TRIAL OF A PREDICTIVE ALGORITHM TO TRANSPLANT PRA PAT

因先天感染而对人类白细胞抗原变得敏感的患者 移植和排斥、输血或怀孕经历 等待时间更长，才能找到没有反应的捐赠者。减少 与非致敏受者相比，移植物存活率也有所下降。 观察到的。伴随着这种延迟和结果的是增加的物理 和心理障碍。 我们开发了一种计算机化预测算法(KATS)，它基于 在确定可接受与不可接受的私人和 公共人类白细胞抗原来源于每月的血清筛查结果。 本质上，该算法预测I类的选择性不匹配 高度致敏患者体内的抗原。回溯性研究有 验证了该算法的预测能力。在首字母中 回顾分析，每个参与中心利用他们的 自身患者小组检测6份相同的血清，共292个细胞 经检测，只有4个细胞的抗原与血清相同。 捐赠者。然而，利用预测，215个交叉匹配否定非 经鉴定为人类白细胞抗原I类。在一项多中心的回顾性研究中， KATS计划预测最终交叉匹配率为95.3%，并成功 移植成功率为85.4%。在另一个多中心 回顾分析，预测移植成功的准确性 在高度致敏的患者中也被预测到。相同算法可以 用于预测难于随机选择的患者的相容供体 血小板输注。 我们现在建议改进对负交叉匹配的预测，并 通过该算法提高基于前瞻性预测的器官共享 从而缩短了等待时间，提高了上座率 在那些自愿强制分享肾脏的中心进行移植 根据Kats的预测和改进的结果。这些结果 将与同等敏化分层的对照组进行比较 在这方面，中心选择不强制分享肾脏的患者 基础。

Patients that become sensitized to HLA alloantigens by virtue of prior transplantation and rejection, blood transfusions or pregnancy experience longer waiting times before a none-reactive donor can be found. Decreased graft survival as compared to unsensitized recipients has also been observed. Accompanying this delay and outcome are the increased physical and psychological morbidity. We have developed a computerized predictive algorithm (KATS) that is based upon the identification of Acceptable versus Unacceptable private and public HLA antigens derived from the results of monthly serum screening. In essence the algorithm predicts selective mismatches for class I antigens in highly sensitized patients. Retrospective studies have validated the predictive power of the algorithm. In an initial retrospective analysis, in which each participating center utilized their own patients panel to examine 6 identical serum, a total of 292 cells were tested yet only 4 of the cells were 4 antigen identical with the serum donor. Utilizing the predictions, however, 215 crossmatch negative non- HLA class I were identified. In a multi-center retrospective study, the KATS program predicted a negative final crossmatch 95.3% and a successful transplant outcome 85.4% of the time. In another multi-center retrospective analysis, the predictive accuracy of a successful transplant in highly sensitized patients was also predicted. The same algorithm may be used to predict compatible donors for patients refractory to random platelet transfusion. We now propose to improve the prediction for a negative crossmatch and increase organ sharing bases on prospective predictions by the algorithm thereby decreasing the waiting time, increasing the rate of transplantation at those centers volunteering to mandatorily share kidneys on the basis of the KATS predictions and improving outcome. These results will be compared to a control group of equally sensitized stratified patients whose centers choose not to mandatorily share kidneys on this basis.