Comparison of Charlson comorbidity index with SAPS and APACHE scores for prediction of mortality following intensive care

Steffen Christensen¹, Martin Berg Johansen¹, Christian Fynbo Christiansen¹, Reinhold Jensen², Stanley Lemeshow^1,3
1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; ²Department of Intensive Care, Skejby Hospital, Aarhus University Hospital, Aarhus, Denmark; ³Division of Biostatistics, College of Public Health, Ohio State University, Columbus, OH, USA

Background: Physiology-based severity of illness scores are often used for risk adjustment in observational studies of intensive care unit (ICU) outcome. However, the complexity and time constraints of these scoring systems may limit their use in administrative databases. Comorbidity is a main determinant of ICU outcome, and comorbidity scores can be computed based on data from most administrative databases. However, limited data exist on the performance of comorbidity scores in predicting mortality of ICU patients.
Objectives: To examine the performance of the Charlson comorbidity index (CCI) alone and in combination with other readily available administrative data and three physiology-based scores (acute physiology and chronic health evaluations [APACHE] II, simplified acute physiology score [SAPS] II, and SAPS III) in predicting short- and long-term mortality following intensive care.
Methods: For all adult patients (n = 469) admitted to a tertiary university–affiliated ICU in 2007, we computed APACHE II, SAPS II, and SAPS III scores based on data from medical records. Data on CCI score age and gender, surgical/medical status, social factors, mechanical ventilation and renal replacement therapy, primary diagnosis, and complete follow-up for 1-year mortality was obtained from administrative databases. We computed goodness-of-fit statistics and c-statistics (area under ROC [receiver operating characteristic] curve) as measures of model calibration (ability to predict mortality proportions over classes of risk) and discrimination (ability to discriminate among the patients who will die or survive), respectively.
Results: Goodness-of-fit statistics supported model fit for in-hospital, 30-day, and 1-year mortality of all combinations of the CCI score. Combining the CCI score with other administrative data revealed c-statistics of 0.75 (95% confidence interval [CI] 0.69–0.81) for in-hospital mortality, 0.75 (95% CI 0.70–0.80) for 30-day mortality, and 0.72 (95% CI 0.68–0.77) for 1-year mortality. There were no major differences in c-statistics between physiology-based systems and the CCI combined with other administrative data.
Conclusion: The CCI combined with administrative data predict short- and long-term mortality for ICU patients as well as physiology-based scores.

Keywords: epidemiology, CCI, physiology-based scores, ICU