Related Experiment Videos
[Hospital Information Systems as risk adjustment in performance indicators]
M Martins1, C Travassos, J Carvalho de Noronha
1Departamento de Administração e Planejamento em Saúde, Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brasil. martins@ensp.fiocruz.br
Insights
The Charlson Comorbidity Index (CCI) showed limited value for adjusting hospital mortality risk in the Brazilian Hospital Database (SIH/SUS). Age was a better predictor, though combining CCI with age is recommended for risk adjustment.
Area of Science:
- Healthcare Informatics
- Public Health
- Epidemiology
Background:
- Risk adjustment is crucial for comparing hospital performance.
- The Brazilian Hospital Database (SIH/SUS) is a key data source for healthcare analysis in Brazil.
- Comorbidity indices, like the Charlson Comorbidity Index (CCI), are used to account for patient severity.
Purpose of the Study:
- To analyze the utility of the SIH/SUS database for risk adjustment of hospital mortality.
- To evaluate the effectiveness of the CCI in risk adjustment using SIH/SUS data.
Main Methods:
- The CCI was applied to 40,299 hospital admissions in Rio de Janeiro.
- Multiple logistic regression analyzed the impact of CCI on mortality probability.
- The study assessed CCI's ability to measure comorbidity burden, excluding the primary diagnosis.
Main Results:
- The CCI was greater than zero in only 5.7% of admissions.
- Combining CCI with age significantly increased cases with a non-zero value.
- Risk adjustment models using CCI demonstrated low sensitivity.
Conclusions:
- Comorbidity is a significant mortality predictor but showed poor discrimination of case severity in the SIH/SUS database, likely due to incomplete diagnostic information.
- Age emerged as the most potent predictor of mortality risk within the SIH/SUS data.
- Despite data quality limitations, using CCI combined with age is recommended for risk adjustment in SIH/SUS measures.
Objective:
To analyze the use of the Brazilian Hospital Database (SIH/SUS) on risk adjustment of hospital mortality, and to evaluate the usefulness of the Charlson comorbidity index (CCI) for risk adjustment of indicators calculated with the available data from the SIH/SUS.
Methods:
The comorbidity index was applied on 40,299 patients admitted in hospital in Rio de Janeiro, Brazil. CCI determines specific values to 17 clinical conditions to measure the burden of the patient's comorbidity, not taking into consideration the main diagnosis. Multiple logistic regression was applied to assess the impact of CCI in estimating the probability of dying.
Results:
CCI was greater than zero in only 5.7% admissions. When combined with age (combined CCI), the percentage of cases with a value greater than zero increased considerably. These models, however, showed to have a low sensitivity.
Conclusions:
Despite comorbidity is an important predictor for the risk of dying, it was observed that this is not a good discriminatory variable of case severity in the studied database. This maybe due to incomplete diagnostic information in the database. In the SIH/SUS data, age is the most important predictor of the risk of dying. However, despite the limited quality of diagnostic information in SIH/SUS, the use of CCI combined with age for adjustment of the risk of dying is recommended in measures using this database.