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Hyeung Keun Park 2 Articles
Prognostic Impact of Charlson Comorbidity Index Obtained from Medical Records and Claims Data on 1-year Mortality and Length of Stay in Gastric Cancer Patients.
Min Ho Kyung, Seok Jun Yoon, Hyeong Sik Ahn, Se min Hwang, Hyun Ju Seo, Kyoung Hoon Kim, Hyeung Keun Park
J Prev Med Public Health. 2009;42(2):117-122.
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AbstractAbstract PDF
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.


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  • Predictive Ability of Charlson Comorbidity Index on Outcomes From Lung Cancer
    Apar Kishor Ganti, Emily Siedlik, Alissa S. Marr, Fausto R. Loberiza, Anne Kessinger
    American Journal of Clinical Oncology.2011; 34(6): 593.     CrossRef
  • Comparative Study on Three Algorithms of the ICD-10 Charlson Comorbidity Index with Myocardial Infarction Patients
    Kyoung Hoon Kim
    Journal of Preventive Medicine and Public Health.2010; 43(1): 42.     CrossRef
  • The Impact of Medicaid Expansion to include population with low income on the preventable hospitalizations
    Hyun-Chul Shin, Se-Ra Kim
    Korean Journal of Health Policy and Administration.2010; 20(1): 87.     CrossRef
  • Charlson Comorbidity Index as a Predictor of Long-Term Survival after Surgery for Breast Cancer: A Nationwide Retrospective Cohort Study in South Korea
    Hye Kyung Woo, Jong Hyock Park, Han Sung Kang, So Young Kim, Sang Il Lee, Hyung Ho Nam
    Journal of Breast Cancer.2010; 13(4): 409.     CrossRef
  • A comparison of the Charlson comorbidity index derived from medical records and claims data from patients undergoing lung cancer surgery in Korea: a population-based investigation
    Hyun-Ju Seo, Seok-Jun Yoon, Sang-Il Lee, Kun Sei Lee, Young Ho Yun, Eun-Jung Kim, In-Hwan Oh
    BMC Health Services Research.2010;[Epub]     CrossRef
  • Health Outcome Prediction Using the Charlson Comorbidity Index In Lung Cancer Patients
    Se-Won Kim, Seok-Jun Yoon, Min-Ho Kyung, Young-Ho Yun, Young-Ae Kim, Eun-Jung Kim
    Korean Journal of Health Policy and Administration.2009; 19(4): 18.     CrossRef
  • Factors Affecting Health of the Rural Residents
    Dong-Koog Son, Kyu-Sik Lee, Jong-Ku Park, Sang-Baek Koh, Ki-Nam Jin, Eun-Woo Nam, Hae-Jong Lee
    Korean Journal of Health Policy and Administration.2009; 19(4): 1.     CrossRef
Severity-Adjusted Mortality Rates: The Case of CABG Surgery.
Hyeung Keun Park, Hyeongsik Ahn, Young Dae Kwon, You Cheol Shin, Jin Seok Lee, Hae Joon Kim, Moon Jun Sohn
Korean J Prev Med. 2001;34(1):21-27.
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  • 24 Download
AbstractAbstract PDF
To develop a model that will predict the mortality of patients undergoing Coronary Artery Bypass Graft (CABG) and evaluate the performance of hospitals. METHODS: Data from 564 CABGs performed in six general hospitals were collected through medical record abstraction by registered nurses. Variables studied involved risk factors determined by severity measures. Risk modeling was performed through logistic regression and validated with cross-validation. The statistical performance of the developed model was evaluated using c-statistic, R2, and Hosmer-Lemeshow statistic. Hospital performance was assessed by severity-adjusted mortalities. RESULTS: The developed model included age, sex, BUN, EKG rhythm, Congestive Heart Failure at admission, acute mental change within 24 hours, and previous angina pectoris history. The c-statistic and R2 were 0.791 and 0.101, respectively. Hosmer-Lemeshow statistic was 10.3(p value=0.2415). One hospital had a significantly higher mortality rate than the average mortality rate, while others were not significantly different. CONCLUSION: Comparing the quality of service by severity adjusted mortality rates, there were significant differences in hospital performance. The severity adjusted mortality rate of CABG surgery may be an indicator for evaluating hospital performance in Korea.

JPMPH : Journal of Preventive Medicine and Public Health