- 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.
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Min Ho Kyung, Seok Jun Yoon, Hyeong Sik Ahn, Se min Hwang, Hyun Ju Seo, Kyoung Hoon Kim, Hyeung Keun Park
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J Prev Med Public Health. 2009;42(2):117-122.
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DOI: https://doi.org/10.3961/jpmph.2009.42.2.117
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- 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.
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Citations
Citations to this article as recorded by  - Factors Associated with Hospital Length of Stay among Women’s Cancer Patients: Based on the In-depth Injury Patient Surveillance System Data
Yoonjung Kang, Hyewon Lee
Journal of Health Informatics and Statistics.2022; 47(2): 148. CrossRef - The impact of global budgeting in Taiwan on inpatients with unexplained fever
Keh-Sen Liu, Tsung-Fu Yu, Hsing-Ju Wu, Chun-Yi Lin
Medicine.2019; 98(37): e17131. CrossRef - What happened to health service utilization, health care expenditures, and quality of care in patients with acute pancreatitis after implementation of global budgeting in Taiwan?
Ya-Lin Ko, Jyun-Wei Wang, Hui-Mei Hsu, Chia-Hung Kao, Chun-Yi Lin
Medicine.2018; 97(41): e12620. CrossRef - The impact of global budgeting on health service utilization, health care expenditures, and quality of care among patients with pneumonia in Taiwan
C.-Y. Lin, T. Ma, C.-C. Lin, C.-H. Kao
European Journal of Clinical Microbiology & Infectious Diseases.2016; 35(2): 219. CrossRef - Comparison of Hospital Standardized Mortality Ratio Using National Hospital Discharge Injury Data
Jong-Ho Park, Yoo-Mi Kim, Sung-Soo Kim, Won-Joong Kim, Sung-Hong Kang
Journal of the Korea Academia-Industrial cooperation Society.2012; 13(4): 1739. CrossRef - 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
- The Incidence and Risk Factors of Hypertension that Developed in a Male-workers' Cohort for 3 Years.
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Hyun Ju Seo, Soo Geun Kim, Chong Soon Kim, Yun Kyun Chang, Il Geun Park
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J Prev Med Public Health. 2006;39(3):229-234.
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Abstract
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- OBJECTIVES
Cardiovascular disease is one of the main causes of death and morbidity in Korea. In this study, the prevalence and incidence of developing hypertension in a male-workers' cohort were investigated during 3-years follow-up with a view to find the risk factors that affected the development of hypertension. METHODS: Among the 5,374 people who participated in a routine health check up, 3,852 people with normal blood pressure and who had no history of hypertension were prospectively followed up for 3 years. The classification of hypertension was based on the JNC7 report (the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure). Life style factors and underlying diseases that were related to the risk factors of hypertension were collected by using a self-report questionnaires via the internet. RESULTS: The prevalence of hypertension was 28.3% (1,520/5,374) at the first screening (2001). It was found that the incidence in 2004 of hypertension for the follow-up subjects (3,711) who had normal blood pressure in 2001 was 7.6 per 100 person-year. Multiple logistic regression analysis of the variables related to the risk factors of hypertension was carried out. The relative risks were 1.037 (95% CI=1.022-1.053) as the age increased 1 year and 1.039 (95% CI=1.023-1.055) as the body mass index increased 1 kg/m2. The relative risk for the prehypertensive group was 2.501(95% CI=1.986-3.149) compared to the normotensive group. These results showed that age, body mass index and the baseline blood pressure were significantly related to the incidence of hypertension. CONCLUSIONS: The incidence of hypertension was 7.6 per 100 person-year during follow-up. It was concluded that the risk factors for developing hypertension in the short-term were age, BMI, and prehypertension; Especially, this showed that it is necessary for prehypertensives to manage their body weight and blood pressure to prevent hypertension in middle-age by modifying their life style.
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