Endoscopy International Open (Apr 2016)
Endoscopic ablation is a cost-effective cancer preventative therapy in patients with Barrett’s esophagus who have elevated genomic instability
Abstract
Background: The surveillance of patients with nondysplastic Barrett’s esophagus (NDBE) has a high cost and is of limited effectiveness in preventing esophageal adenocarcinoma (EAC). Ablation for NDBE remains expensive and controversial. Biomarkers of genomic instability have shown promise in identifying patients with NDBE at high risk for progression to EAC. Here, we evaluate the cost-effectiveness of using such biomarkers to stratify patients with NDBE by risk for EAC and, subsequently, the cost-effectiveness of ablative therapy. Methods: A Markov decision tree was used to evaluate four strategies in a hypothetical cohort of 50-year old patients with NDBE over their lifetime: strategy I, natural history without surveillance; strategy II, surveillance per current guidelines; strategy III, ablation for all patients; strategy IV, risk stratification with use of a biomarker panel to assess genomic instability (i. e., mutational load [ML]). Patients with no ML underwent minimal surveillance, patients with low ML underwent standard surveillance, and patients with high ML underwent ablation. The incremental cost-effectiveness ratio (ICER) and incremental net health benefit (INHB) were assessed. Results: Strategy IV provided the best values for quality-adjusted life years (QALYs), ICER, and INHB in comparison with strategies II and III. Results were robust in sensitivity analysis. In a Monte Carlo analysis, the relative risk for the development of cancer in the patients managed with strategy IV was decreased. Critical determinants of strategy IV cost-effectiveness were the complete response rate, cost of ablation, and surveillance interval in patients with no ML. Conclusion: The use of ML to stratify patients with NDBE by risk was the most cost-effective strategy for preventive EAC treatment. Targeting ablation toward patients with high ML presents an opportunity for a paradigm shift in the management of NDBE.