• Health Policy and Management
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• v.21 no.4
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• pp.541-564
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• 2011

This study aimed to investigate health outcome of acute myocardial infarction (AMI) patients such as mortality and length of stay in hospital and to identify factors associated with the health outcome according to the comorbidity index. Nation-wide representative samples of 3,748 adult inpatients aged between 20-85 years with acute myocardial infarction were derived from the Korea National Hospital Discharge Injury Survey, 2005-2008. Comorbidity index was measured using the Charlson Comorbidity Index (CCI). The data were analyzed using t-test, ANOVA, multiple regression, logistic regression analysis in order to investigate the effect of comorbidity on health outcome. According to the study results, the factors associated with length of hospital stay of acute myocardial infarction patients were gender, insurance type, residential area scale, admission route, PCI perform, CABG perform, and CCI. The factors associated with mortality of acute myocardial infarction patients were age, admission route, PCI perform, and CCI. CCI with a higher length of hospital stay and mortality also increased significantly. This study demonstrated comorbidity risk adjustment for health outcome and presented important data for health care policy. In the future study, more detailed and adequate comorbidity measurement tool should be developed, so patients' severity can be adjusted accurately.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.22-32
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• 2016

As the number of aged population rapidly goes up, the cases of stroke and the related medical expenses continuously increase. The purpose of this study is to investigate the mortality of stroke patients based on CCI(Charlson Comorbidity Index) by utilizing the Korea National Hospital Discharge Injury Survey, analyzing the factors associated with the mortality of stroke patients. We analyzed 21,494 cases which are classified as the death of strokes aged over 20 years by using the Korea National Hospital Discharge Injury Survey between the year 2005 and 2010. In order to find out the mortality based on CCI and status of comorbidity, we used the technical statistics. We performed a logistic regression analysis to examine the reasons for the mortality of the strokes. We found that the independent variables for the influence of the mortality of strokes include age, type of insurance, residence urban size, size of hospital beds, the location of hospital, admission route, physical therapy, brain surgery, type of stroke, and CCI. This indicates that the effective monitoring on the age, types of stroke, comorbidity is needed. In addition to this, more medical support toward medicaid patients are needed, too. We believe that these results will be used positively for the evaluation of the stroke patients, providing the basic materials for the further research on the establishment of the health-related policy.

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

• Health Policy and Management
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• v.19 no.4
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• pp.18-32
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• 2009

The goal of this study was to predict the health outcomes of lung cancer surgery based on the Charlson comorbidity index (CCI). An attempt was likewise made to assess the prognostic value of such data for predicting mortality, survival rate, and length of hospital stay. A medical-record review of 389 patients with non-small-cell lung cancer was performed. To evaluate the agreement, the kappa coefficient was tested. Logistic-regression analysis was also conducted within two years after the surgery to determine the association of CCI with death. Survival and multiple-regression analyses were used to evaluate the relationship between CCI and the hospital care outcomes within two-year survival after lung cancer surgery and the length of hospital stay. The results of the study showed that CCI is a valid prognostic indicator of two-year mortality and length of hospital stay, and that it shows the health outcomes, such as death, survival rate, and length of hospital stay, after the surgery, thus enabling the development and application of the methodology using a systematic and objective scale for the results.

• Journal of Preventive Medicine and Public Health
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• v.42 no.2
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• pp.117-122
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• 2009

Objectives : We tried to evaluate the agreement of the Charlson comorbidity index values(CCI) obtained from different sources(medical records and National Health Insurance claims data) for gastric cancer patients. We also attempted to assess the prognostic value of these data for predicting 1-year mortality and length of the hospital stay(length of stay). Methods : Medical records of 284 gastric cancer patients were reviewed, and their National Health Insurance claims data and death certificates were also investigated. To evaluate agreement, the kappa coefficient was tested. Multiple logistic regression analysis and multiple linear regression analysis were performed to evaluate and compare the prognostic power for predicting 1 year mortality and length of stay. Results : The CCI values for each comorbid condition obtained from 2 different data sources appeared to poorly agree(kappa: 0.00-0.59). It was appeared that the CCI values based on both sources were not valid prognostic indicators of 1-year mortality. Only medical record-based CCI was a valid prognostic indicator of length of stay, even after adjustment of covariables($\beta$ = 0.112, 95% CI = [0.017-1.267]). Conclusions : There was a discrepancy between the data sources with regard to the value of CCI both for the prognostic power and its direction. Therefore, assuming that medical records are the gold standard for the source for CCI measurement, claims data is not an appropriate source for determining the CCI, at least for gastric cancer.

