• 대한예방의학회:학술대회논문집
• /
• 1998.10b
• /
• pp.311-312
• /
• 1998

• Korea Journal of Hospital Management
• /
• v.14 no.1
• /
• pp.23-35
• /
• 2009

The objectives of the present study is to examine the validity of Charlson Comorbidity Index(CCI) based on medical record data; to utilize the index to determine outcome indexes such as mortality, length of stay and cost for the domestic patients whose have received total hip arthroplasty. Based on medical record date, 1-year Mortality was analyzed to be 0.664 of C statistic. The $R^2$ for the predictability for length of stay and the cost was about 0.0181, 0.1842. Fee of national health insurance and total cost including the cost not covered by insurance, also had statistically significance above 3 points of Charlson point score(p=0.0290, 0.0472; $p.{\le}0.05$). The 1-year mortality index, length of stay and cost of the total hip arthroplasty patients which was obtained utilizing CCI have a limitative prediction power and therefore should be carefully analyzed for use.

• Journal of Korean Academy of Nursing
• /
• v.41 no.1
• /
• pp.92-100
• /
• 2011

Purpose: Mortality Provability Model (MPM) II is a model for predicting mortality probability of patients admitted to ICU. This study was done to test the validity of MPM II for critically ill neurological patients and to determine applicability of MPM II in predicting mortality of neurological ICU patients. Methods: Data were collected from medical records of 187 neurological patients over 18 yr of age who were admitted to the ICU of C University Hospital during the period from January 2008 to May 2009. Collected data were analyzed through $X^2$ test, t-test, Mann-Whiteny test, goodness of fit test, and ROC curve. Results: As to mortality according to patients' general and clinically related characteristics, mortality was statistically significantly different for ICU stay, hospital stay, APACHE III score, APACHE predicted death rate, GCS, endotracheal intubation, and central venous catheter. Results of Hosmer-Lemeshow goodness-of-fit test were MPM $II_0$ ($X^2$=0.02, p=.989), MPM $II_24$ ($X^2$=0.99 p=.805), MPM $II_48$ ($X^2$=0.91, p=.822), and MPM $II_72$ ($X^2$=1.57, p=.457), and results of the discrimination test using the ROC curve were MPM $II_0$, .726 (p<.001), MPM $II_24$, .764 (p<.001), MPM $II_48$, .762 (p<.001), and MPM $II_72$, .809 (p<.001). Conclusion: MPM II was found to be a valid mortality prediction model for neurological ICU patients.

• Korean Circulation Journal
• /
• v.48 no.12
• /
• pp.1148-1156
• /
• 2018

Background and Objectives: It is controversial that decreased left ventricular function could predict poststroke outcomes. The purpose of this study is to elucidate whether left ventricular ejection fraction (LVEF) can predict cardiovascular events and mortality in acute ischemic stroke (AIS) without atrial fibrillation (AF) and coronary heart disease (CHD). Methods: Transthoracic echocardiography was conducted consecutively in patients with AIS or transient ischemic attack at Soonchunhyang University Hospital between January 2008 and July 2016. The clinical data and echocardiographic LVEF of 1,465 patients were reviewed after excluding AF and CHD. Poststroke disability, major adverse cardiac events (MACE; nonfatal stroke, nonfatal myocardial infarction, and cardiovascular death) and all-cause mortality during 1 year after index stroke were prospectively captured. Cox proportional hazards regressions analysis were applied adjusting traditional risk factors and potential determinants. Results: The mean follow-up time was $259.9{\pm}148.8days$ with a total of 29 non-fatal strokes, 3 myocardial infarctions, 33 cardiovascular deaths, and 53 all-cause mortality. The cumulative incidence of MACE and all-cause mortality were significantly higher in the lowest LVEF (<55) group compared with the others (p=0.022 and 0.009). In prediction models, LVEF (per 10%) had hazards ratios of 0.54 (95% confidence interval [CI], 0.36-0.80, p=0.002) for MACE and 0.61 (95% CI, 0.39-0.97, p=0.037) for all-cause mortality. Conclusions: LVEF could be an independent predictor of cardiovascular events and mortality after AIS in the absence of AF and CHD.

• Journal of Korean Society for Atmospheric Environment
• /
• v.22 no.5
• /
• pp.554-563
• /
• 2006

Numerous epidemiological studies have shown stronger associations between $PM_{2.5}$ and both mortality and morbidity than $PM_{10}$. The association of $PM_{2.5}$ with respiratory mortality was examined in Seoul, during the period of $1996{\sim}2002$. Because $PM_{2.5}$ data were available for only 10% of this time period, a prediction regression model was developed to estimate $PM_{2.5}$ concentration. Death count due to respiratory-related diseases(total respiratory mortality; ICD-10, J00-J98) and death counts(cause-specific mortality) due to pneumonia(ICD-10, J12-J18), COPD(ICD-10, J40-J44) and asthma(ICD-10, J45-J46) were considered in this study. Averaged daily mortality was 5.6 for total respiratory mortality and 1.1 to 1.6 for cause-specific mortality. Generalized additive Poisson models controlling for confounders were used to evaluate the acute effects of particle exposures on total respiratory mortality and cause-specific mortality. An IQR increase in 5-day moving average of $PM_{2.5}(22.6{\mu}g/m^3)$ was associated with an 8.2%(95% CI: 4.5 to 12.1%) increase in total respiratory mortality The association of $PM_{2.5}$ was stronger for the elderly ($\geq$65 years old, 10.1%, 95% CI: 5.8 to 14.5%) and for males(8.9%, 95% CI: 2.1 to 11.3%). A $10{\mu}g/m^3$ increase in 5-day moving average of $PM_{2.5}$ was strongly associated with total respiratory mortality in winter(9.5%, 95% CI: 6.6 to 12.4%), followed by spring(3.1%, 95% CI: -1.2 to 7.5%), which was a different pattern with the finding in North American cities. However, our results are generally consistent with those observed in recent epidemiological studies, and suggest that $PM_{2.5}$ has a stronger effect on respiratory mortality in Seoul.

