• Journal of Korean Academy of Nursing
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• v.30 no.5
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• pp.1243-1253
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• 2000

The purpose of this study was to verify the validity of the Patient Severity Classification Tool by examining the correlations between the APACHE Ⅲ and the Patient Severity Classification Tool and to propose admission criteria to the ICU. The instruments used for this study were the APACHE Ⅲ developed by Knaus and the Patient Severity Classification Tool developed by Korean Clinical Nurses Association. Data was collected from the 156 Medical ICU patients during their first 24 hours of admission at the Seoul National University Hospital by three trained Medical ICU nurses from April 20 to August 31 1999. Data were analyzed using the frequency, $x^2$, Wilcoxon rank sum test, and Spearman rho. There was statistically significant correlations between the scores of the APACHE III and the Patient Severity Classification Tool. Mortality rate was increased as patients classification of severity in both the APACHE III and the Patient Severity Classification Tool scored higher. The Patient Severity Classification Tool was proved to be a valid and reliable tool, and a useful tool as one of the severity predicting factors, ICU admission criteria, information sharing between ICUs, quality evaluations of ICUs, and ICU nurse staffing.

• Journal of Korean Neurosurgical Society
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• v.52 no.3
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• pp.200-203
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• 2012

Objective : The sub-axial injury classification (SLIC) and severity scale was developed to decide whether to operate the cervical injured patient or not, but the reliability of SLIC and severity scale among the different physicians was not well known. Therefore, we evaluated the reliability of SLIC among a spine surgeon, a resident of neurosurgery and a neuro-radiologist. Methods : In retrograde review in single hospital from 2002 to 2009 years, 75 cases of sub-axial spine injured patients underwent operation. Each case was blindly reviewed for the SLIC and severity scale by 3 different observers by two times with 4 weeks interval with randomly allocated. The compared axis was the injury morphology score, the disco-ligamentous complex score, the neurological status score and total SLIC score; the neurological status score was derived from the review of medical record. The kappa value was used for the statistical analysis. Results : Interobserver agreement of SLIC and severity scale was substantial agreement in the score of injury morphology [intraclass correlation (ICC)=0.603] and total SLIC and severity sacle (ICC value=0.775), but was fair agreement in the disco-ligamentous complex score (ICC value= 0.304). Intraobserver agreements were almost perfect agreement in whole scales with ICC of 0.974 in a spine surgeon, 0.948 in a resident of neurosurgery, and 0.963 in a neuro-radiologist. Conclusion : The SLIC and severity scale is comprehensive and easily applicable tool in spine injured patient. Moreover, it is very useful tool to communicate among spine surgeons, residents of neurosurgery and neuro-radiologists with sufficient reproducibility.

• Proceedings of the KAIS Fall Conference
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• 2012.05a
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• pp.22-24
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• 2012

The tools that classify the severity of patients based on the prediction of mortality include APACHE, SAPS, and MPM. Theses tools rely crucially on the evaluation of patients' general clinical status on the first date of their admission to ICU. Nursing activities are one of the most crucial factors influencing on the quality of treatment that patients receive and one of the contributing factors for their prognosis and safety. The purpose of this study was to identify the goodness-of-fit of CPSCS of critical patient severity classification system(CPSCS) and Glasgow coma scale(GCS) and the clinical usefulness of its death rate prediction. Data were collected from the medical records of 187 neurological patients who were admitted to the ICU of C University Hospital. The data were analyzed through $x^2$ test, t-test, Mann-Whitney, Kruskal-Wallis, goodness-of-fit test, and ROC curve. In accordance with patients' general and clinical characteristics, patient mortality turned out to be statistically different depending on ICU stay, endotracheal intubation, central venous catheter, and severity by CPSCS. Homer-Lemeshow goodness-of-fit tests were CPSCS and GCS and the results of the discrimination test using the ROC curve were $CPSCS_0$, .734, $GCS_0$,.583, $CPSCS_{24}$,.734, $GCS_{24}$, .612, $CPSCS_{48}$,.591, $GCS_{48}$,.646, $CPSCS_{72}$,.622, and $GCS_{72}$,.623. Logistic regression analysis showed that each point on the CPSCS score signifies1.034 higher likelihood of dying. Applied to neurologically ill patients, early CPSCS scores can be regarded as a useful tool.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2012.04a
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• pp.190-193
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• 2012

