Prognosis of Patients With Heart Failure Receiving Autologous Myoblast Patches - Comparison of Single-Arm Trial Data to Registry Data

Circ J. 2023 Mar 24;87(4):481-486. doi: 10.1253/circj.CJ-22-0319. Epub 2022 Nov 15.

Abstract

Background: Clinical studies in regenerative medicine remain insufficient in Japan due to ethical concerns regarding the control group and a lack of statistical methodology to evaluate efficacy in a small treatment group. This study evaluated the efficacy of autologous myoblast patch (AMP) treatment for heart failure using restricted mean survival time (RMST) analysis by comparing data from a small single-arm trial to epidemiological data from a registry.Methods and Results: The clinical trial arm included 55 patients with advanced ischemic cardiomyopathy who received an AMP between 2010 and 2020. The registry-based control group comprised 937 participants with severely impaired left ventricular function who were hospitalized for heart failure during the study period. Due to the limited number of patients, RMST analysis was used to compare survival between the 2 groups. Cox regression analyses revealed non-significant differences in survival between the groups at 3, 3.5, and 4 years. In contrast, RMST analyses revealed significant differences in survival at 3 years (P=0.008) and 3.5 (P=0.024) years, but not at 4 years.

Conclusions: This small single-arm trial using RMST analyses was able to detect the efficacy of AMP transplantation for advanced heart failure (compared with a registry-based control group), with better survival until 3.5 years. This approach may be useful for efficacy analyses in regenerative medicine, where traditional clinical trials are difficult.

Keywords: Autologous myoblast patch; Clinical trial; Regenerative medicine; Restricted mean survival time analysis; Single-arm trial.

Publication types

  • Comparative Study
  • Evaluation Study

MeSH terms

  • Heart Failure* / therapy
  • Humans
  • Myoblasts
  • Myocardial Ischemia*
  • Prognosis
  • Routinely Collected Health Data