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Risk scores for prediction of 30‐day mortality after transcatheter aortic valve implantation: Results from a two‐center study in Norway

机译:经沟管主动脉瓣植入后30天死亡率预测的风险分数:在挪威的双中心研究结果

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Objectives Transcatheter aortic valve implantation (TAVI)‐specific risk scores have been developed based on large registry studies. Our aim was to evaluate how both surgical and novel TAVI risk scores performed in predicting all cause 30‐day mortality. In addition, we wanted to explore the validity of our own previously developed model in a separate and more recent cohort. Methods The derivation cohort included patients not eligible for open surgery treated with TAVI at the University Hospital of North Norway (UNN) and Oslo University Hospital (OUS) from February 2010 through June 2013. From this cohort, a logistic prediction model (UNN/OUS) for all cause 30‐day mortality was developed. The validation cohort consisted of patients not included in the derivation cohort and treated with TAVI at UNN between June 2010 and April 2017. EuroSCORE, Logistic EuroSCORE, EurosSCORE 2, STS score, German AV score, OBSERVANT score, IRRMA score, and FRANCE‐2 score were calculated for both cohorts. The discriminative accuracy of each score, including our model, was evaluated by receiver operating characteristic (ROC) analysis and compared using DeLong test where P ?.05 was considered statistically significant. Results The derivation cohort consisted of 218 and the validation cohort of 241 patients. Our model showed statistically significant better accuracy than all other scores in the derivation cohort. In the validation cohort, the FRANCE‐2 had a significantly higher predictive accuracy compared to all scores except the IRRMA and STS score. Our model showed similar results. Conclusion Existing risk scores have shown limited accuracy in predicting early mortality after TAVI. Our results indicate that TAVI‐specific risk scores might be useful when evaluating patients for TAVI.
机译:目标经变形管主动脉瓣植入(Tavi) - 基于大型登记研究的特价风险分数已经开发。我们的目标是评估在预测所有导致30天死亡率的预测中进行外科和新的Tavi风险评分。此外,我们希望在一个单独的和更新的队列中探索自己以前开发的模型的有效性。方法衍生群组包括2010年6月至2013年6月在2013年6月至2013年6月在2013年6月的北挪威大学(UNN)和奥斯陆大学医院(OUS)在北挪威大学(UNN)和奥斯陆大学医院(OUS)治疗的患者。来自这一伙伴,一种物流预测模型(UNN / OUS )对于所有原因,发达了30天的死亡率。验证队列由衍生队队的患者组成,并在2010年6月至2017年6月间在UNN的Tavi治疗。Euroscore,Logistic Euroscore,Eurosscore 2,STS得分,德国AV得分,观察分数,Irrma得分和法国2为两个队列计算得分。通过接收器操作特征(ROC)分析评估每个分数,包括我们的模型的每个分数的辨别准确性,并使用DELONG测试进行比较,其中P <。05被认为是统计学意义。结果衍生队队伍由218和241名患者的验证队列组成。我们的模型显示出统计上的显着更好的准确性,而是衍生队列中的所有其他分数。在验证队列中,与除IFRMA和STS分数之外的所有分数相比,法国-2的预测准确性明显更高。我们的模型显示出类似的结果。结论现有的风险评分在Tavi后预测早期死亡率有限。我们的结果表明,在评估TAVI患者时,Tavi特定风险评分可能很有用。

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