Assessment and Improvement of Elixhauser Comorbidity Index for Predicting In-hospital Mortality in Heart Transplant Patients

Abstract

Background: Heart transplant (HT) has a high in-hospital mortality of around 5%. Risk prediction in-hospital mortality can be informative for transplant candidacy and post-HT prognosis. Elixhauser Comorbidity Index (ECI) is an ICD diagnostic code-based comorbidity measurement tool that can predict in-hospital mortality. While it has been validated in the large in-patient population, the accuracy of the mortality prediction has not been assessed in HT. Methods: This study assessed the in-hospital mortality risk prediction by ECI as well as demographic variables in HT patients in the National Inpatient Sample (NIS) database. Demographic information was included in the multivariable ECI with demographics (ECID) model to assess in-hospital mortality. Moreover, ECI and age were used to develop a single index adjusted ECI (aECI) for mortality prediction. Results: Age best predicts (c-statistic = 0.673, 95% CI = 0.638-0.709) in-hospital mortality, followed by ECI (c-statistic = 0.638, 95% CI = 0.598-0.678), race (c-statistic = 0.571, 95% CI = 0.533-0.609). Sex did not have predictive power (c-statistic = 0.501, 95% CI = 0.467-0.535) for in-hospital mortality. The predictive power of ECI was improved (c-statistic = 0.753, 95% CI = 0.720-0.785) in the ECID model. The single measure aECI had comparable discriminative power (c-statistic = 0.763, 95% CI = 0.731-0.794) to ECID in predicting in-hospital mortality. Conclusion: This study showed that ECI was an effective measure to predict post-HT in-hospital mortality. The improved measure aECI can be easily derived from ECI as a quick reference to assess post-HT in-hospital mortality in both the clinic and health administration.