• 보건행정학회지
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• 제26권1호
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• pp.71-78
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• 2016

The value of using health insurance claim database is continuously rising in healthcare research. In studies where comorbidities act as a confounder, comorbidity adjustment holds importance. Yet researchers are faced with a myriad of options without sufficient information on how to appropriately adjust comorbidity. The purpose of this study is to assist in selecting an appropriate index, look back period, and data range for comorbidity adjustment. No consensus has been formed regarding the appropriate index, look back period and data range in comorbidity adjustment. This study recommends the Charlson comorbidity index be selected when predicting the outcome such as mortality, and the Elixhauser's comorbidity measures be selected when analyzing the relations between various comorbidities and outcomes. A longer look back period and inclusion of all diagnoses of both inpatient and outpatient data led to increased prevalence of comorbidities, but contributed little to model performance. Limited data range, such as the inclusion of primary diagnoses only, may complement limitations of the health insurance claim database, but could miss important comorbidities. This study suggests that all diagnoses of both inpatients and outpatients data, excluding rule-out diagnosis, be observed for at least 1 year look back period prior to the index date. The comorbidity index, look back period, and data range must be considered for comorbidity adjustment. To provide better guidance to researchers, follow-up studies should be conducted using the three factors based on specific diseases and surgeries.

• 한국산학기술학회논문지
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• 제13권6호
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• pp.2672-2679
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• 2012

본 연구는 급성심근경색증 환자의 사망률 측정을 위한 중증도 보정 모형을 개발하여 의료의 질 평가에 필요한 기초자료를 제공하고자 수행되었다. 이를 위해서 질병관리본부의 2005-2008년 퇴원손상환자 699,701건의 자료를 분석하였다. Charlson Comorbidity Index 보정 방법을 이용한 경우와 새롭게 개발된 중증도 보정 모형의 예측력 및 적합도를 비교하기 위해 로지스틱 회귀분석을 실시하였다. 새롭게 개발된 모형에는 연령, 성, 입원경로, PCI 유무, CABG 유무, 동반질환 12가지 변수가 포함되었다. 분석결과 CCI를 이용한 중증도 보정 모형보다 새롭게 개발된 중증도 보정 사망 모형의 C 통계량 값이 0.796(95%CI=0.771-0.821)으로 더 높아 모형의 예측력이 더 우수한 것으로 나타났다. 본 연구를 통하여 중증도 보정 방법에 따라 사망률, 유병률, 예측력에도 차이가 있음을 확인하였다. 향후에 이모형은 의료의 질 평가에 이용하고, 질환별로 임상적 의미와 특성, 모형의 통계적 적합성 등을 고려한 중증도 보정모형이 계속해서 개발되어야 할 것이다.

• 디지털융복합연구
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• 제17권2호
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• pp.217-230
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• 2019

본 연구는 기존 동반질환을 이용한 중증도 보정 방법의 제한점을 보완하기 위해 급성심근경색증 환자의 맞춤형 중증도 보정방법을 개발하고, 이의 타당성을 평가하기 위해 수행되었다. 이를 위하여 질병관리본부에서 2006년부터 2015년까지 10년간 수집한 퇴원손상심층조사 자료 중 주진단이 급성심근경색증인 한국표준질병사인분류(KCD-7) 코드 I20.0~I20.9의 대상자를 추출하였고, 동반질환 중증도 보정 도구로는 기존 활용되고 있는 CCI(Charlson comorbidity index), ECI(Elixhauser comorbidity index)와 새로이 제안하는 CCS(Clinical Classification Software)를 사용하였다. 이에 대한 중증도 보정 사망예측모형 개발을 위하여 머신러닝 기법인 로지스틱 회귀분석, 의사결정나무, 신경망, 서포트 벡터 머신기법을 활용하여 비교하였고 각각의 AUC(Area Under Curve)를 이용하여 개발된 모형을 평가하였다. 이를 평가한 결과 중증도 보정도구로는 CCS 가 가장 우수한 것으로 나타났으며, 머신러닝 기법 중에서는 서포트 벡터 머신을 이용한 모형의 예측력이 가장 우수한 것으로 확인되었다. 이에 향후 의료서비스 결과평가 등 중증도 보정을 위한 연구에서는 본 연구에서 제시한 맞춤형 중증도 보정방법과 머신러닝 기법을 활용하도록 하는 것을 제안한다.

• 보건행정학회지
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• 제21권4호
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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.

• Journal of Preventive Medicine and Public Health
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• 제42권4호
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• pp.267-273
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• 2009

Objectives : To compare the performance of three comorbidity measurements (Charlson comorbidity index, Elixhauser s comorbidity and comorbidity selection) with the effect of different comorbidity lookback periods when predicting in-hospital mortality for patients who underwent percutaneous coronary intervention. Methods : This was a retrospective study on patients aged 40 years and older who underwent percutaneous coronary intervention. To distinguish comorbidity from complications, the records of diagnosis were drawn from the National Health Insurance Database excluding diagnosis that admitted to the hospital. C-statistic values were used as measures for in comparing the predictability of comorbidity measures with lookback period, and a bootstrapping procedure with 1,000 replications was done to determine approximate 95% confidence interval. Results : Of the 61,815 patients included in this study, the mean age was 63.3 years (standard deviation: ${\pm}$10.2) and 64.8% of the population was male. Among them, 1,598 2.6%) had died in hospital. While the predictive ability of the Elixhauser's comorbidity and comorbidity selection was better than that of the Charlson comorbidity index, there was no significant difference among the three comorbidity measurements. Although the prevalence of comorbidity increased in 3 years of lookback periods, there was no significant improvement compared to 1 year of a lookback period. Conclusions : In a health outcome study for patients who underwent percutaneous coronary intervention using National Health Insurance Database, the Charlson comorbidity index was easy to apply without significant difference in predictability compared to the other methods. The one year of observation period was adequate to adjust the comorbidity. Further work to select adequate comorbidity measurements and lookback periods on other diseases and procedures are needed.

