Introduction Risk stratification based on Euroscore II (ESII) is used in some centres to assist decisions to perform transcatheter aortic valve implant (TAVI) procedures. ESII is a generic, non-TAVI-specific metric, and its performance fades for mortality at follow-up longer than 30 days. We investigated if a TAVI-specific predictive model could achieve improved predictive preinterventional accuracy of 1-year mortality compared with ESII. Patients and methods In this prospective pilot study, 284 participants with severe symptomatic aortic valve stenosis who underwent TAVI were enrolled. Standard clinical metrics (American Society of Anesthesiology (ASA), New York Heart Association and ESII) and patient-reported outcome measures (EuroQol-5 Dimension-Visual Analogue Scale, Kansas City Cardiomyopathy Questionnaire and Clinical Frailty Scale (CFS)) were assessed 1 day before TAVI. Using these data, we tested predictive models (logistic regression and decision tree algorithm (DTA)) with 1-year mortality as the dependent variable. Results Logistic regression yielded the best prediction, with ASA and CFS as the strongest predictors of 1-year mortality. Our logistic regression model score showed significantly better prediction accuracy than ESII (area under the curve=0.659 vs 0.800; p=0.002). By translating our results to a DTA, cut-off score values regarding 1-year mortality risk emerged for low, intermediate and high risk. Treatment costs and length of stay (LoS) significantly increased in high-risk patients. Conclusions and significance A novel TAVI-specific model predicts 1-year mortality, LoS and costs after TAVI using simple, established, transparent and inexpensive metrics before implantation. Based on this preliminary evidence, TAVI team members and patients can make informed decisions based on a few key metrics. Validation of this score in larger patient cohorts is needed. Data are available upon reasonable request.

中文翻译：

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使用机器学习算法对经导管主动脉瓣植入术中的死亡率、住院时间和住院费用进行个性化干预前风险分层：试点试验

简介 一些中心使用基于 Euroscore II (ESII) 的风险分层来协助决策进行经导管主动脉瓣植入 (TAVI) 手术。ESII 是一个通用的、非 TAVI 特定的指标，其性能会随着随访时间超过 30 天的死亡率而下降。我们研究了与 ESII 相比，TAVI 特异性预测模型是否可以提高干预前 1 年死亡率的预测准确性。患者和方法 在这项前瞻性试点研究中，招募了 284 名患有严重主动脉瓣狭窄并接受 TAVI 治疗的参与者。对标准临床指标（美国麻醉学会 (ASA)、纽约心脏协会和 ESII）和患者报告的结果测量（EuroQol-5 维度视觉模拟量表、堪萨斯城心肌病问卷和临床衰弱量表 (CFS)）进行了评估 1 TAVI 前一天。使用这些数据，我们测试了以 1 年死亡率为因变量的预测模型（逻辑回归和决策树算法 (DTA)）。结果 Logistic 回归得出了最佳预测，其中 ASA 和 CFS 是 1 年死亡率的最强预测因子。我们的逻辑回归模型得分显示出比 ESII 显着更好的预测准确性（曲线下面积 = 0.659 与 0.800；p = 0.002）。通过将我们的结果转化为 DTA，得出了低、中和高风险的 1 年死亡风险的截止分数值。高危患者的治疗费用和住院时间（LoS）显着增加。结论和意义 一种新颖的 TAVI 特定模型可在植入前使用简单、既定、透明且廉价的指标来预测 TAVI 后 1 年死亡率、LoS 和成本。根据这些初步证据，TAVI 团队成员和患者可以根据一些关键指标做出明智的决定。需要在更大的患者群体中验证该评分。数据可根据合理要求提供。