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Am J Transplant:预测肾脏移植结果的关键数据

2017-01-13 佚名 生物谷

根据一项山间医疗中心的最新研究,如果医生使用他们可以获得的所有数据去预测肾移植病人移植失败的可能性,那么这些病人存活的几率更将高。尽管国家数据库包含大量肾移植的数据,但是目前为止仍然缺少可以准确预测病人移植物失效或者病人死亡时间的模型,因为这些模型都没有考虑病人肾移植后的费用数据。 这项最新发表在《American Journal of Transplantation》上的研究强调医


根据一项山间医疗中心的最新研究,如果医生使用他们可以获得的所有数据去预测肾移植病人移植失败的可能性,那么这些病人存活的几率更将高。尽管国家数据库包含大量肾移植的数据,但是目前为止仍然缺少可以准确预测病人移植物失效或者病人死亡时间的模型,因为这些模型都没有考虑病人肾移植后的费用数据。

这项最新发表在《American Journal of Transplantation》上的研究强调医生应该使用来自国家数据库及病人病例上的纵向数据去预测肾移植结果并为病人提供个性化护理。

“如果我们可以向我们的数据库中增加综合的、移植后的病人数据,我们的预测模型可能更准确地预测出哪些病人可能在肾移植后失败或者死亡。”来自盐城山间医疗中心的肾移植和胰腺移植科主任Titte R. Srinivas博士说道:“考虑到电子病历中有大量数据,我认为我们可以改善预测模型不准确的这个现状。”

此前的预测模型准确率达60%,但据Srinivas博士所说,如果综合利用实验记录、肾功能、生命特征及排异病理等信息,预测的准确率可以提高到85%。

通过利用南卡罗来纳医科大学2007年1月到2015年6月的病例及肾移植受体的数据,研究人员共开发了四种预测模型。这些数据将模型预测病人肾移植1年后失败的准确率提高了16%,预测病人肾移植3年后死亡或者失败的准确率也有所提高。

“我们下一步的计划是与临床医生一起共享这些模型,通过这些预测模型让医生对病人可能发生的状况提前采取预防措施。”Srinivas博士说道:“通过使用这些数据及模型,医生采取的个性化护理措施将显著改善病人的预后。”

原始出处:

R. Srinivas, D. J. Taber, Z. Su, J. Zhang, G. Mour, D. Northrup, A. Tripathi, J. E. Marsden, W. P. Moran, P. D. Mauldin .Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept.American Journal of Transplantation,DOI: 10.1111/ajt.14099

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    2017-07-11 bsmagic9140
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    2017-01-30 xuyu
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    2017-01-16 虈亣靌

    文章很好,经常阅读

    0

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    2017-01-14 yafeiliutjh

    科学探索

    0

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