Dataset Information

Validation and adaptation of the Charlson Comorbidity Index using administrative data from the Colombian health system: retrospective cohort study.

ABSTRACT:

Objective

To validate the Charlson Comorbidity Index (CCI) for the Colombian population using administrative databases from the health insurance system.

Design

Retrospective cohort study.

Setting

Database reports of events related to services that insurers provided (Health Promoter Enterprises, EPS in Spanish) in the Colombian health system, which covered 22.19 million residents in 2016.

Participants

The study cohort comprised 3 849 849 patients aged 18 years and up admitted to the Colombian hospitals between 1 January and 31 December 2016.

Primary outcome

The study aimed to gauge the CCI's predictive value for mortality by comparing the calibration and discrimination of three different versions of the index, with mortality information obtained from death certificates, including date of death and diagnoses associated with cause of death. Follow-up was conducted for at least 1 year.

Results

Altogether, 46 429 patients died within 1 year (1.21%). Discriminatory power in predicting 1-year mortality was calculated for three versions of the ICC. In the original CCI model, the area under the Receiver operating characteristic (ROC) curve was 0.906 (95% CI (0.906 to 0.907), p<0.001). In the version for Colombia, it was 0.908 (95% CI (0.908 to 0.909), p<0.001) and for the new model it was 0.909 (95% CI (0.908 to 0.910), p<0.001).

Conclusions

Adapting the CCI based on the 14 predictive variables of the new model resulted in an adequate predictive value for 1-year mortality in patients who were hospitalised for all causes. These findings support the use of the modified CCI in the Colombian population.

SUBMITTER: Oliveros H

PROVIDER: S-EPMC8943719 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

Validation and adaptation of the Charlson Comorbidity Index using administrative data from the Colombian health system: retrospective cohort study.

Oliveros Henry H Buitrago Giancarlo G

BMJ open 20220323 3

<h4>Objective</h4>To validate the Charlson Comorbidity Index (CCI) for the Colombian population using administrative databases from the health insurance system.<h4>Design</h4>Retrospective cohort study.<h4>Setting</h4>Database reports of events related to services that insurers provided (Health Promoter Enterprises, EPS in Spanish) in the Colombian health system, which covered 22.19 million residents in 2016.<h4>Participants</h4>The study cohort comprised 3 849 849 patients aged 18 years and up ...[more]

PMID: 35321892

Similar Datasets

Project description:ObjectivesThe Charlson comorbidity index (CCI), the most ubiquitous comorbid risk score, predicts one-year mortality among hospitalized patients and provides a single aggregate measure of patient comorbidity. The Quan adaptation of the CCI revised the CCI coding algorithm for applications to administrative claims data using the International Classification of Diseases (ICD). The purpose of the current study is to adapt and validate a coding algorithm for the CCI using the SNOMED CT standardized vocabulary, one of the most commonly used vocabularies for data collection in healthcare databases in the U.S.MethodsThe SNOMED CT coding algorithm for the CCI was adapted through the direct translation of the Quan coding algorithms followed by manual curation by clinical experts. The performance of the SNOMED CT and Quan coding algorithms were compared in the context of a retrospective cohort study of inpatient visits occurring during the calendar years of 2013 and 2018 contained in two U.S. administrative claims databases. Differences in the CCI or frequency of individual comorbid conditions were assessed using standardized mean differences (SMD). Performance in predicting one-year mortality among hospitalized patients was measured based on the c-statistic of logistic regression models.ResultsFor each database and calendar year combination, no significant differences in the CCI or frequency of individual comorbid conditions were observed between vocabularies (SMD ≤ 0.10). Specifically, the difference in CCI measured using the SNOMED CT vs. Quan coding algorithms was highest in MDCD in 2013 (3.75 vs. 3.6; SMD = 0.03) and lowest in DOD in 2018 (3.93 vs. 3.86; SMD = 0.02). Similarly, as indicated by the c-statistic, there was no evidence of a difference in the performance between coding algorithms in predicting one-year mortality (SNOMED CT vs. Quan coding algorithms, range: 0.725-0.789 vs. 0.723-0.787, respectively). A total of 700 of 5,348 (13.1%) ICD code mappings were inconsistent between coding algorithms. The most common cause of discrepant codes was multiple ICD codes mapping to a SNOMED CT code (n = 560) of which 213 were deemed clinically relevant thereby leading to information gain.ConclusionThe current study repurposed an important tool for conducting observational research to use the SNOMED CT standardized vocabulary.

Dataset Information

Validation and adaptation of the Charlson Comorbidity Index using administrative data from the Colombian health system: retrospective cohort study.

Objective

Design

Setting

Participants

Primary outcome

Results

Conclusions

Publications

Validation and adaptation of the Charlson Comorbidity Index using administrative data from the Colombian health system: retrospective cohort study.

Similar Datasets

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