Abstract
In clinical trials of chronic graft-versus-host disease (cGVHD), the need to start a new systemic treatment is considered a treatment failure. A composite endpoint called “failure-free survival” (FFS), where events are initiation of a new systemic cGVHD treatment, recurrent malignancy, and death, has been suggested as a possible long-term indicator of success. The goal of the current study was to identify changes in cGVHD manifestations from baseline to 6 months that could accurately predict subsequent longer-term FFS, thereby making it possible to assess outcomes earlier than would otherwise be possible. We used data from 2 prospective, multicenter, observational studies to develop the cGVHD-FFS index. The cGVHD-FFS index was calculated at 6 months, a typical timepoint for assessment of the primary endpoint of phase II cGVHD trials. Subsequent FFS was only 45% within the next 2 years. We found that changes in the scores for the eyes, joint/fascia, and mouth ulcers from baseline to 6 months were associated with subsequent FFS, but the prognostic accuracy of these changes was not adequate for use in trials. Biomarker studies might help to identify criteria that improve prediction of long-term clinical outcomes in patients with cGVHD.
Original language | English (US) |
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Pages (from-to) | 2468-2473 |
Number of pages | 6 |
Journal | Biology of Blood and Marrow Transplantation |
Volume | 25 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2019 |
Bibliographical note
Funding Information:Financial disclosure: Funding provided by a grant from the National Cancer Institute , CA118953 (P.I. S. Lee)
Funding Information:
Financial disclosure: Funding provided by a grant from the National Cancer Institute, CA118953 (P.I. S. Lee), Declaration of Competing Interest: There are no conflicts of interest to report.
Publisher Copyright:
© 2019 American Society for Transplantation and Cellular Therapy
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
Keywords
- Chronic graft-versus-host disease
- Failure-free survival
- Prognostic model