Use of administrative data to create a colorectal cancer database

Abstract

Background: Research into Colorectal cancer (CRC) require maintenance of clinical cancer databases with complex datasets. These are resource intensive, region specific, and compromised by reporting bias [1]. Administrative data are routinely captured for each hospital admission and may serve as an alternative source for populating databases. However, the accuracy of administrative data has not been fully explored and may vary by data item. The aims of this study included identifying a cohort of new CRC patients from administrative data, measuring its accuracy, and deriving coding algorithms to improve the accuracy of diagnoses, procedures and short-term outcomes.

There has been much debate that major surgery, in particular for cancer patients, should be concentrated in tertiary centres, based on the premise that high volume centres achieve better outcomes. In this study, we investigated two hypotheses: that the majority of complex colorectal cancer resections are performed in major city hospitals and that the short-term outcomes are better in CSSANZ (Colorectal Surgical Society of Australia and New Zealand) hospitals.

Large Inpatient administrative databases are a common source used to identify comorbidities recorded with International Classification of Disease (ICD) diagnostic codes. These data sources may be used to assess the effect of baseline comorbidity status on surgical care outcomes. In this study, we hypothesized that the ASA PS (American Society of Anaesthesiologists physical status) classification can predict short-term outcomes after a colorectal cancer resection when compared to the Elixhauser comorbidity index (ECI).

Methods: A retrospective study was conducted to identify all new colorectal cancer resections at The Royal Melbourne Hospital from 1st of January 2008 to 31st of December 2013, using administrative data. Code combinations and algorithms were used to improve the accuracy of administrative data.

These algorithms were utilized to identify an accurate cohort of colorectal cancer resection cases from the Victorian Admitted Episodes Dataset (VAED), between July 2008 to June 2013. The short-term outcomes and workloads were compared in public hospitals across the state of Victoria.

The algorithms constructed were also utilised to identify an accurate cohort of CRC resection cases from Dr Foster Global Comparators Victorian dataset. ASA PS classification scores were identified from these cases. Multiple linear regression models were constructed to study the association between comorbidity indices and short-term outcomes.

Results: It is possible to use administrative data to identify new colorectal cancer patients who have had a surgical resection, using specific coding algorithms. Administrative data has an accuracy of 80-100% for most data fields, and this accuracy can be improved using coding algorithms.

An accurate cohort of colorectal cancer resection cases was identified from the VAED dataset. Seventy-three percent of CRC resections in the state were performed in metropolitan city hospitals. There was no significant difference in LOS (length of stay), mortality and reoperation rates between CSSANZ and non-CSSANZ hospitals. This study demonstrates that administrative data is both cost-effective and informative.

The ASA PS model was indeed shown to be a strong predictor of the primary outcome: length of stay (LOS). The significant predictors of LOS were emergency operations, rectal cancer resections, ASA3 and patients age. The Elixhauser model was a better predictor than the ASA PS model. However, the full model adjusted for both the ECI and ASA PS grade was the best predictor of outcome. The study indeed showed the ability of the ASA PS classification to identify short-term clinical outcomes.

Conclusion: These studies make the possibility of a Victorian CRC registry containing all surgical CRC patients a real possibility. Such a registry would enable outcomes research across the whole state with the possibility of data linkage to international administrative data sets.