The Deyo-Charlson and Elixhauser-van Walraven Comorbidity Indices as predictors of mortality in critically ill patients.
OBJECTIVE
Our primary objective was to compare the utility of the Deyo-Charlson Comorbidity Index (DCCI) and Elixhauser-van Walraven Comorbidity Index (EVCI) to predict mortality in intensive care unit (ICU) patients.


METHODS
Observational study of 2 tertiary academic centres located in Boston, Massachusetts.


METHODS
The study cohort consisted of 59,816 patients from admitted to 12 ICUs between January 2007 and December 2012.


METHODS
For the primary analysis, receiver operator characteristic curves were constructed for mortality at 30, 90, 180, and 365 days using the DCCI as well as EVCI, and the areas under the curve (AUCs) were compared. Subgroup analyses were performed within different types of ICUs. Logistic regression was used to add age, race and sex into the model to determine if there was any improvement in discrimination.


RESULTS
At 30 days, the AUC for DCCI versus EVCI was 0.65 (95% CI 0.65 to 0.67) vs 0.66 (95% CI 0.65 to 0.66), p=0.02. Discrimination improved at 365 days for both indices (AUC for DCCI 0.72 (95% CI 0.71 to 0.72) vs AUC for EVCI 0.72 (95% CI 0.72 to 0.72), p=0.46). The DCCI and EVCI performed similarly across ICUs at all time points, with the exception of the neurosciences ICU, where the DCCI was superior to EVCI at all time points (1-year mortality: AUC 0.73 (95% CI 0.72 to 0.74) vs 0.68 (95% CI 0.67 to 0.70), p=0.005). The addition of basic demographic information did not change the results at any of the assessed time points.


CONCLUSIONS
The DCCI and EVCI were comparable at predicting mortality in critically ill patients. The predictive ability of both indices increased when assessing long-term outcomes. Addition of demographic data to both indices did not affect the predictive utility of these indices. Further studies are needed to validate our findings and to determine the utility of these indices in clinical practice.
Division of Critical Care Medicine, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts, USA.
Karim S
KS
Ladha
Division of Critical Care Medicine, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
Kevin
K
Zhao
Division of Critical Care Medicine, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts, USA.
Sadeq A
SA
Quraishi
Inserm Research Center for Epidemiology and Biostatistics (U897), Bordeaux, France College for Health Sciences, University of Bordeaux, Bordeaux, France.
Tobias
T
Kurth
Division of Critical Care Medicine, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts, USA.
Matthias
M
Eikermann
Division of Trauma, Emergency Surgery and Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA Department of Surgery, Harvard Medical School, Boston, Massachusetts, USA.
Haytham M A
HM
Kaafarani
Department of Surgery, Hartford Hospital, Hartford, Connecticut, USA.
Eric N
EN
Klein
Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Raghu
R
Seethala
Division of Trauma, Emergency Surgery and Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA Department of Surgery, Harvard Medical School, Boston, Massachusetts, USA Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
Jarone
J
Lee
D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D002423:Cause of Death; D015331:Cohort Studies; D015897:Comorbidity; D003422:Critical Care; D016638:Critical Illness / Q000401:mortality*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D016015:Logistic Models; D008297:Male; D008404:Massachusetts / Q000453:epidemiology; D008875:Middle Aged; D011379:Prognosis; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*
EPIDEMIOLOGY