Department of Medical Sciences, Uppsala University, Uppsala, Sweden; Department of Medicine, Visby Hospital, Visby, Sweden.
Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
Cardiovasc Revasc Med. 2023 Nov;56:9-15. doi: 10.1016/j.carrev.2023.06.005. Epub 2023 Jun 9.
Early and late readmissions after Transcatheter Aortic Valve Replacement (TAVR) are common and associated with worse outcome. A risk prediction model (TAVR-30) was recently developed using readily available clinical variables to identify patients at risk for hospital readmission within 30 days after TAVR. We performed an independent external validation of the TAVR-30 model.
The Swedish TAVR-registry, linked together with other mandatory national registries was used to identify all TAVR procedures, variables from the original model, hospitalizations and deaths between the years 2008 to 2021.
A total of 8459 patients underwent TAVR, 7693 patients had complete data and were included in the analysis. Out of these, 928 patients experienced a readmission within 30 days. Using the estimates from the original model, a concordance (c)-index of 0.51, a calibration slope of 0.07 and intercept of -0.62 were obtained respectively, overall implying poor model performance.
This independent external validation indicates poor performance of the TAVR-30 model in a Swedish setting. Further research is needed to develop more reliable tools for predicting the risk of early hospital readmission after TAVR, as well as, for providing a deeper understanding of how to develop risk models that performs well in patients with multiple underlying comorbidities.
经导管主动脉瓣置换术(TAVR)后的早期和晚期再入院很常见,且与预后较差相关。最近开发了一种风险预测模型(TAVR-30),该模型使用易于获得的临床变量来识别 TAVR 后 30 天内有住院风险的患者。我们对 TAVR-30 模型进行了独立的外部验证。
使用瑞典的 TAVR 登记处,与其他强制性国家登记处相关联,以确定 2008 年至 2021 年间所有 TAVR 手术、原始模型中的变量、住院和死亡。
共有 8459 例患者接受了 TAVR,其中 7693 例患者数据完整,纳入分析。其中,928 例患者在 30 天内再次入院。使用原始模型的估计值,获得了 0.51 的一致性(c)指数、0.07 的校准斜率和-0.62 的截距,总体上表明模型性能不佳。
这项独立的外部验证表明,TAVR-30 模型在瑞典环境下的表现不佳。需要进一步研究以开发更可靠的工具来预测 TAVR 后早期住院再入院的风险,以及更深入地了解如何开发在患有多种潜在合并症的患者中表现良好的风险模型。