Department of Mathematics, Khalifa University, Abu Dhabi, United Arab Emirates.
School of Medicine, Khalifa University, Abu Dhabi, United Arab Emirates.
Sci Rep. 2022 Nov 30;12(1):20651. doi: 10.1038/s41598-022-24351-x.
A life-saving treatment, solid organ transplantation (SOT) has transformed the survival and quality of life of patients with end-organ dysfunction. The coronavirus disease (COVID-19) pandemic has impacted the practice of deceased and living donations worldwide by various resource shifting, including healthcare personnel and equipment such as ventilators and bed space. Our work explores the COVID-19 pandemic and global transplant data to create a statistical model for deducing the impact of COVID-19 on living donor and deceased donor transplants in the United States of America (USA). In severely impacted regions, transplant centers need to carefully balance the risks and benefits of performing a transplant during the COVID-19 pandemic. In our statistical model, the COVID cases are used as an explanatory variable (input) to living or deceased donor transplants (output). The model is shown to be statistically accurate for both estimation of the correlation structure, and prediction of future donors. The provided predictions are to be taken as probabilistic assertions, so that for each instant where the prediction is calculated, a statistical measure of accuracy (confidence interval) is provided. The method is tested on both low and high frequency data, that notoriously exhibit a different behavior.
一种救生治疗方法，实体器官移植（SOT）改变了终末期器官功能障碍患者的生存和生活质量。冠状病毒病（COVID-19）大流行通过各种资源转移，包括医护人员和呼吸机以及床位等设备，影响了全球的已故和活体捐赠实践。我们的工作探讨了 COVID-19 大流行和全球移植数据，以创建一个统计模型，推断 COVID-19 对美国活体供体和已故供体移植的影响。在受严重影响的地区，移植中心需要仔细权衡在 COVID-19 大流行期间进行移植的风险和收益。在我们的统计模型中，COVID 病例用作活体或已故供体移植的解释变量（输入）（输出）。该模型在估计相关结构和预测未来供体方面都具有统计学准确性。所提供的预测应被视为概率性断言，因此，对于计算预测的每个时刻，都会提供准确性的统计度量（置信区间）。该方法在低频和高频数据上均经过测试，这些数据的行为明显不同。
