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Risk prediction models for survival after heart transplantation: A systematic review.
American Journal of Transplantation ( IF 8.8 ) Pub Date : 2019-12-16 , DOI: 10.1111/ajt.15708
Natasha Aleksova 1 , Ana C Alba 1 , Victoria M Molinero 1 , Katherine Connolly 2 , Ani Orchanian-Cheff 3 , Mitesh Badiwala 1 , Heather J Ross 1 , Juan G Duero Posada 1
Affiliation  

Risk prediction scores have been developed to predict survival following heart transplantation (HT). Our objective was to systematically review the model characteristics and performance for all available scores that predict survival after HT. Ovid Medline and Epub Ahead of Print and In-Process & Other Non-Indexed Citations, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Clinical Trials were searched to December 2018. Eligible articles reported a score to predict mortality following HT. Of the 5392 studies screened, 21 studies were included that derived and/or validated 16 scores. Seven (44%) scores were validated in external cohorts and 8 (50%) assessed model performance. Overall model discrimination ranged from poor to moderate (C-statistic/area under the receiver operating characteristics 0.54-0.77). The IMPACT score was the most widely validated, was well calibrated in two large registries, and was best at discriminating 3-month survival (C-statistic 0.76). Most scores did not perform particularly well in any cohort in which they were assessed. This review shows that there are insufficient data to recommend the use of one model over the others for prediction of post-HT outcomes.

中文翻译:

心脏移植后生存的风险预测模型:系统评价。

已经开发出风险预测评分来预测心脏移植 (HT) 后的存活率。我们的目标是系统地审查预测 HT 后生存的所有可用分数的模型特征和性能。Ovid Medline 和 Epub Ahead of Print and In-Process & Other Non-Indexed Citations、Ovid Embase、Cochrane 系统评价数据库和 Cochrane 对照临床试验中央登记册被检索到 2018 年 12 月。符合条件的文章报告了一个分数来预测以下死亡率H T。在筛选的 5392 项研究中,有 21 项研究被纳入,得出和/或验证了 16 项评分。7 个 (44%) 分数在外部队列中得到验证,8 个 (50%) 评估了模型性能。总体模型辨别力从差到中等(C 统计量/接受者操作特征下的面积 0. 54-0.77)。IMPACT 评分得到了最广泛的验证,在两个大型登记处得到了很好的校准,并且最擅长区分 3 个月的生存率(C 统计量 0.76)。大多数分数在评估它们的任何队列中都没有表现得特别好。这篇综述表明,没有足够的数据来推荐使用一种模型而不是其他模型来预测 HT 后结果。
更新日期:2019-12-16
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