LAUSR

6612 Background: Population-based data on the implementation of guidelines for cancer patients in daily practice are scarce. Therefore, we evaluated practice variation patterns and associated variables in the systemic treatment of metastatic colorectal cancer (mCRC) between 2008 and 2015 in the Netherlands. Methods: We selected a random sample of adult mCRC patients diagnosed from 2008 to 2015 from the National Cancer Registry in 20 Dutch hospitals. We examined the influence of patient, demographic and tumor characteristics on the odds of being treated with systemic therapy according to the current guideline and assessed its association with survival. Results: Our study population consisted of 2222 mCRC patients of whom 1307 patients received systemic therapy for mCRC. Practice variation was most obvious in the use of bevacizumab and anti-EGFR therapy in patients with (K)RAS wild-type tumors. Administration rates did not differ between hospital types but fluctuated between individual hospitals for bevacizumab (8-92%; p<0·0001) and anti-EGFR therapy (10-75%; p=0·05). Bevacizumab administration was inversely correlated to higher age (OR:0·2; 95% CI: 0·1-0·3) comorbidity (OR:0·6; 95% CI: 0·5-0·8) and the presence of metachronous metastases (OR:0·5; 95% CI: 0·3-0·7), but patient characteristics did not differ between hospitals with low or high bevacizumab administration rates. Exposure to bevacizumab (HR:0·8; 95% CI: 0·7-0·9) and anti-EGFR therapy (HR:0·6; 95% CI: 0·5-0·8) was associated with prolonged survival. Conclusions: We identified significant inter-hospital variation in targeted therapy administration for mCRC patients, which may affect outcome. Age and comorbidity were inversely correlated with non-administration of bevacizumab, but did not explain inter-hospital practice variation. Our data strongly indicate that practice variation is based on individual strategy of hospitals rather than guideline recommendations or patient-driven decisions. Individual hospital strategies are an additional factor that may explain the observed differences between real-life data and results obtained from clinical trials.