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

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.

[1]  Matthew Knuiman,et al.  Estimating Long-Term Survival of Critically Ill Patients: The PREDICT Model , 2008, PloS one.

[2]  H. Flaatten,et al.  Severity scoring in the ICU: a review , 2008, Acta anaesthesiologica Scandinavica.

[3]  D. Angus,et al.  Racial variation in the incidence, care, and outcomes of severe sepsis: analysis of population, patient, and hospital characteristics. , 2008, American journal of respiratory and critical care medicine.

[4]  C. Sprung,et al.  Are Blood Transfusions Associated with Greater Mortality Rates?: Results of the Sepsis Occurrence in Acutely Ill Patients Study , 2008, Anesthesiology.

[5]  J. Finn,et al.  Combining multiple comorbidities with Acute Physiology Score to predict hospital mortality of critically ill patients: a linked data cohort study , 2007, Anaesthesia.

[6]  S. Keenan,et al.  Adjustment of intensive care unit outcomes for severity of illness and comorbidity scores. , 2006, Journal of critical care.

[7]  Bekele Afessa,et al.  The influence of missing components of the Acute Physiology Score of APACHE III on the measurement of ICU performance , 2005, Intensive Care Medicine.

[8]  Peter J Pronovost,et al.  A systematic review of the Charlson comorbidity index using Canadian administrative databases: a perspective on risk adjustment in critical care research. , 2005, Journal of critical care.

[9]  J. Vincent.  Evidence-based medicine in the ICU: important advances and limitations. , 2004, Chest.

[10]  D. Wagner,et al.  Impact of Different Measures of Comorbid Disease on Predicted Mortality of Intensive Care Unit Patients , 2002, Medical care.

[11]  J. Vincent,et al.  Anemia and blood transfusion in critically ill patients. , 2002, JAMA.

[12]  M Maclure,et al.  Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. , 2001, American journal of epidemiology.

[13]  A. Padkin,et al.  Using high quality clinical databases to complement the results of randomised controlled trials: the case of recombinant human activated protein C , 2001, BMJ : British Medical Journal.

[14]  G. Clermont,et al.  Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care , 2001, Critical care medicine.

[15]  J. Norrie,et al.  Assessment of the performance of five intensive care scoring models within a large Scottish database , 2000, Critical care medicine.

[16]  L Frank,et al.  When an Entire Country Is a Cohort , 2000, Science.

[17]  T. F. Andersen,et al.  The Danish National Hospital Register. A valuable source of data for modern health sciences. , 1999, Danish medical bulletin.

[18]  R M Poses,et al.  Prediction of survival of critically ill patients by admission comorbidity. , 1996, Journal of clinical epidemiology.

[19]  L. Goldman,et al.  The SUPPORT Prognostic Model: Objective Estimates of Survival for Seriously Ill Hospitalized Adults , 1995, Annals of Internal Medicine.

[20]  S. Lemeshow,et al.  A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study , 1993 .

[21]  S. Lemeshow,et al.  Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. , 1993, JAMA.

[22]  D. Pittet,et al.  Importance of pre-existing co-morbidities for prognosis of septicemia in critically ill patients , 1993, Intensive Care Medicine.

[23]  R. Deyo,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.

[24]  W. Knaus,et al.  APACHE III study design: analytic plan for evaluation of severity and outcome in intensive care unit patients. Implications. , 1989, Critical care medicine.

[25]  E. Draper,et al.  APACHE II: A severity of disease classification system , 1985, Critical care medicine.

[26]  S. Lemeshow,et al.  Comparison of Charlson comorbidity index with sAPs and APAChE scores for prediction of mortality following intensive care , 2011 .

[27]  S. Lemeshow,et al.  A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. , 1993, JAMA.

[28]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.