• Quality Improvement in Health Care
• /
• v.10 no.2
• /
• pp.216-231
• /
• 2003

Objectives: To develop a model that predicts a death probability of acute myocardial infarction(AMI) patient, and to evaluate a performance of hospital services using the developed model. Methods: Medical records of 861 AMI patients in 7 general hospitals during 1996 and 1997 were reviewed by two trained nurses. Variables studied were risk factors which were measured in terms of severity measures. A risk model was developed by using the logistic regression, and its performance was evaluated using cross-validation and bootstrap techniques. The statistical prediction capability of the model was assessed by using c-statistic, $R^2$ as well as Hosmer-Lemeshow statistic. The model performance was also evaluated using severity-adjusted mortalities of hospitals. Results: Variables included in the model building are age, sex, ejection fraction, systolic BP, congestive heart failure at admission, cardiac arrest, EKG ischemia, arrhythmia, left anterior descending artery occlusion, verbal response within 48 hours after admission, acute neurological change within 48 hours after admission, and 3 interaction terms. The c statistics and $R^2$ were 0.887 and 0.2676. The Hosmer-Lemeshow statistic was 6.3355 (p-value=0.6067). Among 7 hospitals evaluated by the model, two hospitals showed significantly higher mortality rates, while other two hospitals had significantly lower mortality rates, than the average mortality rate of all hospitals. The remaining hospitals did not show any significant difference. Conclusion: The comparison of the qualities of hospital service using risk-adjusted mortality rates indicated significant difference among them. We therefore conclude that risk-adjusted mortality rate of AMI patients can be used as an indicator for evaluating hospital performance in Korea.

• The Korean journal of helicobacter and upper gastrointestinal research
• /
• v.18 no.4
• /
• pp.225-230
• /
• 2018

Upper gastrointestinal (GI) bleeding (UGIB) is the most common GI emergency, and it is associated with significant morbidity and mortality. Early identification of low-risk patients suitable for outpatient management has the potential to reduce unnecessary costs, and prompt triage of high-risk patients could allow appropriate intervention and minimize morbidity and mortality. Several risk-scoring systems have been developed to predict the outcomes of UGIB. As each scoring system measures different primary outcome variables, appropriate risk scores must be implemented in clinical practice. The Glasgow-Blatchford score (GBS) should be used to predict the need for interventions such as blood transfusion or endoscopic or surgical treatment. Patients with GBS ${\leq}1$ have a low likelihood of adverse outcomes and can be considered for early discharge. The Rockall score was externally validated and is widely used for prediction of mortality. The recently developed AIMS65 score is easy to calculate and was proposed to predict in-hospital mortality. The Forrest classification is based on endoscopic findings and can be used to stratify patients into high- and low-risk categories in terms of rebleeding and thus is useful in predicting the need for endoscopic hemostasis. Early risk stratification is critical in the management of UGIB and may improve patient outcome and reduce unnecessary health care costs through standardization of care.

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

• The Korean Journal of Applied Statistics
• /
• v.30 no.5
• /
• pp.701-717
• /
• 2017

The interest of Korean society and government on future demographic structures is increasing due to rapid aging. Korea's mortality rate is decreasing, but the declined gap is variable. In this study, we compare the Lee-Carter, Lee-Miller, Booth-Maindonald-Smith model and functional data model (FDM) as well as Coherent FDM using non-parametric smoothing technique. We are then examine a reasonable model for projecting on mortality declined rate trend in terms of accuracy of mortality rate by ages and life expectancy. The possibility of using non-parametric techniques for the prediction of mortality in Korea was also examined. Based on the analysis results, FDM and Coherent FDM, which uses the non-parametric technique and reflects the trend of recent data, are excellent. As a result, FDM and Coherent FDM are good fit, and predictability is also excellent assuming no significant future changes.

• Journal of Korean Critical Care Nursing
• /
• v.8 no.1
• /
• pp.71-79
• /
• 2015

Purpose: The purpose of this study was to compare the applicability of the Charlson Comorbidity Index (CCI) and Acute Physiology, Age, Chronic Health Evaluation III (APACHE III) to the prediction of the healthcare outcomes of intensive care unit (ICU) patients. Methods: This research was performed with 136 adult patients (age>18 years) who were admitted to the ICU between May and June 2012. Data were measured using the CCI score with a comorbidity index of 19 and the APACHE III score on the standard of the worst result with vital signs and laboratory results. Discrimination was evaluated using receiver operating characteristic (ROC) curves and area under an ROC curve (AUC). Calibration was performed using logistic regression. Results: The overall mortality was 25.7%. The mean CCI and APACHE III scores for survivors were found to be significantly lower than those of non-survivors. The AUC was 0.835 for the APACHE III score and remained high, at 0.688, for the CCI score. The rate of concordance according to the CCI and the APACHE III score was 69.1%. Conclusion: The route of admission, days in ICU, CCI, and APACHE III score are associated with an increased mortality risk in ICU patients.