distinguish low-risk (<5) from high-risk (≥5) patients. Multivariate analysis using the generalized estimating equation in the logistic regression model indicated that the SSTLI score was an independent predictor of mortality (odds ratio, 1.027; 95% confidence interval, 1.018-1.036; P<0.001). Conclusions: The SSTLI was verified to predict mortality in patients with polytrauma and liver injury. A score of ≥5 on the SSTLI indicated a high-risk of post-traumatic mortality.

• Journal of Chest Surgery
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• v.53 no.6
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• pp.392-399
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• 2020

Background: Surgical treatment of empyema thoracis in patients with chronic kidney disease is challenging, and few studies in the literature have evaluated this issue. In this study, we aim to report the surgical outcomes of empyema and to analyze factors predicting perioperative mortality in patients with chronic kidney disease. Methods: This retrospective study included data from 34 patients with chronic kidney disease (estimated glomerular filtration rate <60 mL/min/1.73 ㎡ for 3 or more months) who underwent surgery for empyema between 2012 and 2020. An analysis of demographic characteristics and perioperative variables, including complications, was carried out. Postoperative mortality was the primary outcome measure. Results: Patients' age ranged from 20 to 74 years with a 29-to-5 male-female ratio. The majority (n=19, 55.9%) of patients were in end-stage renal disease (ESRD) requiring maintenance hemodialysis. The mean operative time was 304 minutes and the mean intraoperative blood loss was 562 mL. Postoperative morbidity was observed in 70.5% of patients (n=24). In the subgroup analysis, higher values for operative time, blood loss, intensive care unit stay, and complications were found in ESRD patients. The mortality rate was 38.2% (n=13). In the univariate and multivariate analyses, poor performance status (Eastern Cooperative Oncology Group >2) (p=0.03), ESRD (p=0.02), and late referral (>8 weeks) (p<0.001) significantly affected mortality. Conclusion: ESRD, late referral, and poor functional status were poor prognostic factors predicting postoperative mortality. The decision of surgery should be cautiously assessed given the very high risk of perioperative morbidity and mortality in these patients.

• Journal of Trauma and Injury
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• v.36 no.4
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• pp.329-336
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• 2023

Purpose: In this study, we aimed to compare the characteristics of patients with trauma by age group in a single center in Korea to identify the clinical characteristics and analyze the risk factors affecting mortality. Methods: Patients aged ≥18 years who visited the Chungbuk National University Hospital Regional Trauma Center between January 2016 and December 2022 were included. The accident mechanism, severity of the injury, and outcomes were compared by classifying the patients into group A (18-64 years), group B (65-79 years), and group C (≥80 years). In addition, logistic regression analysis was performed to identify factors affecting death. Results: The most common injury mechanism was traffic accidents in group A (40.9%) and slipping in group B (37.0%) and group C (56.2%). Although group A had the highest intensive care unit admission rate (38.0%), group C had the highest mortality rate (9.5%). In the regression analysis, 3 to 8 points on the Glasgow Coma Scale had the highest odds ratio for mortality, and red blood cell transfusion within 24 hours, intensive care unit admission, age, and Injury Severity Score were the predictors of death. Conclusions: For patients with trauma, the mechanism, injured body region, and severity of injury differed among the age groups. The high mortality rate of elderly patients suggests the need for different treatment approaches for trauma patients according to age. Identifying factors affecting clinical patterns and mortality according to age groups can help improve the prognosis of trauma patients in the future.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.998-1008
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• 2022