• Journal of Periodontal and Implant Science
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• v.48 no.2
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• pp.92-102
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• 2018

Purpose: This study investigated the validity of the Charlson comorbidity index (CCI) as a predictor of periodontal disease (PD) over a 12-year period. Methods: Nationwide representative samples of 149,785 adults aged ${\geq}60$ years with PD (International Classification of Disease, 10th revision [ICD-10], K052-K056) were derived from the National Health Insurance Service-Elderly Cohort during 2002-2013. The degree of comorbidity was measured using the CCI (grade 0-6), including 17 diseases weighted on the basis of their association with mortality, and data were analyzed using multivariate Cox proportional-hazards regression in order to investigate the associations of comorbid diseases (CDs) with PD. Results: The multivariate Cox regression analysis with adjustment for sociodemographic factors (sex, age, household income, insurance status, residence area, and health status) and CDs (acute myocardial infarction, congestive heart failure, peripheral vascular disease, cerebral vascular accident, dementia, pulmonary disease, connective tissue disorders, peptic ulcer, liver disease, diabetes, diabetes complications, paraplegia, renal disease, cancer, metastatic cancer, severe liver disease, and human immunodeficiency virus [HIV]) showed that the CCI in elderly comorbid participants was significantly and positively correlated with the presence of PD (grade 1: hazard ratio [HR], 1.11; P<0.001; grade ${\geq}2$: HR, 1.12, P<0.001). Conclusions: We demonstrated that a higher CCI was a significant predictor of greater risk for PD in the South Korean elderly population.

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

• Journal of Preventive Medicine and Public Health
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• v.42 no.1
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• pp.49-58
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• 2009

Objectives : The purpose of the current study was to evaluate the usefulness of the following four comorbidity indices in gastric cancer patients who underwent surgery: Charlson Comorbidity Index(CCI), Cumulative Illness rating scale(CIRS), Index of Co-existent Disease(ICED), and Kaplan-Feinstein Scale(KFS). Methods : The study subjects were 614 adults who underwent surgery for gastric cancer at K hospital between 2005 and 2007. We examined the test-retest and inter-rater reliability of 4 comorbidity indices for 50 patients. Reliability was evaluated with Spearman rho coefficients for CCI and CIRS, while Kappa values were used for the ICED and KFS indices. Logistic regression was used to determine how these comorbidity indices affected unplanned readmission and death. Multiple regression was used for determining if the comorbidity indices affected length of stay and hospital costs. Results : The test-retest reliability of CCI and CIRS was substantial(Spearman rho=0.746 and 0.775, respectively), while for ICED and KFS was moderate(Kappa=0.476 and 0.504, respectively). The inter-rater reliability of the CCI, CIRS, and ICED was moderate(Spearman rho=0.580 and 0.668, and Kappa=0.433, respectively), but for KFS was fair(Kappa=0.383). According to the results from logistic regression, unplanned readmissions and deaths were not significantly different between the comorbidity index scores. But, according to the results from multiple linear regression, the CIRS group showed a significantly increased length of hospital stay(p<0.01). Additionally, CCI showed a significant association with increased hospital costs (p<0.01). Conclusions : This study suggests that the CCI index may be useful in the estimation of comorbidities associated with hospital costs, while the CIRS index may be useful where estimatation of comorbiditie associated with the length of hospital stay are concerned.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2672-2679
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• 2012

The study was done to provide basic data of medical quality evaluation after developing the comorbidity disease mortality measurement modeled on the severity-adjustment method of AMI. This study analyzed 699,701 cases of Hospital Discharge Injury Data of 2005 and 2008, provided by the Korea Centers for Disease Control and Prevention. We used logistic regression to compare the risk-adjustment model of the Charlson Comorbidity Index with the predictability and compatibility of our severity score model that is newly developed for calibration. The models severity method included age, sex, hospitalization path, PCI presence, CABG, and 12 variables of the comorbidity disease. Predictability of the newly developed severity models, which has statistical C level of 0.796(95%CI=0.771-0.821) is higher than Charlson Comorbidity Index. This proves that there are differences of mortality, prevalence rate by method of mortality model calibration. In the future, this study outcome should be utilized more to achieve an improvement of medical quality evaluation, and also models will be developed that are considered for clinical significance and statistical compatibility.

• 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.