• Journal of Veterinary Clinics
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• v.34 no.5
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• pp.313-317
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• 2017

Symmetric dimethylarginine (SDMA) is a new renal biomarker for kidney function. It is almost exclusively eliminated by renal filtration. The purpose of this retrospective study was to evaluate the changes in serum ceatinine (CREA), blood urea nitrogen (BUN) and SDMA concentrations in dogs with chronic mitral valve insufficiency (CMVI), according to the severity of CMVI. The evaluation of the severity of CMVI was performed according to the American College of Veterinary Internal Medicine (ACVIM) classification of heart failure. The dogs were classified into two groups: group 1 (ACVIM B; n = 11) and group 2 (ACVIM C; n = 15). In dogs with advanced CMVI, the serum SDMA concentrations were significantly increased above the normal reference range and were independent of body weight (BW), systolic blood pressure (SBP), or sex. No dog in either group had higher serum CREA concentrations than the upper limit. The serum SDMA concentration may be a better renal marker than serum CREA concentrations for the early diagnoses of renal dysfunction in dogs with CMVI.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.101-112
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• 2014

In this study, a risk assessment method based on scenario for the product safety management in Korea has been developed and proposed. To this end, Korea's related regulations for product safety management should be analyzed first, and the risk assessment method necessary for the enforcement of the regulations is presented by itemizing the method into the case of general injury and toxic substances. The features of the method presented in this study are as follows: (i) It is a method based on the injury scenario which can occur during the use of product. (ii) It assesses a risk based on the probability of the scenario and the severity of injury. (iii) In the case of toxic substances, it assesses a risk considering the hazard of the toxic substances on the human body and the severity of injury. To determine the probability of the injury scenario, this study has decomposed the scenario into several configuration factors and estimates each factor's probability to calculate the whole scenario's probability. The results of risk assessment through the method of this study are presented and it is shown that the method can be applied to the product classification for the product safety management.

• Korean Journal of Clinical Pharmacy
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• v.30 no.1
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• pp.44-50
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• 2020

Background: Drug-drug interactions (DDIs) in patients using oral anticancer treatment are more common than in those using injectable anticancer agents. In addition, DDIs related to anticancer treatment are known to cause clinically significant outcomes, such as treatment failure and severe toxicity. To prevent these negative outcomes, significant DDIs are monitored and managed using the information provided in drug databases. We aimed to evaluate the consistency of information on clinically significant DDIs for tyrosine kinase inhibitors (TKIs) between representative drug databases. Methods: We selected clinically significant DDIs involving medications that are co-prescribed with TKIs and met the following criteria: the severity level of DDIs was equal or greater than "D" in Lexicomp^® or "major" in Micromedex^®. We then analyzed the consistency of the severity classification and evidence level between the drug databases. Spearman's correlation coefficient was used to identify the relationship between DDI information in the drug databases. Results: In total, 627 DDI pairs were identified as clinically significant; information on these was provided by Lexicomp^® and Micromedex^® for 571 and 438 pairs, respectively, and both drug databases provided information on 382 DDI pairs. There was no correlation between the severity and evidence level of DDIs provided in the two databases; Spearman's correlation coefficient for Lexicomp^® and Micromedex^® was -0.009 (p=0.861) and -0.064 (p=0.209), respectively. Conclusion: To judge the significance of DDIs, healthcare providers should consider that the information on DDIs may be different between drug information databases; hence, clinical factors must be considered concurrently.

• The Korean Journal of Pain
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• v.5 no.2
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• pp.221-228
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• 1992

The effect of epidural nalbuphine on pruritus, nausea, vomiting, voiding difficulties and/or analgesia induced by epidural morphine was determined in sixty Cesarian delivery patients. They were physical status 1 or 2 by ASA classification and randomly divided into three groups. They were administered morphine 3 mg only(group A), nalbuphine 5 mg with morphine 3 mg(group B), or nalbuphine 10 mg with morphine 3 mg(group C) at the time of peritoneal closure. During postoperative 24 hours their analgesic effects were evaluated by visual analogue scale(0~10). Respiratory rates, Trieger dot test and severity of side effects(0~2) were also evaluated. The results were as follows; 1) Analgesic duration of the first epidural administration was significantly long in group A than other groups, but there was no difference between that of group B and group C. 2) Pruritus was more severe in group A than other groups but the severity was decreased by increasing nalbuphine dosage. 3) Nausea and or vomiting was mild in group C and the incidence of nausea and/or vomiting combined with pruritus was decreased by increasing nalbuphine dosage. 4) Voiding difficulties was more severe in group A than other groups but the severity was not decreased by increasing nalbuphine dosage. 5) None of the patients had objective sedation or low respiration rate(< 10 times/minute). We concluded that epidural administration of nalbuphine 10 mg with morphine 3mg for post-Cesarean section pain management is one of good methods to reduce side effects induced by epidural morphine.

• Journal of The Korean Society of Clinical Toxicology
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• v.13 no.2
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• pp.87-94
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• 2015

Purpose: We investigated comparison of clinical characteristics and prognosis by initial endoscopic severity in caustic injury and then discussed predisposing factors which can be helpful in predicting the prognosis and determining the treatment. Methods: This study was a retrospective review of medical records from patients over the age of 15, who underwent initial endoscopy for caustic injury from April 2007 through November 2014. Patients were classified according to two groups based on the initial endoscopic finding by Zargar's classification: patients with grade 0, I, IIa at esophagus (low risk group) and patients with grade IIb, IIIa, IIIb at esophagus (high risk group). The two groups were then compared. Results: A total of 55 patients were included (low risk group [n=44] vs. high risk group [n=11]). Old age (p<0.001), large amount of ingestion (p<0.05), oropharyngeal symptoms (p<0.01), high SOFA score (p<0.001), high WBC count (p<0.05), low base excess (p<0.01), and HCO3 (p<0.05) were statistically significant factors in the high risk group. A poor prognosis was observed for hospital stay (p<0.001), ICU admission (p<0.001), mortality (p<0.01), and stricture (p<0.001) in the high risk group. Conclusion: Clinical characteristics including age, amount of ingestion, oropharyngeal symptoms, SOFA score, WBC count, base excess, and $HCO_3$ can be helpful in the decision to undergo initial endoscopy and risk assessment by initial endoscopic severity can be helpful in predicting prognosis and determining the treatment plan.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.4
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• pp.13-29
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• 2020