Objective: The present study aimed to assess the relationship between incidental abnormalities on thoracic computed tomography (CT) and mortality in a general screening population using a long-term follow-up analysis. Materials and Methods: We retrospectively collected the medical records and CT images of 840 participants (mean age ± standard deviation [SD], 58.5 ± 6.7 years; 564 male) who underwent thoracic CT at a single health promotion center between 2007 and 2010. Two thoracic radiologists independently reviewed all CT images and evaluated any incidental abnormalities (interstitial lung abnormality [ILA], emphysema, coronary artery calcification [CAC], aortic valve [AV] calcification, and pulmonary nodules). Kaplan-Meier analysis with log-rank and z-tests was performed to assess the relationship between incidental CT abnormalities and all-cause mortality in the subsequent follow-up. Cox proportional hazards regression was performed to further identify risk factors of all-cause mortality among the incidental CT abnormalities and clinical factors. Results: Among the 840 participants, 55 (6%), 171 (20%), 288 (34%), 396 (47%), and 97 (11%) had findings of ILA, emphysema, CAC, pulmonary nodule, and AV calcification, respectively, on initial CT. The participants were followed up for a mean period ± SD of 10.9 ± 1.4 years. All incidental CT abnormalities were associated with all-cause mortality in univariable analysis (p < 0.05). However, multivariable analysis further revealed fibrotic ILA as an independent risk factor for all-cause mortality (hazard ratio, 2.52 [95% confidence interval, 1.02-6.22], p = 0.046). ILA were also identified as an independent risk factor for lung cancer or respiratory disease-related deaths. Conclusion: Incidental abnormalities on screening thoracic CT were associated with increased mortality during the long-term follow-up. Among incidental CT abnormalities, fibrotic ILA were independently associated with increased mortality. Appropriate management and surveillance may be required for patients with fibrotic ILA on thoracic CT obtained for general screening purposes.

• Journal of Trauma and Injury
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• v.37 no.3
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• pp.201-208
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• 2024

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.

• Journal of Preventive Medicine and Public Health
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• v.40 no.1
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• pp.29-35
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• 2007

Objectives : To assess whether the risk-adjusted in-hospital mortality rates for non-emergent and isolated coronary artery bypass graft surgery (CABG) patients exhibited a consistent trend from 2001 to 2003. Methods : The data used in this study came from CABG claims that were submitted to a Korean Health Insurance Review Agency (HIRA) in 2001, 2002, and 2003. Study datasets included data from 17 tertiary hospitals, which had at least 25 claims each year over 3 years. The inter-hospital differences in patients' risk-factors were identified and controlled in the risk-adjustment model. Actual and predicted mortality rates for each hospital were calculated in 2001, 2002, 2003, and 2001+2002, and were then examined to identify consistent rate patterns over time. Kappa analysis was applied to assess the agreements between rates. Results : Hospitals with lower-than-expected inpatient mortality rates showed more consistent rates than those with higher-than-expected mortality rates. The mortality rates that were calculated based on data obtained over multiple years had less variation among hospitals than rates based on single year data. Based on the Kappa score, the highest agreement was found when the rates were compared between the 2-year combined data (2001+2002) and 2003. Conclusions : Consistent patterns over 3 years were most evident for hospitals which had lower-than expected mortality rates. Policy makers can use this information to identify the degree of outcomes in hospitals and help motivate or channel the behaviors of providers.

• Acute and Critical Care
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• v.33 no.4
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• pp.230-237
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• 2018

Background: Socioeconomic status (SES) is closely associated with health outcomes, including mortality in critically ill patients admitted to intensive care unit (ICU). However, research regarding this issue is lacking, especially in countries where the National Health Insurance System is mainly responsible for health care. This study aimed to investigate how the SES of ICU patients in South Korea is associated with mortality. Methods: This was a retrospective observational study of adult patients aged ${\geq}20$ years admitted to ICU. Associations between SES-related factors recorded at the time of ICU admission and 30-day and 1-year mortalities were analyzed using univariable and multivariable Cox regression analyses. Results: A total of 6,008 patients were included. Of these, 394 (6.6%) died within 30 days of ICU admission, and 1,125 (18.7%) died within 1 year. Multivariable Cox regression analysis found no significant associations between 30-day mortality after ICU admission and SES factors (P>0.05). However, occupation was significantly associated with 1-year mortality after ICU admission. Conclusions: Our study shows that 30-day mortality after ICU admission is not associated with SES in the National Health Insurance coverage setting. However, occupation was associated with 1-year mortality after ICU admission.