Performance of a new clinical grading system for chronic graft-versus-host disease: a multicenter study
- PMID: 12714524
- DOI: 10.1182/blood-2002-10-3141
Performance of a new clinical grading system for chronic graft-versus-host disease: a multicenter study
Abstract
We recently reported 3 risk factors (RFs) at diagnosis of chronic graft-versus-host disease (cGVHD) that were significantly associated with increased nonrelapse mortality. These included extensive skin involvement (ESI), thrombocytopenia (TP), and progressive type of onset (PTO). The hazard ratio (HR) for mortality of the patients with prognostic score (PS) between 0 and 2 (intermediate-risk; 1 RF) compared to those with PS 0 (favorable-risk; 0 RF) was 3.7 (95% CI, 1.4, 9.3); the HR for patients with PS equal to or more than 2 (high-risk; > 1 RF) compared with intermediate-risk group was 6.9 (3.8, 12.4). A rare presentation of TP and PTO without ESI yielded a PS of 1.8 (intermediate-risk). This paper reports the performance of the prognostic model and the individual RFs using data from an additional 1105 patients from University of Nebraska (n = 60), International Bone Marrow Transplantation Registry (n = 708), Fred Hutchinson Cancer Research Center (n = 188), and University of Minnesota (n = 149). The extent of skin involvement was quantified in 3 cohorts using the available data collected in different formats before the analysis. Although the HR for mortality of the patients in the intermediate-risk group versus those in the favorable-risk group ranged from 2.3 to 8.9 across the centers, it was between 1.6 to 6.9 for patients in the high-risk group versus those in the intermediate-risk group. Although TP itself was uniformly associated with increased risk of mortality across all test samples, ESI and PTO showed statistically significant associations with mortality in 1 and 2 cohorts, respectively. In conclusion, the model was predictive of cGVHD-specific survival, but the mortality hazard associated with ESI was lower in each of these test samples compared with the learning sample. Although the new clinical grading based on the model is promising because of its utility across multiple independent data sets, prospective validation is needed.
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