The Deyo-Charlson and Elixhauser-van Walraven Comorbidity Indices as predictors of mortality in critically ill patients

Abstract

ObjectivesOur 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.SettingObservational study of 2 tertiary academic centres located in Boston, Massachusetts.ParticipantsThe study cohort consisted of 59 816 patients from admitted to 12 ICUs between January 2007 and December 2012.Primary and secondary outcomeFor 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.ResultsAt 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.ConclusionsThe 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.

Charlson comorbidity index derived from chart review or administrative data: agreement and prediction of mortality in intensive care patients

“…The finding that the CCI combined with other readily available variables performed almost as well as physiology-based scores is in concert with previous findings 17. Moreover, the CCI can be calculated at the time of admission to the ICU, in contrast to physiology-based scores that are dependent on laboratory and bedside clinical data 18. The c -statistic for the CCI was higher for 1-year than for 30-day mortality, as was shown previously in ICU patients 18…”

Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease

“…[13] The assessment of comorbidities is an essential component in determining the prognosis of several diseases, especially pneumonia. [14] Probably because of the small sample size, there was no significant difference in any comorbidity including hypertension, diabetes, COPD, cancer, and others between the two groups. The potential impact of comorbidities on the disease outcomes of patients with COVID-19 pneumonia requires further observation and research.…”

A disease-specific comorbidity index for predicting mortality in patients admitted to hospital with a cardiac condition

“…Previous examples can be found in gastroenterology and intensive care medicine. 10,29 However, despite the huge effect of cardiac diseases on society, the potential of health administrative databases to risk-stratify patient cohorts in this context has received little attention.…”