Exploitation of Healthcare Databases in Anesthesiology and Surgical Care for Comparing Comorbidity Indexes in Cholecystectomized Patients

Objective: Charlson comorbidity index (CCI) and Elixhauser comorbidity index (ECI) have been used as prognostic tools in surgical and medical research. We compared their ability to predict in-hospital mortality among cholecystectomized patients using a Spanish large database of 87 hospitals during the period 2008-2010.

Methods: The electronic healthcare database Minimal Basic Data Set (MBDS) contain information of diseases, conditions, procedures and demographic data of patients attended in hospital setting. We used available information to calculate CCI and ECI, and analyzed their relation to in-hospital mortality.

Results: The models including age, gender, tobacco use disorders, hospital size and CCI or ECI were predictive of in-hospital mortality among cholecystectomized patients, when measured in terms of adjusted Odds Ratios and 95% confidence limits. There was a dose-effect relationship between score of prognostic indexes and risk of death. Area under the curve for ROC predictive models for in-hospital mortality were 0.8717 for CCI and 0.8771 for ECI, but differences were not statistically significant (p> 10

− 6

).

Conclusion: Both CCI and ECI were predictive of in-hospital mortality among cholecystectomized patients in a large sample of Spanish patients after controlling for age, gender, group of hospital and tobacco use disorders. The availability of more hospitals databases through Big Data can strengthen the external validity of these results if we control several threats of internal validity such as biases and missing values.