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The optimal hormonal replacement modality selection for multiple organ procurement from brain-dead organ donors

机译:从脑死亡器官供体采购多器官的最佳激素替代方式选择

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Abstract: The management of brain-dead organ donors is complex. The use of inotropic agents and replacement of depleted hormones (hormonal replacement therapy) is crucial for successful multiple organ procurement, yet the optimal hormonal replacement has not been identified, and the statistical adjustment to determine the best selection is not trivial. Traditional pair-wise comparisons between every pair of treatments, and multiple comparisons to all (MCA), are statistically conservative. Hsu’s multiple comparisons with the best (MCB) – adapted from the Dunnett’s multiple comparisons with control (MCC) – has been used for selecting the best treatment based on continuous variables. We selected the best hormonal replacement modality for successful multiple organ procurement using a two-step approach. First, we estimated the predicted margins by constructing generalized linear models (GLM) or generalized linear mixed models (GLMM), and then we applied the multiple comparison methods to identify the best hormonal replacement modality given that the testing of hormonal replacement modalities is independent. Based on 10-year data from the United Network for Organ Sharing (UNOS), among 16 hormonal replacement modalities, and using the 95% simultaneous confidence intervals, we found that the combination of thyroid hormone, a corticosteroid, antidiuretic hormone, and insulin was the best modality for multiple organ procurement for transplantation.
机译:摘要:脑死亡器官供体的管理很复杂。使用正性肌力药物和替代耗竭的激素(激素替代疗法)对于成功获取多器官至关重要,但尚未确定最佳的激素替代,确定最佳选择的统计调整并非易事。统计上保守的是,每对治疗之间的传统成对比较以及对所有治疗的多次比较。许氏与最佳对照(MCB)的多重比较(根据Dunnett与对照的多重对照(MCC)改编而成)已用于根据连续变量选择最佳治疗方法。我们采用两步法为成功的多器官采购选择了最佳的激素替代方式。首先,我们通过构建广义线性模型(GLM)或广义线性混合模型(GLMM)来估计预测利润,然后鉴于激素替代模式的测试是独立的,我们采用了多种比较方法来确定最佳激素替代模式。基于来自器官共享联合网络(UNOS)的10年数据,在16种激素替代方式中,并使用95%同时置信区间,我们发现甲状腺激素,皮质类固醇,抗利尿激素和胰岛素的组合采购多器官移植的最佳方式。

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