Department of Medical Informatics, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands.
Heart Centre, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Location University of Amsterdam, Amsterdam, The Netherlands.
Catheter Cardiovasc Interv. 2022 Nov;100(5):879-889. doi: 10.1002/ccd.30398. Epub 2022 Sep 7.
The currently available mortality prediction models (MPM) have suboptimal performance when predicting early mortality (30-days) following transcatheter aortic valve implantation (TAVI) on various external populations. We developed and validated a new TAVI-MPM based on a large number of predictors with recent data from a national heart registry.
We included all TAVI-patients treated in the Netherlands between 2013 and 2018, from the Netherlands Heart Registration. We used logistic-regression analysis based on the Akaike Information Criterion for variable selection. We multiply imputed missing values, but excluded variables with >30% missing values. For internal validation, we used ten-fold cross-validation. For temporal (prospective) validation, we used the 2018-data set for testing. We assessed discrimination by the c-statistic, predicted probability accuracy by the Brier score, and calibration by calibration graphs, and calibration-intercept and calibration slope. We compared our new model to the updated ACC-TAVI and IRRMA MPMs on our population.
We included 9144 TAVI-patients. The observed early mortality was 4.0%. The final MPM had 10 variables, including: critical-preoperative state, procedure-acuteness, body surface area, serum creatinine, and diabetes-mellitus status. The median c-statistic was 0.69 (interquartile range [IQR] 0.646-0.75). The median Brier score was 0.038 (IQR 0.038-0.040). No signs of miscalibration were observed. The c-statistic's temporal-validation was 0.71 (95% confidence intervals 0.64-0.78). Our model outperformed the updated currently available MPMs ACC-TAVI and IRRMA (p value < 0.05).
The new TAVI-model used additional variables and showed fair discrimination and good calibration. It outperformed the updated currently available TAVI-models on our population. The model's good calibration benefits preprocedural risk-assessment and patient counseling.
目前可用的死亡率预测模型（MPM）在预测经导管主动脉瓣植入术（TAVI）后不同人群的早期死亡率（30 天）方面表现不佳。我们基于来自国家心脏登记的大量最新数据，开发并验证了一种新的 TAVI-MPM。
我们纳入了 2013 年至 2018 年期间在荷兰接受 TAVI 治疗的所有患者，这些患者来自荷兰心脏登记。我们使用基于赤池信息量准则的逻辑回归分析进行变量选择。我们对缺失值进行多重插补，但排除缺失值超过 30%的变量。对于内部验证，我们使用十折交叉验证。对于时间（前瞻性）验证，我们使用 2018 年的数据进行测试。我们通过 C 统计量评估区分度，通过 Brier 评分评估预测概率准确性，通过校准图、校准截距和校准斜率评估校准。我们在人群中比较了我们的新模型与更新的 ACC-TAVI 和 IRRMA MPM。
我们纳入了 9144 例 TAVI 患者。观察到的早期死亡率为 4.0%。最终的 MPM 有 10 个变量，包括：危急术前状态、手术紧迫性、体表面积、血清肌酐和糖尿病状态。中位数 C 统计量为 0.69（四分位间距[IQR]0.646-0.75）。中位数 Brier 评分 0.038（IQR 0.038-0.040）。未观察到校准不当的迹象。时间验证的 C 统计量为 0.71（95%置信区间 0.64-0.78）。我们的模型优于更新的当前可用的 MPMs ACC-TAVI 和 IRRMA（p 值 < 0.05）。
新的 TAVI 模型使用了额外的变量，表现出良好的区分度和良好的校准度。它在我们的人群中优于更新的当前可用的 TAVI 模型。该模型的良好校准有助于术前风险评估和患者咨询。
