Improving quality of care and reducing costs with angiogenesis-specific imaging tests in metastatic breast cancer.

Abstract

150 Background: Anti-angiogenesis (AA) drugs (e.g., bevacizumab) are expensive and their clinical benefit in metastatic breast cancer (MBC) has been challenged. Healthcare reform and financial pressures prioritize programs which improve patient quality of care and reduce costs of unnecessary care. Angiogenesis-specific imaging tests (A-IT) under development have potential to offer earlier, accurate determination of response. For A-IT-identified responders, AA treatment would be continued. For patients identified as non-responders, futile AA treatment and associated toxicities can be avoided and alternative therapies initiated. Methods: A decision-tree model was developed to estimate the impact of A-IT from determination of AA therapy eligibility through to disease progression. Key decision nodes were presence/absence of A-IT (assessing change in biomarker expression across 2 PET/CT scans: at AA eligibility, then after one cycle of AA), A-IT sensitivity/specificity (SE/SP) and clinician adherence to test results (tied to belief that results are valid enough to stop AA therapy). Key model inputs (and base case values): 1) median time to progression (TTP) for current MBC patients on AA therapies (9.5 months); 2) median TTP for A-IT identified responders (13 months); 3) costs of bevacizumab, one cycle ($5,200); 4) percentage of AA patients with hemorrhage (4%); 5) costs of hemorrhaging, per event ($14,694); 6) per patient costs for A-IT ($6,000); 7) estimated SE/SP of A-IT – 95%/75%; and 8) clinician adherence to test results (75%). Results: Based on a cohort of 100 MBC patients, use of A-IT results in 29 patients avoiding futile AA therapy with a saving of $460,000, versus a scenario where A-IT was not used. One-way threshold sensitivity analysis shows A-IT is cost-saving if SP >62% or when clinician adherence is ≥63%; results are not sensitive to AA hemorrhage rate. Conclusions: Use of A-IT could improve quality of care by optimizing AA therapy, i.e., by identifying responders who will experience survival benefit and non-responders who can avoid futile therapy and toxicity risks. Significant cost savings may be possible as a result of early determination of response to AA drugs.