Intensive Care Unit, Reina Sofia University Hospital, Cordoba, Spain.
Department of Computer Science and Numerical Analysis, Universidad de Córdoba, Córdoba, Spain.
PLoS One. 2021 Jun 4;16(6):e0252148. doi: 10.1371/journal.pone.0252148. eCollection 2021.
One of the main problems of lung transplantation is the shortage of organs as well as reduced survival rates. In the absence of an international standardized model for lung donor-recipient allocation, we set out to develop such a model based on the characteristics of past experiences with lung donors and recipients with the aim of improving the outcomes of the entire transplantation process.
This was a retrospective analysis of 404 lung transplants carried out at the Reina Sofía University Hospital (Córdoba, Spain) over 23 years. We analyzed various clinical variables obtained via our experience of clinical practice in the donation and transplantation process. These were used to create various classification models, including classical statistical methods and also incorporating newer machine-learning approaches.
The proposed model represents a powerful tool for donor-recipient matching, which in this current work, exceeded the capacity of classical statistical methods. The variables that predicted an increase in the probability of survival were: higher pre-transplant and post-transplant functional vital capacity (FVC), lower pre-transplant carbon dioxide (PCO2) pressure, lower donor mechanical ventilation, and shorter ischemia time. The variables that negatively influenced transplant survival were low forced expiratory volume in the first second (FEV1) pre-transplant, lower arterial oxygen pressure (PaO2)/fraction of inspired oxygen (FiO2) ratio, bilobar transplant, elderly recipient and donor, donor-recipient graft disproportion requiring a surgical reduction (Tailor), type of combined transplant, need for cardiopulmonary bypass during the surgery, death of the donor due to head trauma, hospitalization status before surgery, and female and male recipient donor sex.
These results show the difficulty of the problem which required the introduction of other variables into the analysis. The combination of classical statistical methods and machine learning can support decision-making about the compatibility between donors and recipients. This helps to facilitate reliable prediction and to optimize the grafts for transplantation, thereby improving the transplanted patient survival rate.
肺移植的主要问题之一是器官短缺和存活率降低。由于缺乏国际标准化的肺供体-受者分配模型，我们旨在根据过去肺供体和受者的经验特点制定这样一个模型，以改善整个移植过程的结果。
这是对雷纳索非亚大学医院（西班牙科尔多瓦） 23 年来进行的 404 例肺移植的回顾性分析。我们分析了通过在捐赠和移植过程中的临床实践经验获得的各种临床变量。这些变量被用于创建各种分类模型，包括经典的统计方法和新的机器学习方法。
所提出的模型代表了一种强大的供体-受者匹配工具，在本研究中，它超越了经典统计方法的能力。预测存活率增加的变量包括：较高的移植前和移植后功能肺活量（FVC）、较低的移植前二氧化碳（PCO2）压力、较低的供体机械通气和较短的缺血时间。对移植存活有负面影响的变量包括：移植前较低的用力呼气量（FEV1）、较低的动脉氧分压（PaO2）/吸入氧分数（FiO2）比值、双肺移植、老年受者和供者、供者-受者移植物不成比例需要手术缩小（Tailor）、联合移植类型、手术中需要体外循环、供体因头部创伤死亡、手术前住院状态以及受者和供者的性别。
这些结果表明该问题具有难度，需要在分析中引入其他变量。经典统计方法和机器学习的结合可以支持供者和受者之间的兼容性决策。这有助于进行可靠的预测，并优化移植用移植物，从而提高移植患者的存活率。