• 한국산학기술학회논문지
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• 제19권11호
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• pp.126-136
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• 2018

본 연구는 머신러닝을 활용하여 급성 뇌졸중 퇴원 환자의 중증도 보정 사망 예측 모형 개발을 목적으로 시행하였다. 전국 단위의 퇴원손상심층조사 2006~2015년 자료 중 한국표준질병사인분류(Korean standard classification of disease-KCD 7)에 따라 뇌졸중 코드 I60-I63에 해당하는 대상자를 추출하여 분석하였다. 동반질환 중증도 보정 도구로는 Charlson comorbidity index(CCI), Elixhauser comorbidity index(ECI), Clinical classification software(CCS)의 3가지 도구를 사용하였고 중증도 보정 모형 예측 개발은 로지스틱회귀분석, 의사결정나무, 신경망, 서포트 벡터 머신 기법을 활용하여 비교해 보았다. 뇌졸중 환자의 동반질환으로는 ECI에서는 합병증을 동반하지 않은 고혈압(hypertension, uncomplicated)이 43.8%로, CCS에서는 본태성고혈압(essential hypertension)이 43.9%로 다른 질환에 비해 가장 월등하게 높은 것으로 나타났다. 동반질환 중중도 보정 도구를 비교해 본 결과 CCI, ECI, CCS 중 CCS가 가장 높은 AUC값으로 분석되어 가장 우수한 중증도 보정 도구인 것으로 확인되었다. 또한 CCS, 주진단, 성, 연령, 입원경로, 수술유무 변수를 포함한 중증도 보정 모형 개발 AUC값은 로지스틱 회귀분석의 경우 0.808, 의사결정나무 0.785, 신경망 0.809, 서포트 벡터 머신 0.830로 분석되어 가장 우수한 예측력을 보인 것은 서포트 벡터머신 기법인 것으로 최종 확인되었고 이러한 결과는 추후 보건의료정책 수립에 활용될 수 있을 것이다.

• Journal of Periodontal and Implant Science
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• 제48권2호
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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 Preventive Medicine and Public Health
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• 제39권1호
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• pp.13-20
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• 2006

Objectives: To propose a risk-adjustment model with using insurance claims data and to analyze whether or not the outcomes of non-emergent and isolated coronary artery bypass graft surgery (CABG) differed between the low- and high-volume hospitals for the patients who are at different levels of surgical risk. Methods: This is a cross-sectional study that used the 2002 data of the national health insurance claims. The study data set included the patient level data as well as all the ICD-10 diagnosis and procedure codes that were recorded in the claims. The patient's biological, admission and comorbidity information were used in the risk-adjustment model. The risk factors were adjusted with the logistic regression model. The subjects were classified into five groups based on the predicted surgical risk: minimal (<0.5%), low (0.5% to 2%), moderate (2% to 5%), high (5% to 20%), and severe (=20%). The differences between the low- and high-volume hospitals were assessed in each of the five risk groups. Results: The final risk-adjustment model consisted of ten risk factors and these factors were found to have statistically significant effects on patient mortality. The C-statistic (0.83) and Hosmer-Lemeshow test ($x^2=6.92$, p=0.55) showed that the model's performance was good. A total of 30 low-volume hospitals (971 patients) and 4 high-volume hospitals (1,087 patients) were identified. Significant differences for the in-hospital mortality were found between the low- and high-volume hospitals for the high (21.6% vs. 7.2%, p=0.00) and severe (44.4% vs. 11.8%, p=0.00) risk patient groups. Conclusions: Good model performance showed that insurance claims data can be used for comparing hospital mortality after adjusting for the patients' risk. Negative correlation was existed between surgery volume and in-hospital mortality. However, only patients in high and severe risk groups had such a relationship.

• Journal of Preventive Medicine and Public Health
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• 제42권2호
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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.

• 보건행정학회지
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• 제26권4호
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• pp.359-372
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• 2016

Background: The purpose of this study was to develop risk-adjustment models for acute stroke mortality that were based on data from Health Insurance Review and Assessment Service (HIRA) dataset and to evaluate the validity of these models for comparing hospital performance. Methods: We identified prognostic factors of acute stroke mortality through literature review. On the basis of the avaliable data, the following factors was included in risk adjustment models: age, sex, stroke subtype, stroke severity, and comorbid conditions. Survey data in 2014 was used for development and 2012 dataset was analysed for validation. Prediction models of acute stroke mortality by stroke type were developed using logistic regression. Model performance was evaluated using C-statistics, $R^2$ values, and Hosmer-Lemeshow goodness-of-fit statistics. Results: We excluded some of the clinical factors such as mental status, vital sign, and lab finding from risk adjustment model because there is no avaliable data. The ischemic stroke model with age, sex, and stroke severity (categorical) showed good performance (C-statistic=0.881, Hosmer-Lemeshow test p=0.371). The hemorrhagic stroke model with age, sex, stroke subtype, and stroke severity (categorical) also showed good performance (C-statistic=0.867, Hosmer-Lemeshow test p=0.850). Conclusion: Among risk adjustment models we recommend the model including age, sex, stroke severity, and stroke subtype for HIRA assessment. However, this model may be inappropriate for comparing hospital performance due to several methodological weaknesses such as lack of clinical information, variations across hospitals in the coding of comorbidities, inability to discriminate between comorbidity and complication, missing of stroke severity, and small case number of hospitals. Therefore, further studies are needed to enhance the validity of the risk adjustment model of acute stroke mortality.