The use of Charlson comorbidity index for observational studies using administrative data in Korea

The number of large-scale epidemiological studies using administrative databases has rapidly increased in recent years. However, in observational studies, the outcomes of interest are heavily influenced by concurrent or pre-existing comorbidities, which act as confounders, and appropriate adjustment of comorbidities is essential to minimize bias. Although several comorbidity indices are available for this purpose, Charlson comorbidity index (CCI) is the most widely used one. The original version of the CCI was developed for use in longitudinal studies, to classify comorbidities that might alter the risk of mortality. It included 19 comorbidities, with each comorbidity having an assigned weight from 1 to 6; the total CCI score is a simple sum of the weights. Although the original CCI was based on a review of medical records, many modified versions have been proposed in subsequent studies using claims data. The CCI and its modified versions are used in virtually all clinical settings, including oncology, cardiology, respiratory care, emergency care, surgery, intensive care, and geriatrics. Appropriate use of a comorbidity index, such as the CCI, is necessary to minimize bias in observational studies in the era of increasing use of administrative data for healthcare research.

Authors

Publication Details

Journal
Precision and Future Medicine
Published
2025-03-31
DOI
https://doi.org/10.23838/pfm.2024.00191
Citations
6
Primary Topic
Technology and Data Analysis
Type
article
Field-Weighted Citation Impact
19.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The use of Charlson comorbidity index for observational studies using administrative data in Korea

Kyungdo Han, Dong Wook Shin
6 citations
Precision and Future Medicine
Technology and Data Analysis
19.48
article

The use of Charlson comorbidity index for observational studies using administrative data in Korea

Kyungdo Han, Dong Wook Shin
article en
6 citations

Abstract

The number of large-scale epidemiological studies using administrative databases has rapidly increased in recent years. However, in observational studies, the outcomes of interest are heavily influenced by concurrent or pre-existing comorbidities, which act as confounders, and appropriate adjustment of comorbidities is essential to minimize bias. Although several comorbidity indices are available for this purpose, Charlson comorbidity index (CCI) is the most widely used one. The original version of the CCI was developed for use in longitudinal studies, to classify comorbidities that might alter the risk of mortality. It included 19 comorbidities, with each comorbidity having an assigned weight from 1 to 6; the total CCI score is a simple sum of the weights. Although the original CCI was based on a review of medical records, many modified versions have been proposed in subsequent studies using claims data. The CCI and its modified versions are used in virtually all clinical settings, including oncology, cardiology, respiratory care, emergency care, surgery, intensive care, and geriatrics. Appropriate use of a comorbidity index, such as the CCI, is necessary to minimize bias in observational studies in the era of increasing use of administrative data for healthcare research.

Precision and Future MedicineVol. 9(1)
Openalex Percentile: Top 1%
Technology and Data Analysis
19.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The use of Charlson comorbidity index for observational studies using administrative data in Korea — Kyungdo Han, Dong Wook Shin · Precision and Future Medicine (2025) | TGRS Research Map | TGRS