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Published on: January 8, 2020
Comorbidity adjustment index for the international classification of diseases, 10th revision
Robert Antonio Ramiarina1, Beatriz Luiza Ramiarina, Renan Moritz V R Almeida
1Ministério da Saúde, Rio de Janeiro, RJ, Brasil.
Insights
A new risk index, similar to the Charlson comorbidity index, effectively predicts in-hospital mortality. This tool aids in risk assessment when comprehensive hospital data is unavailable.
Area of Science:
- Clinical Epidemiology
- Health Services Research
Background:
- The Charlson Comorbidity Index (CCI) is widely used for risk adjustment in healthcare research.
- Adapting existing indices to specific healthcare settings and data availability is crucial for accurate risk assessment.
Purpose of the Study:
- To develop a Charlson-like comorbidity index using clinical conditions and weights from the original CCI.
- To evaluate the index's ability to predict in-hospital mortality.
Main Methods:
- Adapted clinical conditions and weights from the International Classification of Diseases, 10th revision.
- Applied the index to 3,733 adult hospital admissions in Rio de Janeiro, Brazil (2001-2003).
- Developed logistic regression models to predict in-hospital mortality using the new index and other variables.
Main Results:
- The developed risk index effectively discriminated in-hospital death, with an Area Under the Curve (AUC) of 0.86 for the full model.
- A risk score of 1 or greater was observed in 22.3% of patients, associated with a 4.5% mortality rate.
- Each unit increase in the risk score correlated with a nearly 50% increase in the odds of in-hospital death.
Conclusions:
- The developed risk index demonstrates strong predictive power for in-hospital mortality.
- This index is a valuable tool for risk adjustment, particularly when detailed hospital databases are limited.
Objective:
To develop a Charlson-like comorbidity index based on clinical conditions and weights of the original Charlson comorbidity index.
Methods:
Clinical conditions and weights were adapted from the International Classification of Diseases, 10th revision and applied to a single hospital admission diagnosis. The study included 3,733 patients over 18 years of age who were admitted to a public general hospital in the city of Rio de Janeiro, southeast Brazil, between Jan 2001 and Jan 2003. The index distribution was analyzed by gender, type of admission, blood transfusion, intensive care unit admission, age and length of hospital stay. Two logistic regression models were developed to predict in-hospital mortality including: a) the aforementioned variables and the risk-adjustment index (full model); and b) the risk-adjustment index and patient's age (reduced model).
Results:
Of all patients analyzed, 22.3% had risk scores > or = 1, and their mortality rate was 4.5% (66.0% of them had scores > or = 1). Except for gender and type of admission, all variables were retained in the logistic regression. The models including the developed risk index had an area under the receiver operating characteristic curve of 0.86 (full model), and 0.76 (reduced model). Each unit increase in the risk score was associated with nearly 50% increase in the odds of in-hospital death.
Conclusions:
The risk index developed was able to effectively discriminate the odds of in-hospital death which can be useful when limited information is available from hospital databases.
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