Prediction of absolute risk of acute graft-versus-host disease following hematopoietic cell transplantation
Spellman, Stephen R.
Hsu, Katharine C.
Lee, Stephanie J.
MetadataShow full item record
CitationLee, Catherine, Sebastien Haneuse, Hai-Lin Wang, Sherri Rose, Stephen R. Spellman, Michael Verneris, Katharine C. Hsu, Katharina Fleischhauer, Stephanie J. Lee, and Reza Abdi. 2018. “Prediction of absolute risk of acute graft-versus-host disease following hematopoietic cell transplantation.” PLoS ONE 13 (1): e0190610. doi:10.1371/journal.pone.0190610. http://dx.doi.org/10.1371/journal.pone.0190610.
AbstractAllogeneic hematopoietic cell transplantation (HCT) is the treatment of choice for a variety of hematologic malignancies and disorders. Unfortunately, acute graft-versus-host disease (GVHD) is a frequent complication of HCT. While substantial research has identified clinical, genetic and proteomic risk factors for acute GVHD, few studies have sought to develop risk prediction tools that quantify absolute risk. Such tools would be useful for: optimizing donor selection; guiding GVHD prophylaxis, post-transplant treatment and monitoring strategies; and, recruitment of patients into clinical trials. Using data on 9,651 patients who underwent first allogeneic HLA-identical sibling or unrelated donor HCT between 01/1999-12/2011 for treatment of a hematologic malignancy, we developed and evaluated a suite of risk prediction tools for: (i) acute GVHD within 100 days post-transplant and (ii) a composite endpoint of acute GVHD or death within 100 days post-transplant. We considered two sets of inputs: (i) clinical factors that are typically readily-available, included as main effects; and, (ii) main effects combined with a selection of a priori specified two-way interactions. To build the prediction tools we used the super learner, a recently developed ensemble learning statistical framework that combines results from multiple other algorithms/methods to construct a single, optimal prediction tool. Across the final super learner prediction tools, the area-under-the curve (AUC) ranged from 0.613–0.640. Improving the performance of risk prediction tools will likely require extension beyond clinical factors to include biological variables such as genetic and proteomic biomarkers, although the measurement of these factors may currently not be practical in standard clinical settings.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:34868987