Validity of a model using routinely collected data for identifying infections following gastric, colon, and liver cancer surgeries.

PURPOSE

Validating outcome measures is a prerequisite for using administrative databases for comparative effectiveness research. Although the Japanese Diagnosis Procedure Combination database is widely used in surgical studies, the outcome measure for postsurgical infection has not been validated. We developed a model to identify postsurgical infections using the routinely collected Diagnosis Procedure Combination data.

METHODS

We retrospectively identified inpatients who underwent surgery for gastric, colon, or liver cancer between April 2016 and March 2018 at four hospitals. Chart reviews were conducted to identify postsurgical infections. We used bootstrap analysis with backwards variable elimination to select independent variables from routinely collected diagnosis and procedure data. Selected variables were used to create a score predicting the chart review-identified infections, and the performance of the score was tested.

RESULTS

Among the 746 eligible patients, 96 patients (13%) had postoperative infections. Three variables were identified as predictors: diagnosis of infectious disease recorded as a complication arising after admission, addition of an intravenous antibiotic, and bacterial microscopy or culture. The prediction model had a C-statistic of 0.885 and pseudo-R of 0.358. A cut-off of one point of the score showed a sensitivity of 92% and specificity of 72%, and a cut-off of two points showed a sensitivity of 75% and specificity of 91%.

CONCLUSIONS

Our model using routinely collected administrative data accurately identified postoperative infections. Further external validation would lead to the application of the model for research using administrative databases.