Using big data to develop and validate clinical prediction models for survival outcomes in kidney transplant

使用大数据开发和验证肾移植生存结果的临床预测模型

• 批准号: 2432390
• 金额: --
• 依托单位: Plymouth University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2432390
• 关键词: Using; big; data; develop; validate

Kidney transplantation is the organ transplant of a kidney to a patient with end-stage kidney disease. When a donor kidney is offered to a waitlisted patient, the clinical team responsible for the care of the potential recipient must make the decision to accept or decline the offer based upon complex and variable information about the donor, the recipient and the process of transplantation. Predicting graft and patient survival following transplantation is important to support this decision-making process. While research has been conducted to predict graft failure following kidney transplantation, they did not focus on patient survival and were based on a limited set of variables. There is a clinical need to develop new statistical methods using big data to better predict graft and patient survival in transplant recipients.This project brings the opportunity to seek to use the linked registry data from national databases to develop and validate clinical prediction models for survival outcomes. The project will aim to integrate data from multiple sources, develop models to predict risks of graft failure and death over time, and conduct an internal and external validation of the developed prediction models.

肾移植是将肾脏移植给终末期肾病患者的器官移植。当一个捐赠肾被提供给一个等待名单的病人时，负责照顾潜在接受者的临床团队必须根据关于捐赠者、接受者和移植过程的复杂和可变的信息做出接受或拒绝这个提议的决定。预测移植物和移植后患者的生存对于支持这一决策过程非常重要。虽然已经进行了预测肾移植后移植物衰竭的研究，但它们并没有关注患者的生存，而是基于一组有限的变量。临床需要利用大数据开发新的统计方法，以更好地预测移植受者的移植和患者生存。该项目为寻求使用来自国家数据库的链接注册数据来开发和验证生存结果的临床预测模型提供了机会。该项目旨在整合来自多个来源的数据，开发预测移植物衰竭和死亡风险的模型，并对开发的预测模型进行内部和外部验证。