%0 Journal Article
%T A Review of the Cost-Effectiveness Evidence for FDA-Approved Cell and Gene Therapies.
%A Abuloha S
%A Niu S
%A Adirika D
%A Harvey BP
%A Svensson M
%J Hum Gene Ther
%V 35
%N 11
%D 2024 Jun 9
%M 38526393
%F 4.793
%R 10.1089/hum.2023.186
%X Cell and gene therapy (CGT) innovations have provided several significant breakthroughs in recent years. However, CGTs often come with a high upfront cost, raising questions about patient access, affordability, and long-term value. This study reviewed cost-effectiveness analysis (CEA) studies that have attempted to assess the long-term value of Food and Drug Administration (FDA)-approved CGTs. Two reviewers independently searched the Tufts Medical Center CEA Registry to identify all studies for FDA-approved CGTs, per January 2023. A data extraction template was used to summarize the evidence in terms of the incremental cost-effectiveness ratio expressed as the cost per quality-adjusted life year (QALY) and essential modeling assumptions, combined with a template to extract the adherence to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. The review identified 26 CEA studies for seven CGTs. Around half of the base-case cost-effectiveness results indicated that the cost per QALY was below $100,000-$150,000, often used as a threshold for reasonable cost-effectiveness in the United States. However, the results varied substantially across studies for the same treatment, ranging from being considered very cost-effective to far from cost-effective. Most models were based on data from single-arm trials with relatively short follow-ups, and different long-term extrapolations between studies caused large differences in the modeled cost-effectiveness results. In sum, this review showed that, despite the high upfront costs, many CGTs have cost-effectiveness evidence that can support long-term value. Nonetheless, substantial uncertainty regarding long-term value exists because so much of the modeling results are driven by uncertain extrapolations beyond the clinical trial data.