Impact of hospital geographic remoteness on short‐term outcomes after colorectal cancer resection using state‐wide administrative data. Issue 7 (26th May 2020) - Udayasiri, Dilshan K.; MacCallum, Caroline; Silva, Nigel Da; Skandarajah, Anita; Hayes, Ian P.
Abstract: Background: This study aimed to use validated coding algorithms, applied to a central repository of administrative data (AD), to report on short‐term outcomes following resection for colorectal cancer (CRC) comparing regional to metropolitan Victorian hospitals. Methods: This is a retrospective cohort study using prospectively gathered AD. The primary outcome was prolonged length of stay (LOS). Secondary outcomes were: inpatient mortality, return to theatre, discharge destination and need for mechanical ventilation/intensive care unit support. Outcomes were adjusted for potential confounders via multivariable logistic regression analysis. Results: This study of 18 470 patients found strong evidence for lower odds of prolonged LOS (odds ratio (OR) 0.53, 95% confidence interval (CI) 0.48–0.58, P  ≤ 0.001) and inpatient mortality (OR 0.67, 95% CI 0.49–0.91, P = 0.01) in inner regional hospital compared with metropolitan hospitals. For outer regional hospitals, there was strong evidence of decreased odds of prolonged LOS (OR 0.64, 95% CI 0.52–0.77, P = <0.001) and return to theatre (OR 0.67, 95% CI 0.47–0.95, P = 0.03). Conclusion: This is the largest and most detailed study concerning short‐term outcomes following CRC resection in Victorian public hospitals. Inner and outer regional centres had similar or better short‐term outcomes than metropolitan hospitals after CRC resection. AD with validated algorithms serves as a large accurate database to report on CRC outcomes. Abstract : This study uses administrative data to report short‐term outcomes after colorectal resection in regional hospitals to metropolitan hospital over a 10‐year period in Victoria. It uses coding algorithms to improve the accuracy of administrative data and accounts for biases with a multivariable logistic regression analysis.