Abstract
Objectives
To compare the cost-effectiveness of collagenase injection (collagenase) and limited fasciectomy (LF) surgery in treating moderate Dupuytren’s contracture (DC) in the United Kingdom over different time horizons.
Methods
An incremental cost-effectiveness analysis was conducted alongside a multicenter, pragmatic, parallel randomized controlled trial (Dupuytren’s Interventions: Surgery versus Collagenase trial), to determine the short-term cost-effectiveness of collagenase compared with LF. A Markov decision analytic model was developed to assess long-term cost-effectiveness.
Results
Collagenase was associated with significantly lower cost and insignificantly lower quality-adjusted life-year (QALY) gain compared with LF at 1 year. The probability of collagenase being cost-effective was more than 99% at willingness-to-pay thresholds of £20 000 to £30 000 per QALY. At 2 years, collagenase was both significantly less costly and less effective compared with LF, and LF became cost-effective above a threshold of £25 488. There was a high level of uncertainty surrounding the 2-year results. Over a lifetime horizon, collagenase generated a cost saving of £2968 per patient but was associated with a mean QALY loss of −0.484. The probability of collagenase being cost-effective dropped to 22% and 16% at £20 000 to £30 000 per QALY, respectively.
Conclusions
Collagenase was less costly and less effective than LF in treating Dupuytren’s contracture. The cost-effectiveness of collagenase compared with LF was time dependent. Collagenase was highly cost-effective 1-year after treatment; however, the probability of collagenase being cost-effective declined over time. The Markov model suggested that LF is more cost-effective over a lifetime horizon. These findings emphasize the importance of longer follow-up when comparing surgical and nonsurgical interventions to fully capture overall costs and benefits.
Authors
Qi Wu Catherine Arundel Charlie Welch Puvanendran Tharmanathan Nick Johnson Belen Corbacho Joseph J. Dias