Extrapolation methods are commonly used to model the cost-effectiveness of health technologies beyond observed data. Reassessing cost-effectiveness estimates using updated clinical trial data has the potential to reduce uncertainty and optimize decision-making. We present a case study based on percutaneous repair (PR) with the Mitraclip system, a technology to treat severe secondary mitral regurgitation (MR). For the study purpose, we considered the COAPT trial that evaluated the effectiveness of adding PR to medical treatment versus medical treatment alone.

We developed a time-varying Markov model to assess the cost-effectiveness of PR. Clinical inputs were based on reconstructed individual patient data from the COAPT trial results reported at 2 years, and at 3 years.

We developed parametric modeling for overall survival (OS) and heart failure hospitalizations (HFH) to obtain clinically plausible extrapolations beyond observed data. We adopted the French perspective and used a 30-year time horizon. We expressed incremental cost-effectiveness ratios (ICERs) as cost per quality-adjusted life year (QALY).

Based on 2 year-data, preferred parametric models for OS and HFH were exponential and log-logistic respectively, yielding an ICER of EUR21,918/QALY and >0.5 probability of PR being cost-effective (EUR50,000/QALY threshold).

Updated analyses at 3 years showed a change of OS trajectory for PR that justified the use of piecewise modelling, yielding an updated ICER that went up to EUR77,904/QALY (base-case), and to a minimum of EUR58,175/QALY (scenario analysis). Using data at 3 years, PR had <0.5 probability of being cost-effective.

In this case study, the availability of updated survival analyses of the main trial is likely to have some impact on decision-making and/or pricing discussion as part of health-technology assessment (HTA). We aim to provide further updated analyses as 4 years results of the COAPT study become available.

More broadly, original technology appraisals are frequently undertaken when mid/long-term follow-up trial data may be lacking. Our example suggests the need for continuous HTA review as new clinical data are released.