Organ procurement organizations (OPOs) face increasing regulatory scrutiny, and the performance of predictive models used to assess OPO performance is critical. We sought to determine whether adding deceased donor physiological and critical care data to the existing Scientific Registry of Transplant Recipients (SRTR) heart yield model would improve the model’s performance. Donor data and heart transplanted (yes/no), the outcome of interest, were obtained from the United Network for Organ Sharing Donor Management Goal (DMG) Registry for 19 141 donors after brain death, from 25 OPOs. The data were split into training and testing portions. Multivariable LASSO regression was used to develop a statistical model incorporating DMG data elements with the existing components of the SRTR model. The DMG + SRTR and SRTR models were applied to the test data to compare the predictive performance of the models. The sensitivity (84%-86%) and specificity (84%-86%) were higher for the DMG + SRTR model compared to the SRTR model (71%-75% and 76%-77%, respectively). For the DMG + SRTR model, the C-statistic was 0.92 to 0.93 compared to 0.80 to 0.81 for the SRTR model. DMG data elements improve the predictive performance of the heart yield model. The addition of DMG data elements to the Organ Procurement and Transplantation Network data collection requirements should be considered.

中文翻译：

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使用来自供体管理目标登记队列的供体重症监护数据开发改进的移植受者已故供体心脏产量科学登记表


器官获取组织 （OPO） 面临越来越多的监管审查，用于评估 OPO 绩效的预测模型的性能至关重要。我们试图确定将已故供体的生理和重症监护数据添加到现有的移植受者科学登记处 （SRTR） 心脏产量模型是否会提高模型的性能。捐献者数据和心脏移植（是/否），即感兴趣的结果，是从器官共享捐献者管理目标 （DMG） 登记处获得的，用于 25 名 OPO 的脑死亡后 19 141 名捐献者。数据分为训练和测试部分。多变量 LASSO 回归用于开发一个统计模型，该模型将 DMG 数据元素与 SRTR 模型的现有组成部分相结合。将 DMG + SRTR 和 SRTR 模型应用于测试数据，以比较模型的预测性能。与 SRTR 模型 （分别为 71%-75% 和 76%-77%） 相比，DMG + SRTR 模型的敏感性 （84%-86%） 和特异性 （84%-86%） 更高。对于 DMG + SRTR 模型，C 统计量为 0.92 至 0.93，而 SRTR 模型的 C 统计量为 0.80 至 0.81。DMG 数据元素提高了心脏产量模型的预测性能。应考虑将 DMG 数据元素添加到器官获取和移植网络数据收集要求中。