An acute graft-versus-host disease activity index to predict survival after hematopoietic cell transplantation with myeloablative conditioning regimens
An acute graft-versus-host disease activity index to predict survival after hematopoietic cell transplantation with myeloablative conditioning regimens
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DOI：
10.1182/blood-2006-01-0254
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发表时间：
2006-07-15
期刊：
影响因子：
20.3
通讯作者：
McDonald, George B.
中科院分区：
文献类型：
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作者：
Leisenring, Wendy M.;Martin, Paul J.;McDonald, George B.
急性移植物抗宿主病(GVHD)的分级算法在评估死亡风险方面是不准确的。我们开发了一种方法，利用386名急性移植物抗宿主病患者的数据来预测死亡率。从GVHD开始到第100天，对皮肤、肝脏和上、下胃肠道的GVHD表现进行评分，并记录免疫抑制治疗、表现和发热的数据。Logistic回归模型预测第200天的无复发死亡率(NRM)，用随机选择的193名患者的数据建立，然后在其余193名患者中进行验证。临床参数分组以优化预测准确性，测量为受试者-操作员特征(ROC)曲线下的面积。最优模型包括血清总胆红素浓度、口服摄入量、是否需要泼尼松治疗以及治疗效果评分。当使用训练和验证数据集中每个患者的平均急性GVHD活动指数(AGVHDAI)来衡量GVHD的总体负担时，ROC曲线下的面积分别为0.87和0.85。生成等高线以反映作为当前aGVHDAI评分的函数的第200天的预测NRM。这些结果表明，GVHD严重程度的临床表现可以准确地实时预测NRM的风险。
Algorithms for grading acute graft-versus-host disease (GVHD) are inaccurate in assessing mortality risk. We developed a method to predict mortality by using data from 386 patients with acute GVHD. From the onset of GVHD to day 100, GVHD manifestations were scored for the skin, liver, and upper and lower gastrointestinal tract, and data were recorded for immunosuppressive treatment, performance, and fever. Logistic regression models predicting nonrelapse mortality (NRM) at day 200 were developed with data from 193 randomly selected patients and then validated in the remaining 193 patients. Clinical parameters were grouped to optimize predictive accuracy measured as the area under a receiver-operator characteristic (ROC) curve. The optimal model included the total serum billrubin concentration, oral intake, need for treatment with prednisone, and performance score. When the overall burden of GVHD was measured by using average Acute GVHD Activity Index (aGVHDAI) scores for each patient in training and validation data sets, areas under ROC curves were 0.87 and 0.85, respectively. Contour lines were generated to reflect the predicted NRM at day 200 as a function of current aGVHDAI scores. These results demonstrate that clinical manifestations of GVHD severity can be used to accurately predict the risk of NRM in real time.