Chemotherapy use near end of life (EOL): Measuring real world benchmarks.

Abstract

228 Background: Use of anti-cancer therapies in the last 14-30 days of life may worsen patient outcomes and increase cost; accordingly, rate of chemotherapy use near EOL is an important quality measure. Contemporary benchmarks are needed, with transparent methods describing the cohort in which the measure is assessed, and criteria for calculation. Methods: Data on chemotherapy use, mortality, and cancer diagnosis was sourced from electronic health records (EHRs) of >8,000 patients seen between 2014-2016 at two large US academic centers, for whom dates of death were available. Death dates were sourced from the EHR and public records (e.g., obituaries). Patients were grouped by diagnosis using ICD-10 codes. Rates of infusional chemotherapy receipt within 14 or 30 days of death were calculated. Results: Across 10 tumor types, 3-7% of patients received chemotherapy within 14d of death, and 6-16% received it within 30d. Rates were stable from 2014-2016 and did not differ by cancer center. Rates were highest in diseases where patients may experience rapid clinical decline near EOL: in pancreatic and rectal cancer, 30d rates were 16% and 13%. The 30d rate was lowest (6%) in kidney cancer. When the cohort was restricted to only treated patients (who received >=5 chemotherapy administrations at the center), rates of chemotherapy use at EOL increased to 6-12% (14d) and 17-28% (30d). Conclusions: This study provides baseline estimates of current rates of EOL chemotherapy use at academic centers. Transparency in methodology is critical; for example, when the whole population of cancer patients seen at a center is considered, rates are low, but when the analysis is limited to patients who received chemotherapy there, rates nearly double. Further studies should focus on whether this quality measure is a meaningful driver of patient and health system outcomes. This work also demonstrates that it is possible to assess this metric across multiple centers; this approach could be easily scaled to all oncology practices integrated in a data sharing network. [Table: see text]