Colin Kruse, Marc Daniel Bouchard, Danielle Dagher, Ashraf Hantouly, Antonio Parlange, Mohit Bhandari, M Zafarullah Khan
PURPOSE: To evaluate the cost-effectiveness of using allograft and xenograft augmentation at the time of primary rotator cuff repair (RCR) using a decision tree analysis. METHODS: An expected-value decision tree analysis was developed to compare the costs and outcomes of primary RCR for large tears performed with or without graft augmentation. Input parameters, including procedural costs, outcome probabilities, and health utility values, were derived from a comprehensive literature review. Health utility was quantified using European Quality of Life 5 Dimension scores to estimate quality-adjusted life years (QALYs) gained over a 10-year postoperative period. Cost-effectiveness was evaluated using the incremental cost-effectiveness ratio, defined as the additional cost per QALY gained with graft augmentation. An intervention was considered cost-effective if the incremental cost-effectiveness ratio was below the $100,000/QALY threshold, consistent with established benchmarks in the literature. RESULTS: Based on the current model, the total cost of conventional RCR was $22,183, compared with $24,074 for allograft augmentation and $25,314 for xenograft augmentation. Allograft augmentation displayed dominance over xenograft in terms of cost-effectiveness, with an incremental cost-effectiveness ratio of $18,844 versus $62,620. Both strategies remained cost-effective under the $100,000/QALY threshold. Sensitivity analyses showed cost thresholds for maintaining cost-effectiveness were $12,505 for allograft and $6225 for xenograft. Retear rate threshold analysis revealed that both augmentation types remained cost-effective if the retear rate was below 27%, representing at least an 8% absolute reduction compared with conventional RCR. CONCLUSIONS: Although associated with higher initial costs, graft augmentation during primary RCR may be a cost-effective strategy, particularly with allograft, which showed superior cost-effectiveness. LEVEL OF EVIDENCE: Level IV, economic analysis: decision tree with input from Level II and III studies.