Automobiles have long been an essential part of daily life, but the social costs of car traffic accidents exceed 9% of the national budget of Korea. Hence, it is necessary to establish prevention and response system for car traffic accidents. In order to present a model that can classify and predict the degree of injury in car traffic accidents, we used big data analysis techniques of K-nearest neighbor, logistic regression analysis, naive bayes classifier, decision tree, and ensemble algorithm. The performances of the models were analyzed by using the data on the nationwide traffic accidents over the past three years. In particular, considering the difference in the number of data among the respective injury severity levels, we used down-sampling methods for the group with a large number of samples to enhance the accuracy of the classification of the models and then verified the statistical significance of the models using ANOVA.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.482-490
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• 2017

This study is a railway accident investigation statistic study with the purpose of prediction and classification of accident severity. Linear regression models have some difficulties in classifying accident severity, but a logistic regression model can be used to overcome the weaknesses of linear regression models. The logistic regression model is applied to escalator (E/S) accidents in all stations on 5~8 lines of the Seoul Metro, using data mining techniques such as logistic regression analysis. The forecasting variables of E/S accidents in urban railway stations are considered, such as passenger age, drinking, overall situation, behavior, and handrail grip. In the overall accuracy analysis, the logistic regression accuracy is explained 76.7%. According to the results of this analysis, it has been confirmed that the accuracy and the level of significance of the logistic regression analysis make it a useful data mining technique to establish an accident severity prediction model for urban railway casualty accidents.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.5
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• pp.455-464
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• 2018

Objective: Machine learning is not yet widely used in the medical field. Therefore, this study was conducted to compare the performance of preexisting severity prediction models and machine learning based models (random forest [RF], gradient boosting [GB]) for mortality prediction in pneumonia patients. Methods: We retrospectively collected data from patients who visited the emergency department of a tertiary training hospital in Seoul, Korea from January to March of 2015. The Pneumonia Severity Index (PSI) and Sequential Organ Failure Assessment (SOFA) scores were calculated for both groups and the area under the curve (AUC) for mortality prediction was computed. For the RF and GB models, data were divided into a test set and a validation set by the random split method. The training set was learned in RF and GB models and the AUC was obtained from the validation set. The mean AUC was compared with the other two AUCs. Results: Of the 536 investigated patients, 395 were enrolled and 41 of them died. The AUC values of PSI and SOFA scores were 0.799 (0.737-0.862) and 0.865 (0.811-0.918), respectively. The mean AUC values obtained by the RF and GB models were 0.928 (0.899-0.957) and 0.919 (0.886-0.952), respectively. There were significant differences between preexisting severity prediction models and machine learning based models (P<0.001). Conclusion: Classification through machine learning may help predict the mortality of pneumonia patients visiting the emergency department.

• Journal of Trauma and Injury
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• v.34 no.2
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• pp.87-97
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• 2021

Purpose: This study examined emergency medical service (EMS) transportation patterns for adult trauma patients before and after establishing a level 1 regional trauma center (RTC) and to evaluate the transportation approach after prehospital severity screening. Methods: This was a retrospective observational study of trauma patients aged ≥18 years admitted via EMS to the emergency department or a level 1 RTC, 1 year before to 3 years after RTC establishment. Patients with an Injury Severity Score (ISS) in the patient registration system were selected. Analyses were performed to determine transportation pattern changes by comparing patients pre- and post-RTC establishment and by yearly comparisons over the 4-year study period using the Mann-Whitney U test and chi-square test. Results: Overall, 3,587 patients were included. The mean ISS was higher in the post-RTC group (n=2,693; 10.63±8.90, median 9.00) than in the pre-RTC group (n=894; 9.44±8.20, median 8.00; p<0.001). The mean transportation distance (9.84±13.71, median 5.80 vs. 13.12±16.15 km, median 6.00; p<0.001) was longer in the post-RTC group than in the pre-RTC group. Furthermore, proportionally fewer patients were transported from an area in the same city as the RTC after establishment (86.1% vs. 78.3%; p<0.001). Yearly comparisons revealed a gradually increasing trend in the hospital death rate (ptrend=0.031). Conclusions: After establishing a level 1 RTC, the EMS transportation of severe trauma patients increased gradually along with the long-distance transportation of minor trauma patients. Therefore, improved prehospital EMS trauma severity assessments and level 1 RTC involvement in patient classification in the prehospital phase are necessary.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.39-46
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• 2022

As the link between traffic accidents and social and economic losses has been confirmed, there is a growing interest in developing safety policies based on crash data and a need for countermeasures to reduce severe crash outcomes such as severe injuries and fatalities. In this study, we select Daejeon city where the relative proportion of fatal crashes is high, as a case study region and focus on the severity of pedestrian crashes. After a series of data manipulation process, we run machine learning algorithms for the optimal model selection and variable identification. Of nine algorithms applied, AdaBoost and Random Forest (ensemble based ones) outperform others in terms of performance metrics. Based on the results, we identify major influential factors (i.e., the age of pedestrian as 70s or 20s, pedestrian crossing) on pedestrian crashes in Daejeon, and suggest them as measures for reducing severe outcomes.