Abstract
The Charlson Comorbidity Index (CCI) is a method for classifying comorbidities as a single measure on a scale, based on ICD-9-CM codes over administrative databases. This study aims to compare different variants of the CCI and their relation with in-hospital mortality, using different weights associated to each comorbidity. Within the 9,613,563 hospitalizations in the 15-year studied period, the area under the ROC curve was higher considering the original Charlson weights, when compared to other more recent proposals. Also, all the indexes had an increased association with in-hospital mortality throughout time. For recent years this association is stronger, demonstrating an increased applicability of the Charlson index in administrative databases. The validity of the coding algorithms strongly depends on the completeness and accuracy of diagnostic coding, particularly considering secondary diagnoses. The Charlson index can be a valuable tool for longitudinal studies, but important differences among weights, through years, and for different main diagnoses, should be considered and discussed.
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Acknowledgments
The authors would like to thank the support given by the Project “NORTE-01-0145-FEDER-000016” (NanoSTIMA), financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF). The authors wish also to thank the Portuguese Ministry of Health’s Authority for Health Services (Administração Central do Sistema de Saúde I. P. – ACSS) for providing access to national hospitalizations data.
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Freitas, A., Santos, J.V., Lobo, M., Santos, C. (2017). Comparing Comorbidity Adjustment Scores for Predicting in-Hospital Mortality Using Administrative Data. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds) Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in Intelligent Systems and Computing, vol 571. Springer, Cham. https://doi.org/10.1007/978-3-319-56541-5_33
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