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Predicting in-hospital mortality in patients undergoing complex gastrointestinal surgery: determining the optimal risk adjustment method.

OBJECTIVE
To compare the performance of Charlson/Deyo, Elixhauser, Disease Staging, and All Patient Refined Diagnosis-Related Groups (APR-DRGs) algorithms for predicting in-hospital mortality after 3 types of major abdominal surgeries: gastric, hepatic, and pancreatic resections.


DESIGN
Cross-sectional nationwide sample.


SETTING
Nationwide Inpatient Sample from 2002 to 2007.


PATIENTS
Adult patients (≥18 years) hospitalized with a primary or secondary procedure of gastric, hepatic, or pancreatic resection between 2002 and 2007.


MAIN OUTCOME MEASURES
Predicting in-hospital mortality using the 4 comorbidity algorithms. Logistic regression analyses were used and C statistics were calculated to assess the performance of the indexes. Risk adjustment methods were then compared.


RESULTS
In our study, we identified 46,395 gastric resections, 18,234 hepatic resections, and 15,443 pancreatic resections. Predicted in-hospital mortality rates according to the adjustment methods agreed for 43.8% to 74.6% of patients. In all types of resections, the APR-DRGs and Disease Staging algorithms predicted in-hospital mortality better than the Charlson/Deyo and Elixhauser indexes (P < .001). Compared with the Charlson/Deyo algorithm, the Elixhauser index was of higher accuracy in gastric resections (0.847 vs 0.792), hepatic resections (0.810 vs 0.757), and pancreatic resections (0.811 vs 0.741) (P < .001 for all comparisons). Higher accuracy of the Elixhauser algorithm compared with the Charlson/Deyo algorithm was not affected by diagnosis rank, multiple surgeries, or exclusion of transplant patients.


CONCLUSIONS
Different comorbidity algorithms were validated in the surgical setting. The Disease Staging and APR-DRGs algorithms were highly accurate. For commonly used algorithms such as Charlson/Deyo and Elixhauser, the latter showed higher accuracy.