The tools that classify the severity of patients based on the prediction of mortality include APACHE, SAPS, and MPM. Theses tools rely crucially on the evaluation of patients' general clinical status on the first date of their admission to ICU. Nursing activities are one of the most crucial factors influencing on the quality of treatment that patients receive and one of the contributing factors for their prognosis and safety. The purpose of this study was to identify the goodness-of-fit of CPSCS of critical patient severity classification system(CPSCS) and Glasgow coma scale(GCS) and the clinical usefulness of its death rate prediction. Data were collected from the medical records of 187 neurological patients who were admitted to the ICU of C University Hospital. The data were analyzed through $x^2$ test, t-test, Mann-Whitney, Kruskal-Wallis, goodness-of-fit test, and ROC curve. In accordance with patients' general and clinical characteristics, patient mortality turned out to be statistically different depending on ICU stay, endotracheal intubation, central venous catheter, and severity by CPSCS. Homer-Lemeshow goodness-of-fit tests were CPSCS and GCS and the results of the discrimination test using the ROC curve were $CPSCS_0$,.734, $GCS_0$,.583, $CPSCS_{24}$,.734, $GCS_{24}$,.612, $CPSCS_{48}$,.591, $GCS_{48}$,.646, $CPSCS_{72}$,.622, and $GCS_{72}$,.623. Logistic regression analysis showed that each point on the CPSCS score signifies1.034 higher likelihood of dying. Applied to neurologically ill patients, early CPSCS scores can be regarded as a useful tool.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.126-136
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• 2018

The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.

• Journal of Trauma and Injury
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• v.36 no.3
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• pp.165-171
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• 2023

Trauma remains a significant healthcare burden, causing over five million yearly fatalities. Notably, the liver is a frequently injured solid organ in abdominal trauma, especially in patients under 40 years. It becomes even more critical given that uncontrolled hemorrhage linked to liver trauma can have mortality rates ranging from 10% to 50%. Liver injuries, mainly resulting from blunt trauma such as motor vehicle accidents, are traditionally classified using the American Association for the Surgery of Trauma grading scale. However, recent developments have introduced the World Society of Emergency Surgery classification, which considers the patient's physiological status. The diagnostic approach often involves multiphase computed tomography (CT). Still, newer methods like split-bolus single-pass CT and contrast-enhanced ultrasound (CEUS) aim to reduce radiation exposure. Concerning management, nonoperative strategies have emerged as the gold standard, especially for hemodynamically stable patients. Incorporating angiography with embolization has also been beneficial, with success rates reported between 80% and 97%. However, it is essential to identify the specific source of bleeding for effective embolization. Given the severity of liver trauma and its potential complications, innovations in diagnostic and therapeutic approaches have been pivotal. While CT remains a primary diagnostic tool, methods like CEUS offer safer alternatives. Moreover, nonoperative management, especially when combined with angiography and embolization, has demonstrated notable success. Still, the healthcare community must remain vigilant to complications and continuously seek improvements in trauma care.

• Journal of Trauma and Injury
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• v.32 no.1
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• pp.40-46
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• 2019

Purpose: We perform an analysis of infection risk factors for fracture patients and confirm that the risk factors reported in previous studies increase the risk of actual infection among fractured patients. In addition, injury severity score (ISS) which is used as an evaluation tool for morbidity of trauma patients, confirms whether there is a relationship with infection after orthopedic fracture surgery. Methods: We retrospectively reviewed 1,818 patients who underwent fixation surgery at orthopedic trauma team, focused trauma center from January 1, 2015 to December 31, 2017. Thirty-five patients were infected after fracture surgery. We analyzed age, sex, open fracture criteria based on Gustilo-Aderson classification 3b, anatomical location (upper extremity or lower extremity) of fracture, diabetes, smoking, ISS. Results: Of 1,818 patients, 35 (1.9%) were diagnosed with postoperative infection. Of the 35 infected patients, nine (25.7%) were female and five (14.0%) were upper extremity fractures. Three (8.6%) were diagnosed with diabetes and eight (22.8%) were smokers. Thirteen (37.1%) had ISS less than nine points and six (17.1%) had ISS 15 points or more. Of 1,818 patients, 80 had open fractures. Surgical site infection were diagnosed in 12 (15.0%) of 80. And nine of 12 were checked with Gustilo-Aderson classification 3b or more. Linear logistic regression analysis was performed using statistical analysis program Stata 15 (Stata Corporation, College Station, TX, USA). In addition, independent variables were logistic regression analyzed individually after Propensity scores matching. In all statistical analyzes, only open fracture was identified as a risk factor. Conclusions: The risk factors for infection in fracture patients were found to be significantly influenced by open fracture rather than the underlying disease or anatomical feature of the patient. In the case of ISS, it is considered that there is a limitation. It is necessary to develop a new scoring system that can appropriately approach the morbidity of fracture trauma patients.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.