Article Text

Download PDFPDF

The Deyo-Charlson and Elixhauser-van Walraven Comorbidity Indices as predictors of mortality in critically ill patients
  1. Karim S Ladha1,2,
  2. Kevin Zhao1,
  3. Sadeq A Quraishi1,2,
  4. Tobias Kurth3,4,
  5. Matthias Eikermann1,2,
  6. Haytham M A Kaafarani5,6,
  7. Eric N Klein7,
  8. Raghu Seethala8,
  9. Jarone Lee5,6,9
  1. 1Division of Critical Care Medicine, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
  2. 2Department of Anaesthesia, Harvard Medical School, Boston, Massachusetts, USA
  3. 3Inserm Research Center for Epidemiology and Biostatistics (U897), Bordeaux, France
  4. 4College for Health Sciences, University of Bordeaux, Bordeaux, France
  5. 5Division of Trauma, Emergency Surgery and Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA
  6. 6Department of Surgery, Harvard Medical School, Boston, Massachusetts, USA
  7. 7Department of Surgery, Hartford Hospital, Hartford, Connecticut, USA
  8. 8Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
  9. 9Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
  1. Correspondence to Dr Jarone Lee; lee.jarone{at}mgh.harvard.edu

Abstract

Objectives 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.

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

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

Primary and secondary outcome 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.

  • EPIDEMIOLOGY

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

Statistics from Altmetric.com

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.